The convergence of various technologies is no longer a luxury but a necessity for business success. If you run an eCommerce store, you're already aware of the importance of Search Engine Optimization (SEO) for visibility, traffic, and ultimately, conversions. But have you ever considered how the Internet of Things (IoT) can further enrich your SEO strategy? As disparate as they may seem, IoT and SEO can intersect in fascinating ways to offer significant advantages for your eCommerce business. Let’s delve into the symbiosis between IoT data and eCommerce SEO.

Why Should eCommerce Care About IoT?

IoT can do wonders for the eCommerce sector by enhancing user experience, streamlining operations, and providing unparalleled data insights. Smart homes, wearables, and voice search devices like Amazon's Alexa or Google Home are becoming standard accessories in households, which means that consumers are using IoT for their online shopping needs more than ever. 

Integrating IoT Insights into UX Design

IoT data isn't just for SEO; it can also transform your site's User Experience (UX) design. By analyzing real-time user interactions captured by IoT devices, you can refine your site layout and navigation for optimal user engagement and conversion. This seamless blend of IoT insights and UX design elevates your eCommerce platform, making it more responsive to user needs and behaviours.

Unlocking User Behavior Insights

One of the most direct ways IoT can impact your SEO strategy is through enhanced data analytics. Devices like smartwatches or fitness trackers could provide valuable information on consumer habits, routines, and preferences. By integrating this IoT data into your SEO strategy, you can better understand your target audience, refine your keyword focus, and tailor your content to better suit the needs and search intent of potential customers.

Voice Search Optimization

Voice-activated devices are increasingly being used to perform searches and online shopping. As voice search is typically more conversational and question-based, you can use IoT data to understand the common phrases or questions consumers ask these devices. This can help you optimize your product descriptions, FAQs, and even blog posts to align with the natural language used in voice searches.

Local SEO and IoT

The "near me" search query is incredibly popular, thanks in part to IoT devices with geolocation capabilities. People use their smartphones or smartwatches to find the nearest restaurant, gas station, or store. If you have a brick-and-mortar store in addition to your online shop, IoT data can help you target local SEO more effectively by integrating local keywords and ensuring your Google My Business listing is up-to-date.

IoT and Page Experience

With IoT, user experience can go beyond the digital interface to incorporate real-world interactions. For example, a smart fridge could remind users to order more milk, directing them to your online grocery store. If your website isn’t optimized for speed and experience, you could lose these high-intent users. Incorporating IoT insights into your SEO strategy can help you anticipate these needs and optimize your site accordingly.

Real-Time Personalization

IoT devices can collect data in real-time, offering insights into user behaviour that can be immediately acted upon. Imagine someone just completed a workout on their smart treadmill. They might then search for protein shakes or workout gear. With real-time data, you could offer timely discounts or suggestions, personalized to the user's immediate needs, all while improving your SEO through higher user engagement and lower bounce rates.

Wrapping It Up

IoT and SEO may seem like different arenas, but they are more interconnected than you'd think. By adopting a holistic approach that marries the insights from IoT devices with your SEO strategy, you can significantly improve your eCommerce site's performance. From optimizing for voice search and improving local SEO to real-time personalization and superior user experience, the opportunities are endless.

The Internet of Things (IoT) continues to revolutionize industries, and Microsoft Azure IoT is at the forefront of this transformation. With its robust suite of services and features, Azure IoT enables organizations to connect, monitor, and manage their IoT devices and data effectively. In this blog post, we will explore the latest trends and use cases of Azure IoT in 2023, showcasing how it empowers businesses across various sectors.

Edge Computing and AI at the Edge:

As the volume of IoT devices and the need for real-time analytics increases, edge computing has gained significant momentum. Azure IoT enables edge computing by seamlessly extending its capabilities to the edge devices. In 2023, we can expect Azure IoT to further enhance its edge computing offerings, allowing organizations to process and analyze data closer to the source. With AI at the edge, businesses can leverage machine learning algorithms to gain valuable insights and take immediate actions based on real-time data.

Edge Computing and Real-time Analytics:

As IoT deployments scale, the demand for real-time data processing and analytics at the edge has grown. Azure IoT Edge allows organizations to deploy and run cloud workloads directly on IoT devices, enabling quick data analysis and insights at the edge of the network. With edge computing, businesses can reduce latency, enhance security, and make faster, data-driven decisions.

Industrial IoT (IIoT) for Smart Manufacturing:

Azure IoT is poised to play a crucial role in the digital transformation of manufacturing processes. IIoT solutions built on Azure enable manufacturers to connect their machines, collect data, and optimize operations. In 2023, we anticipate Azure IoT to continue empowering smart manufacturing by offering advanced analytics, predictive maintenance, and intelligent supply chain management. By harnessing the power of Azure IoT, manufacturers can reduce downtime, enhance productivity, and achieve greater operational efficiency.

Connected Healthcare:

In the healthcare industry, Azure IoT is revolutionizing patient care and operational efficiency. In 2023, we expect Azure IoT to drive the connected healthcare ecosystem further. IoT-enabled medical devices, remote patient monitoring systems, and real-time data analytics can help healthcare providers deliver personalized care, improve patient outcomes, and optimize resource allocation. Azure IoT's robust security and compliance features ensure that sensitive patient data remains protected throughout the healthcare continuum.

Smart Cities and Sustainable Infrastructure:

As cities strive to become more sustainable and efficient, Azure IoT offers a powerful platform for smart city initiatives. In 2023, Azure IoT is likely to facilitate the deployment of smart sensors, intelligent transportation systems, and efficient energy management solutions. By leveraging Azure IoT, cities can enhance traffic management, reduce carbon emissions, and improve the overall quality of life for their residents.

Retail and Customer Experience:

Azure IoT is transforming the retail landscape by enabling personalized customer experiences, inventory optimization, and real-time supply chain visibility. In 2023, we can expect Azure IoT to continue enhancing the retail industry with innovations such as cashier-less stores, smart shelves, and automated inventory management. By leveraging Azure IoT's capabilities, retailers can gain valuable insights into customer behavior, streamline operations, and deliver superior shopping experiences.

AI and Machine Learning Integration:

Azure IoT integrates seamlessly with Microsoft's powerful artificial intelligence (AI) and machine learning (ML) capabilities. By leveraging Azure IoT and Azure AI services, organizations can gain actionable insights from their IoT data. For example, predictive maintenance algorithms can analyze sensor data to detect equipment failures before they occur, minimizing downtime and optimizing operational efficiency.

Enhanced Security and Device Management:

In an increasingly interconnected world, security is a top priority for IoT deployments. Azure IoT provides robust security features to protect devices, data, and communications. With features like Azure Sphere, organizations can build secure and trustworthy IoT devices, while Azure IoT Hub ensures secure and reliable device-to-cloud and cloud-to-device communication. Additionally, Azure IoT Central simplifies device management, enabling organizations to monitor and manage their IoT devices at scale.

Industry-specific Solutions:

Azure IoT offers industry-specific solutions tailored to the unique needs of various sectors. Whether it's manufacturing, healthcare, retail, or transportation, Azure IoT provides pre-built solutions and accelerators to jumpstart IoT deployments. For example, in manufacturing, Azure IoT helps optimize production processes, monitor equipment performance, and enable predictive maintenance. In healthcare, it enables remote patient monitoring, asset tracking, and patient safety solutions.

Integration with Azure Services:

Azure IoT seamlessly integrates with a wide range of Azure services, creating a comprehensive ecosystem for IoT deployments. Organizations can leverage services like Azure Functions for serverless computing, Azure Stream Analytics for real-time data processing, Azure Cosmos DB for scalable and globally distributed databases, and Azure Logic Apps for workflow automation. This integration enables organizations to build end-to-end IoT solutions with ease.

Conclusion:

In 2023, Azure IoT is set to drive innovation across various sectors, including manufacturing, healthcare, cities, and retail. With its robust suite of services, edge computing capabilities, and AI integration, Azure IoT empowers organizations to harness the full potential of IoT and achieve digital transformation. As businesses embrace the latest trends and leverage the diverse use cases of Azure IoT, they can gain a competitive edge, improve operational efficiency, and unlock new opportunities in the connected world.

About Infysion

We work closely with our clients to help them successfully build and execute their most critical strategies. We work behind-the-scenes with machine manufacturers and industrial SaaS providers, to help them build intelligent solutions around Condition based machine monitoring, analytics-driven Asset management, accurate Failure predictions and end-to-end operations visibility. Since our founding 3 years ago, Infysion has successfully productionised over 20+ industry implementations, that support Energy production, Water & electricity supply monitoring, Wind & Solar farms management, assets monitoring and Healthcare equipment monitoring.

We strive to provide our clients with exceptional software and services that will create a meaningful impact on their bottom line.

 Visit our website to learn more about success stories, how we work, Latest Blogs and different services we do offer!

Cloud-based motor monitoring as a service is revolutionizing the way industries manage and maintain their critical assets. By leveraging the power of the cloud, organizations can remotely monitor motors, analyze performance data, and predict potential failures. However, as this technology continues to evolve, several challenges emerge that need to be addressed for successful implementation and operation. In this blog post, we will explore the top challenges faced in cloud-based motor monitoring as a service in 2023. 

Data Security and Privacy:

One of the primary concerns in cloud-based motor monitoring is ensuring the security and privacy of sensitive data. As motor data is transmitted and stored in the cloud, there is a need for robust encryption, authentication, and access control mechanisms. In 2023, organizations will face the challenge of implementing comprehensive data security measures to protect against unauthorized access, data breaches, and potential cyber threats. Compliance with data privacy regulations, such as GDPR or CCPA, adds an additional layer of complexity to this challenge.

Connectivity and Network Reliability:

For effective motor monitoring, a reliable and secure network connection is crucial. In remote or industrial environments, ensuring continuous connectivity can be challenging. Factors such as signal strength, network coverage, and bandwidth limitations need to be addressed to enable real-time data transmission and analysis. Organizations in 2023 will need to deploy robust networking infrastructure, explore alternative connectivity options like satellite or cellular networks, and implement redundancy measures to mitigate the risk of network disruptions.

Scalability and Data Management:

Cloud-based motor monitoring generates vast amounts of data that need to be efficiently processed, stored, and analyzed. In 2023, as the number of monitored motors increases, organizations will face challenges in scaling their data management infrastructure. They will need to ensure that their cloud-based systems can handle the growing volume of data, implement efficient data storage and retrieval mechanisms, and utilize advanced analytics and machine learning techniques to extract meaningful insights from the data.

Integration with Existing Systems:

Integrating cloud-based motor monitoring systems with existing infrastructure and software can pose significant challenges. In 2023, organizations will need to ensure seamless integration with their existing enterprise resource planning (ERP), maintenance management, and asset management systems. This includes establishing data pipelines, defining standardized protocols, and implementing interoperability between different systems. Compatibility with various motor types, brands, and communication protocols also adds complexity to the integration process.

Cost and Return on Investment:

While cloud-based motor monitoring offers numerous benefits, organizations must carefully evaluate the cost implications and expected return on investment (ROI). Implementing and maintaining the necessary hardware, software, and cloud infrastructure can incur significant expenses. Organizations in 2023 will face the challenge of assessing the financial viability of cloud-based motor monitoring, considering factors such as deployment costs, ongoing operational expenses, and the potential savings achieved through improved motor performance, reduced downtime, and optimized maintenance schedules.

Connectivity and Reliability:

Cloud-based motor monitoring relies heavily on stable and reliable internet connectivity. However, in certain remote locations or industrial settings, maintaining a consistent connection can be challenging. The availability of high-speed internet, network outages, or intermittent connections may impact real-time monitoring and timely data transmission. Service providers will need to address connectivity issues to ensure uninterrupted monitoring and minimize potential disruptions.

Scalability and Performance:

As the number of monitored motors increases, scalability and performance become critical challenges. Service providers must design their cloud infrastructure to handle the growing volume of data generated by motor sensors. Ensuring real-time data processing, analytics, and insights at scale will be vital to meet the demands of large-scale motor monitoring deployments. Continuous optimization and proactive capacity planning will be necessary to maintain optimal performance levels.

Integration with Legacy Systems:

Integrating cloud-based motor monitoring with existing legacy systems can be a complex undertaking. Many organizations have legacy equipment or infrastructure that may not be inherently compatible with cloud-based solutions. The challenge lies in seamlessly integrating these disparate systems to enable data exchange and unified monitoring. Service providers need to offer flexible integration options, standardized protocols, and compatibility with a wide range of motor types and manufacturers.

Data Analytics and Actionable Insights:

Collecting data from motor sensors is only the first step. The real value lies in extracting actionable insights from this data to enable predictive maintenance, identify performance trends, and optimize motor operations. Service providers must develop advanced analytics capabilities that can process large volumes of motor data and provide meaningful insights in a user-friendly format. The challenge is to offer intuitive dashboards, anomaly detection, and predictive analytics that empower users to make data-driven decisions effectively.

Conclusion:

Cloud-based motor monitoring as a service offers tremendous potential for organizations seeking to optimize motor performance and maintenance. However, in 2023, several challenges need to be addressed to ensure its successful implementation. From data security and connectivity issues to scalability, integration, and advanced analytics, service providers must actively tackle these challenges to unlock the full benefits of cloud-based motor monitoring. By doing so, organizations can enhance operational efficiency, extend motor lifespan, and reduce costly downtime in the ever-evolving landscape of motor-driven industries.

Connected devices in the medical field bring a multitude of benefits, including improved patient care, enhanced diagnostics, and streamlined healthcare processes. However, the complexity associated with these devices is a significant consideration. Here, we explore the intricacies involved in the realm of connected medical devices.

First and foremost, interoperability is a critical challenge. Medical environments comprise various devices from different manufacturers, each with its own communication protocols and data formats. Ensuring seamless connectivity and data exchange between these devices necessitates standardized interfaces and robust interoperability frameworks.

Data security and privacy are paramount in the medical domain. Connected devices generate and transmit sensitive patient data, including personal health information and vital signs. Safeguarding this information from unauthorized access, data breaches, and cyber threats requires robust encryption, authentication mechanisms, and strict adherence to regulatory standards like the Health Insurance Portability and Accountability Act (HIPAA)

The complexity also arises from the diverse range of connected devices used in healthcare. From wearable sensors to implantable devices, infusion pumps to remote monitoring systems, each device has specific requirements, connectivity options, and integration challenges. Managing this ecosystem of devices, ensuring seamless communication, and maintaining their functionality demand specialized expertise and effective device management solutions.

Furthermore, regulatory compliance adds another layer of complexity. Connected medical devices must meet rigorous standards to ensure safety, accuracy, and reliability. Regulatory bodies, such as the U.S. Food and Drug Administration (FDA), closely scrutinize these devices for adherence to quality standards, clinical validation, and risk mitigation measures.

Additionally, healthcare organizations need to navigate the complexity of data analytics and actionable insights. Connected devices generate vast amounts of data that must be processed, analyzed, and transformed into meaningful information for healthcare professionals. Extracting valuable insights from this data necessitates advanced analytics algorithms, machine learning techniques, and data visualization tools.

Overcoming the challenges requires collaboration among manufacturers, healthcare providers, and regulatory bodies to develop robust standards, innovative solutions, and best practices that ensure safe, secure, and effective utilization of connected devices to revolutionize patient care.

We all know how IoT has revolutionized the way we interact with the world. IoT devices are now ubiquitous, from smart homes to industrial applications. A significant portion of these devices are Wireless Sensor Networks (WSNs), which are a key component of IoT systems. However, designing and implementing WSNs presents several challenges for embedded engineers. In this article, we discuss some of the significant challenges that embedded engineers face when working with WSNs.

WSNs are a network of small, low-cost, low-power, and wirelessly connected sensor nodes that can sense, process, and transmit data. These networks can be used in a wide range of applications such as environmental monitoring, healthcare, industrial automation, and smart cities. WSNs are typically composed of a large number of nodes, which communicate with each other to gather and exchange data. The nodes are equipped with sensors, microprocessors, transceivers, and power sources. The nodes can also be stationary or mobile, depending on the application.

One of the significant challenges of designing WSNs is the limited resources of the nodes. WSNs are designed to be low-cost, low-power, and small, which means that the nodes have limited processing power, memory, and energy. This constraint limits the functionality and performance of the nodes. Embedded engineers must design WSNs that can operate efficiently with limited resources. The nodes should be able to perform their tasks while consuming minimal power to maximize their lifetime.

Another challenge of WSNs is the limited communication range. The nodes communicate with each other using wireless radio signals. However, the range of the radio signals is limited, especially in indoor environments where the signals are attenuated by walls and other obstacles. The communication range also depends on the transmission power of the nodes, which is limited to conserve energy. Therefore, embedded engineers must design WSNs that can operate reliably in environments with limited communication range.

WSNs also present a significant challenge for embedded engineers in terms of data management. WSNs generate large volumes of data that need to be collected, processed, and stored. However, the nodes have limited storage capacity, and transferring data to a centralized location may not be practical due to the limited communication range. Therefore, embedded engineers must design WSNs that can perform distributed data processing and storage. The nodes should be able to process and store data locally and transmit only the relevant information to a centralized location.

Security is another significant challenge for WSNs. The nodes in WSNs are typically deployed in open and unprotected environments, making them vulnerable to physical and cyber-attacks. The nodes may also contain sensitive data, making them an attractive target for attackers. Embedded engineers must design WSNs with robust security features that can protect the nodes and the data they contain from unauthorized access.

The deployment and maintenance of WSNs present challenges for embedded engineers. WSNs are often deployed in harsh and remote environments, making it difficult to access and maintain the nodes. The nodes may also need to be replaced periodically due to the limited lifetime of the power sources. Therefore, embedded engineers must design WSNs that are easy to deploy, maintain, and replace. The nodes should be designed for easy installation and removal, and the network should be self-healing to recover from node failures automatically.

Final thought; WSNs present significant challenges for embedded engineers, including limited resources, communication range, data management, security, and deployment and maintenance. Addressing these challenges requires innovative design approaches that can maximize the performance and efficiency of WSNs while minimizing their cost and complexity. Embedded engineers must design WSNs that can operate efficiently with limited resources, perform distributed data processing and storage, provide robust security features, and be easy to deploy

In recent days, neural networks have become a topic for discussion. But the question still needs to be solved- How can it affect our world today and tomorrow?

The global neural network market's compound annual growth rate (CAGR) is expected to be 26.7% from 2021 to 2030. This means that new areas of application for them might appear soon. The Internet of Things that is IoT, is today's most fascinating and required technological solution for business. Around 61% of companies utilize IoT platforms, and we can anticipate the integration of neural networks into enterprise IoT solutions. This anticipation raises many questions, like what gets such collaboration and how to prepare it. Can we optimize the IoT ecosystem using neural networks, and who will approach such solutions?

What do you understand by a neural network, and how is it beneficial for enterprise IoT?

An artificial neural network that is ANN is a network of artificial neurons striving to simulate the analytical mechanisms taken by the human brain. This type of artificial intelligence includes a range of algorithms that can "learn" from their own experience and improve themselves, which is very different from classical algorithms that are programmed to resolve only specific tasks. Thus, with time, the neural network will remain pertinent and keep on improving.

With the proper implementation, enterprise internet of things (EIoT) and ANN can offer the business the most valuable things: precise analytics and forecasts. In general, it is not possible to compare both. Enterprise IoT is a system that needs software for data analysis, whereas ANN is a component that needs a large amount of data to be operational. Their team naturally controls the analytical tasks; therefore, high-level business tasks are performed most effectively, reducing costs, automating processes, finding new revenue sources, etc.

In the Internet of Things ecosystem, neural networks help in two areas above all:

• Data acquisition via ANN-based machine vision
• Advanced-data analysis

If it needs significant investments to execute ANN in big data analytics solutions, neural network image processing can decrease the cost of the IoT solution. Thus, neural networks improve enterprise IoT solutions, enhance their value, and speed up global adoption.

Which solutions within enterprise IoT can be enhanced using neural networks?

IoT-based visual control

The IoT ecosystem begins with data collection. Data quality impacts the accuracy of the ultimate prediction. If you implement visual control in your production processes, neural networks can boost the quality of products by superseding outdated algorithms. Besides this, they will optimize the EIoT solution. Conventional machine vision systems are pricey as they require the highest resolution cameras to catch minor defects in a product. They come with complex specific software that fails to respond to immediate changes.

Neural networks within machine vision systems can:

• Diminish camera requirements
• Self-learn on your data
• Automate high-speed operations

Indeed, industrial cameras use large-format global shutter sensors having high sensitivity and resolution to develop the highest quality images. Nevertheless, a well-trained ANN starts to identify images with time. It allows them to reduce the technical needs for the camera and ultimately cuts the final cost of the enterprise IoT implementation. You cannot compromise the quality of images to detect small components like parts in circuit boards; however, it is manageable for printing production, completeness checking, or food packaging.

After training, neural networks use massive amounts of data to identify objects from the images. It enables you to customize the EIoT solution and train the ANN to operate specifically with your product by processing your images.

For example, convolutional neural networks are utilized actively in the healthcare industry to detect X-rays and CT scans. The outcome offered by such custom systems is more precise than conventional ones. The capability to process information at high speeds permits the automation of production processes. When the problem or defect is caught, neural networks promptly report it to the operator or launch an intelligent reaction, like automating sorting. Hence, it allows real-time detection and rejection of defective production.

An exclusive example of how ANN is utilized for edge and fog computing. As per PSA, a neural network executed in a machine vision system permits lowering the number of defects by 90% in half a year, whereas production costs are decreased by 30%. Prospective areas for ANN in IoT visual control are quality assurance, sorting, production, collecting, marking, traffic control, and ADAS.

Big data advanced analytics for enterprise IoT:

Today, neural networks allow businesses to grab advantages like predictive maintenance, new revenue flows, asset management, etc. It is possible via deep neural networks (DNN) and the deep Learning (DL) method involving multiple layers for data processing. They detect hidden data trends and valuable information from a significant dataset by employing classification, clustering, and regression. It results in effective business solutions and the facilitation of business applications.

In comparison to traditional models, DL manages with the attributes that are expected for IoT data:

1. Assess the time of taking measurements
2. Resist the high noise of the enterprise IoT data
3. Conduct accurate real-time analysis
4. Determine heterogeneous and discordant data
5. Process a large amount of data

In practice, this implies that you don't require middle solutions to deliver and sort the data in the cloud or to analyze them in real-time. For example, full-cycle metallurgical enterprises can execute one solution to analyze the variable and unstructured data from metal mining, smelting, and final manufacturing products. Airplanes generate about 800TB of data per hour, making it impossible to process it all ideally using conventional analytical systems.

Today, DNN models are successful in the following enterprise IoT applications. 

Healthcare:

Today, it has become easy to predict disease using AI-based IoT systems, and this technology is developing for further improvements. For instance, the latest invention based on the neural network can detect the risk of heart attacks by up to 94.8%. DNN is also helpful in disease detection: the spectrogram of a person's voice received using IoT devices can identify voice pathologies after DNN processing. In general, ANN-based IoT health monitoring systems' accuracy is estimated to be above 85%.

Power consumption:

DL systems in the enterprise Internet of Things have provided results in power demand prediction based on power price forecasting, consumption data, anomaly, power theft detection, and leak detection. Smart meter data analysis permits you to calculate consumption, determine the unusual usage of electricity, and predict with an accuracy of more than 95%, which will help you to adjust energy consumption.

Manufacturing:

Neural networks help to use the most demanded IoT service among manufacturers properly- predictive equipment maintenance. It was ascertained to be a workable practice for mechanical and electrical systems. This network provides accurate real-time status monitoring and predicts proper life rest. Another best example is the recognition of employee activity by taking readings and following in-depth analysis.

Transportation & Logistics:

Deep Learning has made smart transportation systems possible. It offers better traffic congestion management by processing travel time, speed, weather, and occupational parking forecasting. Analytical reports based on vehicle data help to discover dangerous driving and possible issues before the failure happens.

As we know, the previous industries generate heterogeneous data. Therefore, the potential of ANN analytics within EIoT will be unlocked for multiple complicated systems.

When to consider ANN for enterprise IoT:

Till now, research in the field of ANNs been very active, and we cannot foretell all the advantages or pitfalls these solutions will convey. No doubt, neural networks find out correlations, models, and trends better than other algorithms. The IoT ecosystem's data will become more extensive, complex, and diverse with time. So, the development of neural networks is the future of IoT.

For now, we can look into the following features of neural networks for enterprise IoT:

• They suit the IoT ecosystem architecture, substituting alternative solutions with significant advantages.
• Essential for industrial image processing.
• Progressive ANN-based data analytics gets the high-level business value of the enterprise IoT solutions – improves productivity, and exactness, boosts sales, and produces informed business decisions.
• Training the ANN requires time and expenditure but will become fully customizable.
• We cannot conclude it is an affordable solution, but the advantages are priceless if the IoT ecosystem is executed accurately.

Therefore, if you are provided with a neural network as one of the opportunities for executing your idea within the IoT ecosystem, give it a chance. You never know, this solution will become a must-have in the coming years.

The wireless module is a modular product of digital data transmission radio. It refers to a high-performance professional data transmission radio module realized with the help of single-chip microcomputer technology and radio technology. The wireless transceiver module is mainly used to control wireless data transmission and reception through a single-chip microcomputer. Generally, it is FSK, GFSK modulation mode. If it is divided by operating frequency, there are 170MHz, 230MHz, 315MHz, 433MHz, 868MHz, 915MHz, 2.4GHz and so on.

The Internet of Things is an important part of the new generation of information technology and an important stage of development in the "informatization" era. The Internet of Things is a network that extends and expands its user end to any item and item for information communication and exchange to achieve intelligence.

With the increasing number of IoT product forms, on the one hand, module prices will continue to fall; on the other hand, IoT modules will continue to develop in a diversified direction to meet the functional requirements of more and more different types of equipment. In the next few years, wireless communication modules for the Internet of Things will develop in the direction of more integration and miniaturization, and more and more chips and functions will be integrated into the IoT module.

From the current development situation, the typical applications of wireless modules in the field of Internet of Things are as follows:

Industrial applications

Industry is an important area for wireless module applications in the Internet of Things. Various types of terminal sensors with environmental awareness capabilities communicate data collected through wireless modules to achieve intelligent monitoring and intelligent control in the production process. This greatly improves production efficiency, improves product quality, reduces production costs, and reduces traditional industrial Ascension to a new stage of smart industry.

For example, parts processing enterprises use various sensors and wireless modules to achieve real-time monitoring of the width, thickness, and temperature of processed parts during the production process, thereby improving production efficiency and optimizing production processes.

Agricultural applications

The application of the wireless module in the agricultural field is to collect the temperature, humidity signals and environmental parameters such as light, soil temperature, leaf surface humidity, etc. in the greenhouse in real time, and realize the automatic stop or start of specified equipment through the Internet of Things technology. It can be processed at any time according to user needs, providing a scientific basis for automatic monitoring of agricultural ecological information, automatic control of the environment, and intelligent management. The temperature and humidity signals are collected through sensors, and the data is transmitted through the wireless module to achieve remote control of the temperature and humidity in the greenhouse, and record the scene conditions to ensure the temperature and humidity balance in the greenhouse.

Smart home applications

Smart home is the automation of various home devices (such as audio and video equipment, network appliances, curtain control, air conditioning control, lighting systems, security systems, etc.) through smart home network networking. Through wireless networks, remote control of home devices can be realized. . Smart home is an important area for wireless module applications. Compared with ordinary homes, smart homes not only provide comfortable, pleasant and high-quality home living spaces, and realize more intelligent home security systems; they also transform the home environment from the original passive still structure into a tool with active wisdom, providing a full range of tools Information interaction function makes you feel more comfortable and worry-free.

Smart medical applications

The intelligent medical system uses simple and practical home medical sensing equipment to monitor the physiological indicators of the patients or the elderly in the home in real time, and transmits the generated physiological indicator data to the caregiver or the relevant medical unit through the wireless module. According to customer needs, relevant value-added services have now begun to be provided, such as emergency call assistance services and expert consulting services.

Smart city security application

Smart city security system is a unified monitoring of the safety of the city. The electronic equipment equipped with wireless modules is used to network decentralized and independent image collection points to remotely monitor, transmit, store, and manage in real time, to achieve unified monitoring, unified storage and unified management of city security, and to provide city management and builders with A new, intuitive, and extended audiovisual management tool.

Environmental monitoring applications

The environmental monitoring system monitors the water quality of the earth's surface water in real time, thereby realizing timely grasp of the water quality status of the main sections of the main river basin, early warning and forecast of major or river basin water pollution accidents, resolving water pollution accident disputes across administrative regions, and monitoring the total Implementation of control system. The West Lake Environmental Monitoring Project provides water quality data of West Lake to environmental protection departments through various monitoring wireless modules installed in the West Lake area, monitors the water quality of West Lake waters in real time, and reports the data of monitoring points to relevant management departments via the Internet. .

Intelligent transportation applications

Intelligent transportation system is the real-time monitoring and management of the car's position and speed, image information inside and outside the car, and other vehicle parameters, effectively meeting the various needs of vehicle owners for vehicle management. The wireless video surveillance system for the bus industry uses the wireless video surveillance and GPS positioning functions of on-board equipment to monitor the running status of the bus in real time. The intelligent bus station interacts with the data of the electronic station board through the media distribution center, and realizes the functions of public transport dispatch information data and multimedia data. It can also use the electronic station board to realize the function of advertising.

The Internet of Things network of interconnected devices such as sensors, gateways, and computers that transfer the data over a wireless network eliminates the need for human intervention. IoT devices can be remotely tracked, and controlled in real-time. It also enables users to connect and interact with others over the internet. The applications of IoT are tremendous and it can be found in every part of human life, ranging from smartwatches to self-driving cars.

What Is IoT Device Management?

IoT device management refers to the ability to remotely accessing, monitoring, tracking and managing the functionality of IoT devices in order to ensure the deployed devices are secure, up to date and compliant. Here’s a breakdown of the reasons why businesses need an IoT device management platform:

• Accelerate time to market

One of the biggest perks of the IoT device management platform is that it helps developers to reduce the time frame of product development and testing, thereby enabling them to release products to market on time. Furthermore, streamlining and automating network and device management tasks enables businesses to concentrate on their core competencies while lowering costs.

• Secure device on and off boarding

A smart device is not, and should not be, automatically connected to an IoT network. A secure approach is required to configure and add only authorized devices to the network architecture, and a network and device management tool makes this simple and straightforward. End nodes can be authenticated and secure communications established via a web interface by registering and attaching them to the authorized base station using their network keys and identification credentials. Only after the node has completed the onboarding process it will be allowed to join the network and securely transmit data using network-level encryption. Similarly, if deployed nodes are no longer required, they can be easily offboarded from the web UI – without having to travel to the field.

• Streamline network monitoring and troubleshooting

IoT device management platform enables firms to gain a top to bottom view of all registered nodes, network traffic and their status in a single interface. It acts as a central hub for data aggregation across base stations in a network with multiple base stations. This is especially useful for monitoring and identifying unexpected network and device problems. Businesses can quickly identify and determine the root causes of bottlenecks with real-time visibility into incoming data, battery level, and keep-alive messages from individual nodes. For example, if a node fails to deliver messages on a regular basis, the radio traffic may be overloaded. If, on the other hand, it completely disconnects from the network and stops sending messages, it could be due to a hardware or firmware flaw. Similarly, by continuously monitoring battery levels, firms can schedule maintenance for multiple devices at the same time, saving time and money.

• Simplifies downstream application deployment

IoT device management platform serves as a link between the edge network, downstream data servers of users and enterprise applications. Using protocols like MQTT and API calls, a versatile IoT device management solution allows for easy integration with any backend system, whether on-premises or in the cloud. As a result, firms can easily deploy and scale IoT applications to meet changing business needs, whether by adding new devices to an existing app or connecting to a new analytics platform. It also enables businesses to gain a better view of all current integrations and applications from a single window thereby simplifying the management of an entire IoT project.

• Eliminate security risks

Considering the ever-increasing complexity of cyber-attacks, it is critical to equip connected IoT network components such as base stations and routers with the most up-to-date security features. A manual approach cannot keep up with the demand for continuous and timely updates to these critical network infrastructures, particularly those deployed remotely - Here comes the need for an IoT device management tool. It will enable businesses to update operating systems automatically and run security updates from afar, helping them to save money while also ensuring their remote base stations are well-prepared against malicious attacks. Furthermore, round-the-clock monitoring of the network assists firms in the early detection of unusual patterns, such as an increase in data traffic, which could indicate a breach and eliminate security hazards.

Conclusion

The advancements in IoT and AI services have made it critical for businesses to rely on efficient and secure methods to manage and control their networks and devices at scale. An IoT device management platform helps businesses to track, manage and gain real-time insights into all devices and stay on top of their deployment. When combined with a robust wireless solution, it enables firms to seamlessly expand their IoT network and solutions at minimal cost and complexity.

The past several years have seen a huge growth in the number of companies offering IoT Platforms. The market research firm IoT Analytics reported 613 companies offering IoT platforms in 2021! This is a mind-blowing number. The IoT platforms vary widely in capabilities but typically focus on one or more of the building blocks of IoT systems – physical devices, internet connectivity, and digital services. In one way or another, they provide software (or in some cases hardware too) that gives companies a head-start when building IoT systems. There are so many companies offering platforms that it is nearly impossible to keep up with all of them.

 Charting the right path, avoiding pitfalls, maximizing your success.

If you are getting into IoT and not familiar with IoT platforms, you might be asking yourself questions like –  What makes up an IoT platform? What advantages could they have for my company? How do I select an IoT platform? 

Let’s tackle these questions one by one. 

What makes up an IoT platform?
 

Features

True IoT platforms typically provide the following features:

• Digital services running in the cloud that physical devices connect to
• Software that runs on devices that communicates with the digital services
• A framework or schema for data messaging and remote command & control of devices
• Security infrastructure to handle device registration, authentication, security credential management
• Tools and methods for updating device firmware over-the-air (OTA)
• Web dashboards for viewing the state of devices and interacting with the system

IoT  platforms may or may not also provide other features, including:

• Analytics tools and dashboards
• Digital twins or shadows
• Application deployment orchestration
• Machine learning orchestration
• Rules engines
• Fleet management tools
• Integrations to other services
• Gateway or hub support for bridging devices to the cloud
• Cellular network plans for devices
• Web or mobile application interfaces and templates

Example Elements of an IoT Platform

Types of IoT Platforms

IoT platforms are not all the same. Their features and target use-cases vary a lot. However, at a high level, they can be grouped into two main categories.

Platform as a Service (PaaS) – Offered by the big cloud service providers

PaaS platforms provide building blocks to do most things an IoT system needs, but it is up to you to write the custom code that connects it all together. With a PaaS provider, you don’t have to worry about underlying server hardware, but you have to compose their services into a working architecture and manage the deployment of applications that use their services. This is more work but allows more flexibility and the opportunity to customize the system to your needs. Ongoing costs of a PaaS IoT platform are typically lower than a SaaS, but expertise is required to ensure correct usage patterns to avoid larger costs. The big cloud providers all offer PaaS IoT platforms. This includes Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

Software as a Service (SaaS) – Offered by numerous software vendors, large and small

With a SaaS provider, you get access to use the software application they deploy and manage for you. Or you can license it and deploy it yourself. SaaS platforms typically provide some configurability and integrations with other systems. There is much less work on the cloud side as this is mostly taken care of for you. However, you are limited to the features that the IoT platform provider offers. You may need to invest more in bridging the platform to your other systems. Depending on your use case, a SaaS may provide more advanced features out-of-the-box than a PaaS. Ongoing costs are likely to be higher with SaaS IoT platforms. Examples of SaaS IoT platform providers include Pelion, Losant, Friendly Technologies, Software AG, Blynk, Particle, ThingsBoard, and Golioth.

What advantages could they have for my company?

Benefits of IoT Platforms – There are a lot.

The goal of IoT platforms is to provide a foundation for product-makers to build IoT solutions on top of.  IoT platforms take care of all the fundamental features that all solutions need (e.g. “the plumbing”), so you can focus on adding value with the differentiating features that you add on top. Users of IoT platforms get a huge benefit from economies of scale – especially if using the most popular platforms. This translates into improved security, more robust services, and lower costs. For these reasons, we always recommend using an IoT platform.

How do I select an IoT platform?

The Big Question – Should you use a PaaS or SaaS?

At SpinDance, we believe in lean and agile business principles. This usually translates into taking a staged approach and focusing on different priorities in each stage. IoT is a journey, not a destination. We have seen the most success when companies tackle each of their challenges in stages, don’t try to do too much too quickly, and don’t lock themselves into long-term decisions too early. Choosing whether to use a PaaS or Saas depends on the stage you are in along your IoT journey.

Our Answer – It depends on your stage in your IoT journey. 

If you are just starting on your IoT journey…

In the disconnected stage your main goals are to learn what technology can do for you, develop a vision for your new product or service with that knowledge, and evaluate your vision based on customer input. At this stage, you shouldn’t be worried too much about scale or efficiency. You need to nail down the problem you want to solve and the solution you propose to solve it with. Ash Maurya, entrepreneur and author of Running Lean, says that “Building a successful product is fundamentally about risk mitigation.” To evaluate and reduce your risk, you need to test your assumptions.

We often recommend building Proof of Concepts and Prototypes in this stage. These experiments are crucial to help you quickly validate the feasibility, desirability, and viability of your plans. They also help rally your organization and potential customers around new possibilities.

SaaS IoT platforms have their most advantage in this stage. They can help you get devices connected and data flowing quickly because they typically have more features ready out-of-the-box. However, since your knowledge about the future is limited at this stage, we recommend you avoid long-term commitments so you don’t get stuck with a solution that doesn’t work for you down the road.

 If you are working on your first connected product…

In the connecting stage, you should have some confidence in your problem-market fit and you should have a better idea of what benefits IoT can bring to your business. Now you need to build a system to deal with the rigors of production. You also need to adapt your organization to support your new product or service.

We recommend shifting your focus to creating robust experiences for your customers spanning across the physical devices and digital interfaces they interact with. You need to consider the other parts of the system such as mobile applications, web applications, database storage, operations dashboards, etc that you’ll need for your customers and your internal teams to interact with the system.

PaaS IoT platforms start to have a strong advantage in this stage. More often than not, we see the needs of the company outstretch the features provided by a SaaS. Therefore, there is a need to augment the capabilities of the SaaS platform or bridge it to your other systems. For example, if a SaaS IoT platform does not provide long-term data storage, you will need to create a bridge that pulls data from the platform’s service and puts it into a database that you control in the cloud. Maintaining and monitoring this bridge is non-trivial which may lead to you wanting to consolidate everything into your existing cloud. For reasons like this, we typically recommend PaaS platforms at this stage.

If you already have connected products out in the market…

The connected or accelerating stages are all about maximizing the benefit of IoT, taking advantage of the valuable data you are likely getting, and aligning your costs to revenue. You should be focused on scaling up your system while you improve your connected customer relationships and build up new processes and skills. These are not insignificant tasks. It takes in-house expertise. Your team needs to understand your systems, be able to improve efficiencies and optimize costs. You’ve got to get data to the right place when you need it, and it has to drive reliable actions across all your infrastructure.

PaaS IoT platforms offer the most advantage at this stage. You have more control of your systems and are not locked into a specific software platform. You have the ability to customize and have tighter integration with your existing systems. This lets you adapt and evolve to meet the needs of your customers over time.

Which production architecture works for you?

Considering the needs of your production system likely go beyond the needs of your prototypes and minimum viable product (MVP), it is best to think about what additional features you will need to augment the capabilities of your selected IoT platform. The diagrams below show the difference between augmenting a SaaS platform versus a PaaS platform.

An IoT System Built Around SaaS Platform

An IoT System Built on a PaaS Platform

What else should be considered when choosing an IoT Platform?

When selecting an IoT platform, you are also choosing an ecosystem to join. This has ramifications that go beyond just the platform. Consider the following questions:

• What device types are already supported / how easy is it to support the devices I need?
• How close does the platform fit my use-case?
• How easy is it to get started and use?
• What skills do I need on my team to utilize the platform?
• Will my team get the support we need to succeed?
• Is the service reliable / highly available / trustworthy?
• What additional features and services will I have to develop?
• What systems do I need to integrate with? How easy is that?
• What will my ongoing costs be for the IoT platform as well as other systems I need to maintain.
• What happens if I want to change to a different IoT Platform?
• Am I building the skills and knowledge we need inside my organization to succeed in the future?

Jumpstarting your IoT Systems with Starter Components

Building a system based on a PaaS platform offers a lot of flexibility and control. But you are faced with configuring and deploying your own applications to get your system running. There are a lot of reasons why you don’t want to create things from scratch. You need a head start. You need to follow good patterns and industry best practices. So, what should you do?

We believe that starter components, a.k.a. solution templates, solution implementations, etc, offer a great jumpstart to standing up a robust system. The big cloud companies know this and offer templates for various use-cases. These can be used in any stage of the  IoT Journey. For example, AWS has a Smart Product Solution solution implementation that features capabilities to connect devices, process and analyze telemetry data, etc within a scalable framework. A fundamentally great feature of this is that it is based on AWS Cloud Development Kit (CDK) which means it can be programmatically deployed in minutes. Microsoft Azure has similar solution examples that can also be deployed and tested relatively quickly.

Additionally, there are a lot of benefits from working with a solution provider that has experience with IoT systems and can offer good guidance and support. SpinDance recently collaborated with our partner TwistThink to build Auris Cloud, a set of customizable IoT components that capture our combined years of experience working on IoT systems. Auris components are customizable to meet the needs of many different types of use cases and are deployable on AWS. Things like security, performance, and scalability are baked into the system. Auris can be optimized for different performance and cost models, integrated with other systems, and deployed as an application that you control. We believe this approach offers a great trade-off between fully custom and off-the-shelf solutions.

Summary

At SpinDance, we don’t recommend you try to build an IoT system from scratch. There are great solutions available from both SaaS and PaaS providers. They offer massive benefits in enabling you to build secure and scalable IoT solutions. However, we recommend you consider your organization’s goals and the stage you are in before locking yourself into an IoT platform. Be sure to start with your customer needs and build backward. Prototype and get things right before scaling. A SaaS IoT platform can be great for building proof of concepts or prototyping but may not work for you long term. For maximum customization, flexibility, and tighter integration with your other cloud applications we recommend a PaaS IoT platform. And for the lowest risks and maximum benefits, we recommend using pre-built components that can be customized to your needs.

How often have you wondered if you have turned off the oven in your kitchen or locked the main door of your house? IoT-powered devices are here to make lives easier and remind you of the necessary actions. Mobile IoT-based applications deliver mobility to control devices and gadgets remotely, regardless of one’s location. This blog post is focused to offer a better understanding of how IoT mobile app developments are becoming useful and how businesses can benefit from this trend.

Internet of Things (IoT), by now, does not need any introduction. However, in brief, IoT is a network of physical devices that are implanted with sensors, software and other technologies to connect and exchange data with other devices and systems over the internet. IoT has become an integral part of every aspect of our lives. From common household products to sophisticated industrial instruments, IoT connects numerous devices to simplify operations. Experts predict that by 2025, there will be more than 22 billion connected IoT devices worldwide. This digit is sure to take over the world by storm!

What makes mobile IoT the most popular trend?

Smartphones are used by billions of people around the world, and the number is growing every day. As a result of their ease of development, mobile apps make sense as the main channel for accessing IoT. It’s due to the reality of networked appliances that are easily accessed through mobile apps from any location, that improves people’s lives. Mobile is also significantly a more versatile platform for data transmission. IoT devices can be efficiently managed and monitored using a single application on a device. Mobile apps play a significant role in the rise of IoT.

What's the connection between IoT devices and mobile apps? 

An IoT device and a mobile phone communicate through a mobile app. The mobile app serves as the primary interface for controlling smart devices. Also, to increase the effectiveness of IoT, mobile IoT apps augment and enhance their utilization.

For instance, your phone can notify your coffee machine that you are approaching, allowing it to begin brewing coffee prior to your arrival. However, you may wonder that such IoT devices can be controlled from a desktop computer as well. Then, why should you have a mobile app? Here are two major reasons you should know:

• Mobile phones are better suited for accessing information from any location.
• Smartphones are jam-packed with sensors of all kinds. Wi-Fi, Bluetooth, and other wireless technologies are among the communication possibilities available to them.

Mobile phones not only transmit geolocation information to your devices with the help of mobile IoT apps but are the most convenient devices for managing IoT technologies because of their characteristics.

What are the benefits of IoT-based mobile apps? 

IoT-enabled services provide access, increase efficiency and drive greater personalization. Most IoT use cases are classified through the following three categories:

• Enabling access: Mobile-enabled IoT devices can ensure service delivery in remote areas.
• Improving efficiency: By real-time tracking of machinery, equipment, and field workers, businesses can gain insights, visibility and control through IoT, resulting in reduced waste, production boost, improved safety and extended life of assets.
• Personalization: Businesses can better understand and profile their customers using customer analytics, allowing them to give more tailored services on an individual level, altering the customer experience.

What are some examples of mobile IoT development? 

IoT is changing everyday aspects to enrich our lives by generating information to improve our convenience. It is bringing more and more things into the digital fold.

The capacity of IoT to supply sensor data and enable device-to-device communication is driving a wide range of applications. Let’s find out some of the most popular use cases and what they accomplish:

• Improving equipment and product quality monitoring to aid manufacturing efficiencies

With the help of IoT assistance, manufacturing machines can be monitored and examined on a continual basis to ensure that they are operating within acceptable tolerances. Moreover, quality faults can also be identified and addressed by monitoring products in real-time.

• Enhancing physical asset tracking

Businesses can easily track assets to find out where they are. They can utilize ring-fencing to protect high-value assets from being stolen or lost.

• Tracking human health and environmental factors through wearables

IoT wearables assist customers in better understanding their own health and allow clinicians to remotely monitor patients. This technology also enables organizations to monitor their employees' health and safety, which is particularly useful for those who work in hazardous situations.

• Embracing new possibilities

One example is the application of IoT in connected logistics for fleet management to improve efficiency and safety. Companies can utilize IoT fleet monitoring to increase efficiency by directing trucks in real-time.

• Innovating business processes

This is demonstrated by the usage of IoT devices for linked assets to monitor the health of remote machines and initiate service calls for preventive maintenance. Remote machine monitoring is also enabling new product-as-a-service business models, in which customers pay to use rather than acquire a product.

The final say

Mobile apps are at the forefront of IoT as we always have our smartphones handy. Smartphones also include a lot of sensors, which makes it easy to handle smart devices and gadgets. Undoubtedly, the market for IoT smartphone apps has a promising future through mobility. In the following years, IoT investments, as well as the number of connected devices and mobile applications, are predicted to skyrocket.

Internet of Things (IoT) today has brought a significant change in our day-to-day lives. And, with connectivity improving in the last few years it has become more widespread with the passing time. This technology has gained a lot of traction with the help of growing mobile technology. It is expected to witness a surge in the number of IoT applications that are expected in the coming years. 

So, it wouldn’t come as no surprise that companies are now starting to consider the integration of this nifty technology with iOS app development. This is because of the benefits such as ease of data collection, employee mobility, process automation, etc.

The integration of IoT into iPhone app development is one of the hottest technologies. It is making the rounds these days. Interestingly, this helps create numerous business opportunities for many enterprises across various sectors as more and more people find their way into the connected world of devices with data.

Before we move on to discussing how one can pick the right IoT development provider, allow us to also quickly list some of the main challenges you are likely to face during this process.

The development of IoT-driven iOS apps is also subject to a plethora of challenges and concerns as is any other digital product. Nonetheless, in the specific context of IoT apps, some of the key issues that are likely to emerge during the development process are connectivity, compatibility, privacy, and security. We mention these challenges to help you ensure that these challenges are properly addressed during the development process.

Now, some tips to help you find the right service provider for developing iPhone apps fortified with IoT.

1. Before you set off to find a development partner for your IoT-fortified iOS development project, it is highly advisable that you first take the time to understand precisely why you want to develop such an app. Take the time to elucidate the goal of developing such an app, what you expect to achieve with it, who is your target audience, etc. All these factors will, then, help you understand what your app should look like, the features it must have, the challenges you will face, and the kind of skills and experience you will need to help you get through this process with a robust product on the other end.

2. The next factor you must keep in mind while looking for a development partner is that the company you choose must be able to help you build a top-quality IoT-driven iOS app and that too without burning a hole in your pockets. What that means is to find a service provider who can deliver the required product without needing massive investments.

3. Now that IoT has firmly established itself as a force to be reckoned with, plenty of development service providers are looking to cash in on the popularity of this new technology. However, that does not necessarily mean they have the skill or experience one needs to develop high-quality iPhone apps. This is why experts recommend opting for a reputed company, even if engaging their services is comparatively more expensive. This will help ensure the timely development of a robust app.

It is clear as day to see that IoT brings a whole lot of value to the table, more so when it comes to iOS apps and their development. We understand trying to embrace new technology in your business and apps can be an intimidating endeavor, but hopefully, the above discussion can help allay some of the concerns you may have had about the process. For the rest, it is advisable to find an expert development company that has the relevant skills for developing iPhone apps for your business and the industry in which you operate. Once you find a trusted development partner, the rest of the process will be rather simple to navigate through.

Today the world is obsessed with the IoT, as if this is a new concept. We've been building the IoT for decades, but it was only recently some marketing "genius" came up with the new buzz-acronym.

Before there was an IoT, before there was an Internet, many of us were busy networking. For the Internet itself was a (brilliant) extension of what was already going on in the industry.

My first experience with networking was in 1971 at the University of Maryland. The school had a new computer, a $10 million Univac 1108 mainframe. This was a massive beast that occupied most of the first floor of a building. A dual-processor machine it was transistorized, though the control console did have some ICs. Rows of big tape drives mirrored the layman's idea of computers in those days. Many dishwasher-sized disk drives were placed around the floor and printers, card readers and other equipment were crammed into every corner. Two Fastrand drum memories, each consisting of a pair of six-foot long counterrotating drums, stored a whopping 90 MB each. Through a window you could watch the heads bounce around.

The machine was networked. It had a 300 baud modem with which it could contact computers at other universities. A primitive email system let users create mail which was queued till nightfall. Then, when demands on the machine were small, it would call the appropriate remote computer and forward mail. The system operated somewhat like today's "hot potato" packets, where the message might get delivered to the easiest machine available, which would then attempt further forwarding. It could take a week to get an email, but at least one saved the $0.08 stamp that the USPS charged.

The system was too slow to be useful. After college I lost my email account but didn't miss it at all.

By the late 70s many of us had our own computers. Mine was a home-made CP/M machine with a Z80 processor and a small TV set as a low-res monitor. Around this time Compuserve came along and I, like so many others, got an account with them. Among other features, users had email addresses. Pretty soon it was common to dial into their machines over a 300 baud modem and exchange email and files. Eventually Compuserve became so ubiquitous that millions were connected, and at my tools business during the 1980s it was common to provide support via this email. The CP/M machine gave way to a succession of PCs, Modems ramped up to 57 K baud.

My tools business expanded rapidly and soon we had a number of employees. Sneakernet was getting less efficient so we installed an Arcnet network using Windows 3.11. That morphed into Ethernet connections, though the cursing from networking problems multiplied about as fast as the data transfers. Windows was just terrible at maintaining reliable connectivity.

In 1992 Mike Lee, a friend from my Boys Night Out beer/politics/sailing/great friends group, which still meets weekly (though lately virtually) came by the office with his laptop. "You have GOT to see this" he intoned, and he showed me the world-wide web. There wasn't much to see as there were few sites. But the promise was shockingly clear. I was stunned.

The tools business had been doing well. Within a month we spent $100k on computers, modems and the like and had a new business: Softaid Internet Services. SIS was one of Maryland's first ISPs and grew quickly to several thousand customers. We had a T1 connection to MAE-EAST in the DC area which gave us a 1.5 Mb/s link… for $5000/month. Though a few customers had ISDN connections to us, most were dialup, and our modem shelf grew to over 100 units with many big fans keeping the things cool.

The computers all ran BSD Unix, which was my first intro to that OS.

I was only a few months back from a failed attempt to singlehand my sailboat across the Atlantic and had written a book-length account of that trip. I hastily created a web page of that book to learn about using the web. It is still online and has been read several million times in the intervening years. We put up a site for the tools business which eventually became our prime marketing arm.

The SIS customers were sometimes, well, "interesting." There was the one who claimed to be a computer expert, but who tried to use the mouse by waving it around over the desk. Many had no idea how to connect a modem. Others complained about our service because it dropped out when mom would pick up the phone to make a call over the modem's beeping. A lot of handholding and training was required.

The logs showed a shocking (to me at the time) amount of porn consumption. Over lunch an industry pundit explained how porn drove all media, from the earliest introduction of printing hundreds of years earlier.

The woman who ran the ISP was from India. She was delightful and had a wonderful marriage. She later told me it had been arranged; they met  their wedding day. She came from a remote and poor village and had had no exposure to computers, or electricity, till emigrating to the USA.

Meanwhile many of our tools customers were building networking equipment. We worked closely with many of them and often had big routers, switches and the like onsite that our engineers were working on. We worked on a lot of what we'd now call IoT gear: sensors et al connected to the net via a profusion of interfaces.

I sold both the tools and Internet businesses in 1997, but by then the web and Internet were old stories.

Today, like so many of us, I have a fast (250 Mb/s) and cheap connection into the house with four wireless links and multiple computers chattering to each other. Where in 1992 the web was incredibly novel and truly lacking in useful functionality, now I can't imagine being deprived of it. Remember travel agents? Ordering things over the phone (a phone that had a physical wire connecting it to Ma Bell)? Using 15 volumes of an encyclopedia? Physically mailing stuff to each other?

As one gets older the years spin by like microseconds, but it is amazing to stop and consider just how much this world has changed. My great grandfather lived on a farm in a world that changed slowly; he finally got electricity in his last year of life. His daughter didn't have access to a telephone till later in life, and my dad designed spacecraft on vellum and starched linen using a slide rule. My son once saw a typewriter and asked me what it was; I mumbled that it was a predecessor of Microsoft Word.

That he understood. I didn't have the heart to try and explain carbon paper.

Originally posted HERE.

Only for specific jobs

Just a few decades ago, headsets were meant for use only with specific job functions – primarily B2B. They were used as simply extensions of communication devices, reserved for astronauts, mission control engineers, air traffic controllers, call center agents, fire fighters, etc. who all had mission critical communication to convey while their hands had to deal with something more important than holding a communication device. In the B2C consumers space you rarely saw anyone wearing headsets in public. The only devices you saw attached to one’s ears were hearing aids.

Tale of two cities: Telephony and music

Most headsets were used for communication purposes, which also referred to as ‘Telephony’ mode. As with most communications, this requires bi-directional audio. Except for serious audiophiles and audio professionals, headsets were not used for music consumption. Any type of half-duplex audio consumption was referred to as ‘Music' mode.

Deskphones and speakerphones

Within the enterprise, a deskphone was the primary communication device for a long time. Speakerphones were becoming a common staple in meeting rooms, facilitating active collaboration amongst geographically distributed team members. So, there were ‘handsets’ but no ‘headsets’ quite yet. 

Mobile revolution: Communication and consumption

As the Internet and the browser were taking shape in the early ’90s, deskphones were getting untethered in the form of big and bulky cellular phones. At around the same time, a Body Area Network (BAN) wireless technology called Bluetooth was invented. Its original purpose was simply to replace the cords used for connecting a keyboard and mouse to the personal computer.

As cellular phones were slimming down and becoming more mainstream, scientists figured out how to use Bluetooth radio for short-range full-duplex audio communications as well. Fueled by rapid cell-phone proliferation, along with the need for convenient hands-free communication by enterprise executives and professionals (for whom hands-free communication while being mobile was important), monaural Bluetooth headsets started becoming a loyal companion to cell phones.

While headsets were used with various telephony devices for communications, portable analog music (Sony Walkman, anybody?) started giving way to portable digital music. Cue the iPod era. The portable music players primarily used simple wired speakers on a rope. These early ‘earbuds’ didn’t even have a microphone in them because they were meant solely for audio consumption – not for audio capture. 

The app economy, softphones and SaaS

Mobile revolution transformed simple communication devices into information exchange devices and then more recently, into mini super computers that have applications to take care of functions served by numerous individual devices like a telephony device, camera, calculator, music player, etc. As narrowband networks gave way to broadband networks for both the wired and wireless worlds, ‘communication’ and ‘media consumption’ began to transform in a significant way as well. 

Communication: Deskphones or ‘hard’-phones started being replaced by VoIP-based soft-phones. A new market segment called Unified Communications (UC) was born because of this hard- to soft-phone transition. UC has been a key growth driver for the enterprise headset market for the last several years, and it continues to show healthy growth. Enterprises could not part ways with circuit-switched telephony devices completely, but they started adopting packet-switched telephony services called soft-phones. So, UC communication device companies are effectively helping enterprises by being the bridge from ‘old’ to ‘new’ technology. UC has recently evolved into UC&C – where the second ‘C’ represents ‘Collaboration.’ Collaboration using audio and video (like Zoom or Teams calls) got a real shot in the arm because of the COVID-19-induced remote work scenario that has been playing out globally for the last year and a half.

Media consumption: ‘Static’ storage media (audio cassettes, VHS tapes, CDs, DVDs) and their corresponding media players, including portable digital music devices like iPods, were replaced by ‘streaming’ services in a swift fashion. 

Why did this transformation matter to the headset world?

Communication & collaboration by the enterprise users as well as media consumption by consumers collided head-on. Because of this, monaural headsets have almost become irrelevant. Nearly all headsets today are binaural or stereo, and have microphone(s) in them.

This is because the same device needs to serve the purposes of both: consuming half-duplex audio when listening to music, podcasts, or watching movies or webinars, and enabling full-duplex audio for a telephone conversation, a conference call, or video conference.

Fewer form factors… more smarts 

From: Very few companies building manifold headset form factors that catered to the needs of every diverse persona out there.

To: Quite a few companies (obviously, a handful of them a great deal more successful than the others) driving the headset space to effectively just two form factors:

1. Tiny True Wireless Stereo (TWS) earbuds and
2. Big binaural occluding cans!

Less hardware… more software

Such a trend has been in place for quite some time impacting several industries. Headsets are no exception. Ever so sophisticated semiconductor components and proliferation of miniaturized Microelectromechanical Systems, or MEMS in short, components have taken the place of numerous bulkier hardware components.

What do modern headsets primarily do with regards to audio?

1. Render received audio in the wearer’s ear
2. Capture spoken audio from the wearer’s mouth
3. Calculate anti-noise and render it in the wearer’s ear (in noise-cancelling headsets)

Sounds straightforward, right? It is not as simple as it sounds – at least for enterprise-grade professional headsets. Audio is processed in the digital domain in all modern headsets using sophisticated digital signal processing techniques. DSP algorithms running on the DSP cores of the processors are the most compute-intensive aspects of these devices. Capture/transmit/record audio DSP is relatively more complicated than the render/receive/llayback audio DSP. Depending on the acoustic design (headset boom, number of microphones, speaker/microphone placement), audio performance requirements, and other audio feature requirements, the DSP workload varies.

Intelligence right at the edge!

Headsets are true edge devices. Most headset designs have severe constraints around several factors: cost, size, weight, power, MIPS, memory, etc.

Headsets are right at the horse’s mouth (pun intended) of massive trends and modern use cases like:

• Wake word detection for virtual private assistants (VPAs)
• Keyword detection for device control and various other data/analytics purposes
• Modern user interface (UI) techniques like voice-as-UI, touch-as-UI, and gestures-as-UI
• Transmit noise cancellation/suppression (TxNC or TxNS)
• Adaptive ambient noise cancellation (ANC) mode selection
• Real-time transcription assistance
• Ambient noise identification
• Speech synthesis, speaker identification, speaker authentication, etc.

Most importantly, note that there is immense end customer value for all these capabilities.

Until recently, even if one wanted to, very little could be done to support most of these advanced capabilities right in the headset. Just the features and functionalities that were addressable within the computational limits of the on-board DSP cores using traditional DSP techniques were all that could be supported.

Enter edge compute, AutoML, tinyML, and MLOps revolutions…

Several DSP-only workloads of the past are rapidly transitioning to an efficient hybrid model of DSP+ML workloads. Quite a few ML only capabilities that were not even possible using traditional DSP techniques are becoming possible right now as well. All of this is happening within the same constraints that existed before.

Silicon as well as software innovations are behind such possibilities. Silicon innovations are relatively slow to be adopted into device architectures at the moment, but they will be over time. Software innovations extract more value out of existing silicon architectures while helping converge on more efficient hardware architecture designs for next-generation products.

Thanks to embedded machine learning, tasks and features that were close to impossible are becoming a reality now. Production-grade Inference models with tiny program and data memory footprints in addition to impressive performance are possible today because of major advancements in AutoML and tinyML techniques. Building these models does not require massive amounts of data either. The ML-framework and automated yet flexible process offered by platforms like those from Edge Impulse make the ML model creation process simple and efficient compared to traditional methods of building such models.

Microphones and sensors galore

All headsets feature at least one microphone, and many feature multiple, sometimes up to 16 of them! The field of ML for audio is vast, and it is continuing to expand further. Many of the ML inferencing that was possible only at the cloud backends or sophisticated compute-rich endpoints are now fully possible in most of the resource-constrained embedded IoT silicon.

Microphones themselves are sensors, but many other sensors like accelerometers, capacitive touch, passive infrared (PIR), ultrasonic, radar, and ultra-wideband (UWB) are making their way into headsets to meet and exceed customer expectations. Spatial audio, aka 3D audio, is one such application that utilizes several sensors to give the end-user an immersive audio experience. Sensor fusion is the concept of utilizing data from multiple sensors concurrently to arrive at intelligent decisions. Sensor fusion implementations that use modern ML techniques have been shown to have impressive performance metrics compared to traditional non-ML methods.

Transmit noise suppression (TxNS) has always been the holy grail of all premium enterprise headsets. It is an important aspect of enterprise collaboration. A magical combination of physical acoustic design – which is more art than science – combined with optimally tuned complex audio DSP algorithms implemented under severe MIPS, memory, latency, and other constraints. In recent years, some groundbreaking work has been done in utilizing recursive neural network (RNN) techniques to improve TxNS performance to levels that were never seen before. Because of their complexity and high-compute footprint, these techniques have been incorporated into devices that have mobile phone platform-like compute capabilities. The challenge of bringing such solutions to the resource-constrained embedded systems, such as enterprise headsets, while staying within the constraints laid out earlier, remains unsolved to a major extent. Advancements in embedded silicon technology, combined with tinyML/AutoML software innovations listed above, is helping address this and several other ML challenges.

Conclusion

Modern use cases that enable the hearables to become ‘smart’ are compelling. Cloud-based frameworks and tools necessary to build, iterate, optimize, and maintain high performance small footprint ML models to address these applications are readily available from entities like Edge Impulse. Any hearable entity that doesn’t take full advantage of this staggering advancement in technology will be at a competitive disadvantage.

Originally posted on the Edge Impulse blog by Arun Rajasekaran.

By Ashley Ferguson

Thanks to the introduction of connected products, digital services, and increased customer expectations, it has been the trend for IoT enterprise spend to consistently increase. The global IoT market is projected to reach $1.4 trillion USD by 2027. The pressure to build IoT solutions and get a return on those investments has teams on a frantic search for IoT engineers to secure in-house IoT expertise. However, due to the complexity of IoT solutions, finding this in a single engineer is a difficult or impossible proposition.

So how do you adjust your search for an IoT engineer? The first step is to acknowledge that IoT solution development requires the fusion of multiple disciplines. Even simple IoT applications require hardware and software engineering, knowledge of protocols and connectivity, web development skills, and analytics. Certainly, there are many engineers with IoT knowledge, but complete IoT solutions require a team of partners with diverse skills. This often requires utilizing external sources to supplement the expertise gaps.

THE ANATOMY OF AN IoT SOLUTION

IoT solutions provide enterprises with opportunities for innovation through new product offerings and cost savings through refined operations. An IoT solution is an integrated bundle of technologies that help users answer a question or solve a specific problem by receiving data from devices connected to the internet. One of the most common IoT use cases is asset tracking solutions for enterprises who want to monitor trucks, equipment, inventory, or other items with IoT. The anatomy of an asset tracking IoT solution includes the following:

This is a simple asset tracking example. For more complex solutions including remote monitoring or predictive maintenance, enterprises must also consider installation, increased bandwidth, post-development support, and UX/UI for the design of the interface for customers or others who will use the solution. Enterprise IoT solutions require an ecosystem of partners, components, and tools to be brought to market successfully.

Consider the design of your desired connected solution. Do you know where you will need to augment skills and services?

If you are in the early stages of IoT concept development and at the center of a buy vs. build debate, it may be a worthwhile exercise to assess your existing team’s skills and how they correspond with the IoT solution you are trying to build.

IoT SKILLS ASSESSMENT

• Hardware
• Firmware
• Connectivity
• Programming
• Cloud
• Data Science
• Presentation
• Technical Support and Maintenance
• Security
• Organizational Alignment

MAKING TIME FOR IoT APPLICATION DEVELOPMENT

The time it will take your organization to build a solution is dependent on the complexity of the application. One way to estimate the time and cost of IoT application development is with Indeema’s IoT Cost Calculator. This tool can help roughly estimate the hours required and the cost associated with the IoT solution your team is interested in building. In MachNation’s independent comparison of the Losant Enterprise IoT Platform and Azure, it was determined that developers could build an IoT solution in 30 hours using Losant and in 74-94 hours using Microsoft Azure.

As you consider IoT application development, consider the makeup of your team. Is your team prepared to dedicate hours to the development of a new solution, or will it be a side project? Enterprise IT teams are often in place to maintain existing operating systems and to ensure networks are running smoothly. In the event that an IT team is tapped to even partially build an IoT solution, there is a great chance that the IT team will need to invite partners to build or provide part of the stack.

HOW THE IoT JOB GETS DONE

Successful enterprises recognize early on that some of these skills will need to be augmented through additional people, through an ecosystem, or with software. It will require more than one ‘IoT engineer’ for the job. According to the results of a McKinsey survey, “the preferences of IoT leaders suggest a greater willingness to draw capabilities from an ecosystem of technology partners, rather than rely on homegrown capabilities.”

IoT architecture alone is intricate. Losant, an IoT application enablement platform, is designed with many of the IoT-specific components already in place. Losant enables users to build applications in a low-to-no code environment and scale them up to millions of devices. Losant is one piece in the wider scope of an IoT solution. In order to build a complete solution, an enterprise needs hardware, software, connectivity, and integration. For those components, our team relies on additional partners from the IoT ecosystem.

The IoT ecosystem, also known as the IoT landscape, refers to the network of IoT suppliers (hardware, devices, software platforms, sensors, connectivity, software, systems integrators, data scientists, data analytics) whose combined services help enterprises create complete IoT solutions. At Losant, we’ve built an IoT ecosystem with reliable experienced partners. When IoT customers need custom hardware, connectivity, system integrators, dev shops, or other experts with proven IoT expertise, we can tap one of our partners to help in their areas of expertise.

SECURE, SCALABLE, SEAMLESS IoT

Creating secure, scalable, and seamless IoT solutions for your environment begins by starting small. Starting small gives your enterprise the ability to establish its ecosystem. Teams can begin with a small investment and apply learnings to subsequent projects. Many IoT success stories begin with enterprises setting out to solve one problem. The simple beginnings have enabled them to now reap the benefits of the data harvest in their environments.

Originally posted here.

By GE Digital

“The End of Cloud Computing.” “The Edge Will Eat The cloud.” “Edge Computing—The End of Cloud Computing as We Know It.”  

Such headlines grab attention, but don’t necessarily reflect reality—especially in Industrial Internet of Things (IoT) deployments. To be sure, edge computing is rapidly emerging as a powerful force in turning industrial machines into intelligent machines, but to paraphrase Mark Twain: “The reports of the death of cloud are greatly exaggerated.” 

The Tipping Point: Edge Computing Hits Mainstream

We’ve all heard the stats—billions and billions of IoT devices, generating inconceivable amounts of big data volumes, with trillions and trillions of U.S. dollars to be invested in IoT over the next several years. Why? Because industrials have squeezed every ounce of productivity and efficiency out of operations over the past couple of decades, and are now looking to digital strategies to improve production, performance, and profit. 

The Industrial Internet of Things (IIoT) represents a world where human intelligence and machine intelligence—what GE Digital calls minds and machines—connect to deliver new value for industrial companies. 

In this new landscape, organizations use data, advanced analytics, and machine learning to drive digital industrial transformation. This can lead to reduced maintenance costs, improved asset utilization, and new business model innovations that further monetize industrial machines and the data they create. 

Despite the “cloud is dead” headlines, GE believes the cloud is still very important in delivering on the promise of IIoT, powering compute-intense workloads to manage massive amounts of data generated by machines. However, there’s no question that edge computing is quickly becoming a critical factor in the total IIoT equation.

This webinar series will take you through the fundamental steps, tools and opportunities for simplifying IoT development.

WEBINAR SERIES:   Fast and Fearless - The Future of IoT Software Development SUMMARY The IoT is transforming the software landscape. What was a relatively straightforward embedded software stack, has been revolutionized due to the IoT where developers juggle specialized workloads, security, machine learning, real-time connectivity, managing devices in the field - the list goes on. How can our industry help developers prototype ‘fearlessly’ because the tools and platforms allow them to navigate varying IoT components? How can developers move to production quickly, capitalizing on innovation opportunities in emerging IoT markets?  This webinar series will take you through the fundamental steps, tools and opportunities for simplifying IoT development. Each webinar will be a panel discussion with industry experts who will share their experience and development tips on the below topics. Part One of Four: The IoT Software Developer Experience Date: Tuesday, May 11, 2021 Webinar Recording Available Here Part Two of Four: AI and IoT Innovation Date: Tuesday, June 29, 2021 Time: 8:00 am PDT/ 3:00 pm UTC Duration: 60 minutes Click Here to Register for Part Two Part Three of Four: Making the Most of IoT Connectivity Date: Tuesday, September 28, 2021 Time: 8:00 am PDT/ 3:00 pm UTC Duration: 60 minutes Click Here to Register for Part Three Part Four of Four: IoT Security Solidified and Simplified Date: Tuesday, November 16, 2021 Time: 8:00 am PDT/ 3:00 pm UTC Duration: 60 minutes Click Here to Register for Part Four

By Sachin Kotasthane

In his book, 21 Lessons for the 21st Century, the historian Yuval Noah Harari highlights the complex challenges mankind will face on account of technological challenges intertwined with issues such as nationalism, religion, culture, and calamities. In the current industrial world hit by a worldwide pandemic, we see this complexity translate in technology, systems, organizations, and at the workplace.

While in my previous article, Humane IIoT, I discussed the people-centric strategies that enterprises need to adopt while onboarding IoT initiatives of industrial IoT in the workforce, in this article, I will share thoughts on how new-age technologies such as AI, ML, and big data, and of course, industrial IoT, can be used for effective management of complex workforce problems in a factory, thereby changing the way people work and interact, especially in this COVID-stricken world.

Workforce related problems in production can be categorized into:

1. Time complexity
2. Effort complexity
3. Behavioral complexity

Problems categorized in either of the above have a significant impact on the workforce, resulting in a detrimental effect on the outcome—of the product or the organization. The complexity of these problems can be attributed to the fact that the workforce solutions to such issues cannot be found using just engineering or technology fixes as there is no single root-cause, rather, a combination of factors and scenarios. Let us, therefore, explore a few and seek probable workforce solutions.

Figure 1: Workforce Challenges and Proposed Strategies in Production

1. Addressing Time Complexity

   Any workforce-related issue that has a detrimental effect on the operational time, due to contributing factors from different factory systems and processes, can be classified as a time complex problem.

   Though classical paper-based schedules, lists, and punch sheets have largely been replaced with IT-systems such as MES, APS, and SRM, the increasing demands for flexibility in manufacturing operations and trends such as batch-size-one, warrant the need for new methodologies to solve these complex problems.

   • Worker attendance

     Anyone who has experienced, at close quarters, a typical day in the life of a factory supervisor, will be conversant with the anxiety that comes just before the start of a production shift. Not knowing who will report absent, until just before the shift starts, is one complex issue every line manager would want to get addressed. While planned absenteeism can be handled to some degree, it is the last-minute sick or emergency-pager text messages, or the transport delays, that make the planning of daily production complex.

     What if there were a solution to get the count that is almost close to the confirmed hands for the shift, an hour or half, at the least, in advance? It turns out that organizations are experimenting with a combination of GPS, RFID, and employee tracking that interacts with resource planning systems, trying to automate the shift planning activity.

     While some legal and privacy issues still need to be addressed, it would not be long before we see people being assigned to workplaces, even before they enter the factory floor.

     During this course of time, while making sure every line manager has accurate information about the confirmed hands for the shift, it is also equally important that health and well-being of employees is monitored during this pandemic time. Use of technologies such as radar, millimeter wave sensors, etc., would ensure the live tracking of workers around the shop-floor and make sure that social distancing norms are well-observed.

   • Resource mapping

     While resource skill-mapping and certification are mostly HR function prerogatives, not having the right resource at the workstation during exigencies such as absenteeism or extra workload is a complex problem. Precious time is lost in locating such resources, or worst still, millions spent in overtime.

     What if there were a tool that analyzed the current workload for a resource with the identified skillset code(s) and gave an accurate estimate of the resource’s availability? This could further be used by shop managers to plan manpower for a shift, keeping them as lean as possible.

     Today, IT teams of OEMs are seen working with software vendors to build such analytical tools that consume data from disparate systems—such as production work orders from MES and swiping details from time systems—to create real-time job profiles. These results are fed to the HR systems to give managers the insights needed to make resource decisions within minutes.

2. Addressing Effort Complexity

   Just as time complexities result in increased  production time, problems in this category result in an increase in effort by the workforce to complete the same quantity of work. As the effort required is proportionate to the fatigue and long-term well-being of the workforce, seeking workforce solutions to reduce effort would be appreciated. Complexity arises when organizations try to create a method out-of-madness from a variety of factors such as changing workforce profiles, production sequences, logistical and process constraints, and demand fluctuations.

   Thankfully, solutions for this category of problems can be found in new technologies that augment existing systems to get insights and predictions, the results of which can reduce the efforts, thereby channelizing it more productively. Add to this, the demand fluctuations in the current pandemic, having a real-time operational visibility, coupled with advanced analytics, will ensure meeting shift production targets.

   • Intelligent exoskeletons

     Exoskeletons, as we know, are powered bodysuits designed to safeguard and support the user in performing tasks, while increasing overall human efficiency to do the respective tasks. These are deployed in strain-inducing postures or to lift objects that would otherwise be tiring after a few repetitions. Exoskeletons are the new-age answer to reducing user fatigue in areas requiring human skill and dexterity, which otherwise would require a complex robot and cost a bomb.

     However, the complexity that mars exoskeleton users is making the same suit adaptable for a variety of postures, user body types, and jobs at the same workstation. It would help if the exoskeleton could sense the user, set the posture, and adapt itself to the next operation automatically.

     Taking a leaf out of Marvel’s Iron Man, who uses a suit that complements his posture that is controlled by JARVIS, manufacturers can now hope to create intelligent exoskeletons that are always connected to factory systems and user profiles. These suits will adapt and respond to assistive needs, without the need for any intervention, thereby freeing its user to work and focus completely on the main job at hand.

     Given the ongoing COVID situation, it would make the life of workers and the management safe if these suits are equipped with sensors and technologies such as radar/millimeter wave to help observe social distancing, body-temperature measuring, etc.

   • Highlighting likely deviations

     The world over, quality teams on factory floors work with checklists that the quality inspector verifies for every product that comes at the inspection station. While this repetitive task is best suited for robots, when humans execute such repetitive tasks, especially those that involve using visual, audio, touch, and olfactory senses, mistakes and misses are bound to occur. This results in costly reworks and recalls.

     Manufacturers have tried to address this complexity by carrying out rotation of manpower. But this, too, has met with limited success, given the available manpower and ever-increasing workloads.

     Fortunately, predictive quality integrated with feed-forwards techniques and some smart tracking with visuals can be used to highlight the area or zone on the product that is prone to quality slips based on data captured from previous operations. The inspector can then be guided to pay more attention to these areas in the checklist.

3. Addressing Behavioral Complexity

   Problems of this category usually manifest as a quality issue, but the root cause can often be traced to the workforce behavior or profile. Traditionally, organizations have addressed such problems through experienced supervisors, who as people managers were expected to read these signs, anticipate and align the manpower.

   However, with constantly changing manpower and product variants, these are now complex new-age problems requiring new-age solutions.

   • Heat-mapping workload

     Time and motion studies at the workplace map the user movements around the machine with the time each activity takes for completion, matching the available cycle-time, either by work distribution or by increasing the manpower at that station. Time-consuming and cumbersome as it is, the complexity increases when workload balancing is to be done for teams working on a single product at the workstation. Movements of multiple resources during different sequences are difficult to track, and the different users cannot be expected to follow the same footsteps every time.

     Solving this issue needs a solution that will monitor human motion unobtrusively, link those to the product work content at the workstation, generate recommendations to balance the workload and even out the ‘congestion.’ New industrial applications such as short-range radar and visual feeds can be used to create heat maps of the workforce as they work on the product. This can be superimposed on the digital twin of the process to identify the zone where there is ‘congestion.’ This can be fed to the line-planning function to implement corrective measures such as work distribution or partial outsourcing of the operation.

   • Aging workforce (loss of tribal knowledge)

     With new technology coming to the shop-floor, skills of the current workforce get outdated quickly. Also, with any new hire comes the critical task of training and knowledge sharing from experienced hands. As organizations already face a shortage of manpower, releasing more hands to impart training to a larger workforce audience, possibly at different locations, becomes an even more daunting task.

     Fully realizing the difficulties and reluctance to document, organizations are increasingly adopting AR-based workforce trainings that map to relevant learning and memory needs. These AR solutions capture the minutest of the actions executed by the expert on the shop-floor and can be played back by the novice in-situ as a step-by-step guide. Such tools simplify the knowledge transfer process and also increase worker productivity while reducing costs.

     Further, in extraordinary situations such  as the one we face at present, technologies such as AR offer solutions for effective and personalized support to field personnel, without the need to fly in specialists at multiple sites. This helps keep them safe, and accessible, still.

Key takeaways and Actionable Insights

The shape of the future workforce will be the result of complex, changing, and competing forces. Technology, globalization, demographics, social values, and the changing personal expectations of the workforce will continue to transform and disrupt the way businesses operate, increasing the complexity and radically changing where, and when of future workforce, and how work is done. While the need to constantly reskill and upskill the workforce will be humongous, using new-age techniques and technologies to enhance the effectiveness and efficiency of the existing workforce will come to the spotlight.

Figure 2: The Future IIoT Workforce

Organizations will increasingly be required to:

1. Deploy data farming to dive deep and extract vast amounts of information and process insights embedded in production systems. Tapping into large reservoirs of ‘tribal knowledge’ and digitizing it for ingestion to data lakes is another task that organizations will have to consider.
2. Augment existing operations systems such as SCADA, DCS, MES, CMMS with new technology digital platforms, AI, AR/VR, big data, and machine learning to underpin and grow the world of work. While there will be no dearth of resources in one or more of the new technologies, organizations will need to ‘acqui-hire’ talent and intellectual property using a specialist, to integrate with existing systems and gain meaningful actionable insights.
3. Address privacy and data security concerns of the workforce, through the smart use of technologies such as radar and video feeds.

Nonetheless, digital enablement will need to be optimally used to tackle the new normal that the COVID pandemic has set forth in manufacturing—fluctuating demands, modular and flexible assembly lines, reduced workforce, etc.

Originally posted here.

The Internet of Things is growing at breakneck speed. One report suggests that the global market for IoT will surpass $1.38 trillion by 2026 — a substantial increase from its 2020 valuation of $761.4 billion.

The IoT is nothing without IoT platforms — middleware that connects sensors, assets, data, software, and business processes. It brings all the different components of your IoT infrastructure together so your business can get every possible benefit.

There are many IoT platforms on the market, and it’s important to find the right one for your business. This can be a challenging task, with lots of complex and competing information to sift through. 

In this article, we’ve put together a list of the main factors that should drive your decision when settling on an IoT platform, helping you make an informed decision that leads to the best solution for your needs.

Why you need an IoT platform?

There are many reasons to consider investing in an IoT platform. Essentially, the job of an IoT platform is to act as a ready-made framework for all your IoT infrastructure, pulling everything together and helping you start getting the benefits as quickly as possible. Here are some of the biggest advantages of a good IoT platform:

• It saves money, by making it more likely that your project will succeed and reducing the amount of time you’ll need to spend developing your own systems and fixing problems. Without relying on an IoT platform, it’s more likely that your project will fail and cost money. IoT platforms also centralize the management of your IoT network which is much more cost-efficient than trying to manage a scattered collection of devices.
• It helps provide security, ensuring your devices are safe, keeping your valuable data safe from the hands of hackers and cybercriminals, and giving you peace of mind.
• It helps you go to market quicker. IoT platforms take care of many aspects of your IoT project, saving you significant time and allowing you to roll out a prototype quickly.
• Good IoT platforms come packed with ready-made features, from help with billing to data analytics support, all geared towards helping you get the most out of your IoT infrastructure and providing valuable support to every member of your team.
• Device and data integration. IoT platforms bring all your devices together and integrate them into one central system. This way, you can integrate the data with your enterprise systems and enhance your organization’s existing processes. The result is a more cohesive network with each part supporting the whole, as opposed to a disparate collection of individual devices.
• It helps improve and streamline operations across your entire business by bringing IoT data together with data from external sources, allowing for a more holistic view of your entire organization which can drive better working processes and help you hit your goals in various areas.

What to look for in an IoT platform?

The best IoT platforms can provide a whole host of major advantages to your project and business as a whole. By providing connectivity as a service, they simplify the process of managing IoT devices with various connectivity technologies and remove the need to establish a contract with multiple network providers. 

But it’s important to pick the right platform for your specific needs. Here are some things to consider to ensure you make the right choice.

Connectivity management

Connectivity is a huge factor when it comes to IoT. Each project and organization has its own specific connectivity requirements, and this will have a direct impact on which IoT platform is the best fit.

Some IoT platforms are more specialized in certain technologies than others. Ideally, you should choose a platform that’s able to orchestrate a range of different connectivity technologies like LoRaWAN, Sigfox, NB-IoT, LTE Cat. M1, 4G, 5G, and WIFI.

Geographical location is also something to consider. Your IoT platform should be able to support IoT applications and devices in all the different geographical regions you need it to.

Scalability

Your IoT project will almost certainly grow over time. As this technology expands and becomes more widely used, almost every business is likely to find itself using more and more IoT devices and functions across multiple use cases.

Your IoT platform should be prepared for this. Select a platform that can comfortably scale as the project grows and is fit for all IoT project states from just a handful of devices in one area to thousands spread across many regions.

The best IoT platforms should be able to scale across a range of different deployment models, such as:

• In a public cloud
• In a private cloud
• On your business premises

Security

Another major concern for IoT networks is security. Attacks on IoT devices are on the rise, with 33% of infected devices now part of the IoT. It’s essential to make sure you choose an IoT platform that prioritizes security.

If you don’t take security seriously, you’re putting your IoT infrastructure at risk of cyberattacks, which could result in downtime, the loss of sensitive data, and serious reputational damage. On top of this, many companies have to comply with strict requirements when it comes to data ownership and security, which means you could face legal penalties if your data is breached.

It’s no longer enough to simply secure your business premises — in our increasingly remotely connected world, you have to keep your devices safe wherever they are. Your IoT platform should also be able to integrate with common cloud infrastructures like Google Cloud, Microsoft Azure, and Amazon AWS.

Usability

The whole point of IoT is to make your life and business processes easier. It shouldn’t add an extra layer of difficulty and complexity to your systems. The best IoT platforms are straightforward and easy to integrate with existing processes.

The main user groups to consider here are:

• The people who will actually be using the system — your end-users
• The people whose job it is to maintain the system like your company’s internal engineers

For both of these groups, the IoT platform should be as user-friendly as possible with minimal friction and challenges. This not only helps you get the most out of your technology but also keeps your team happy and stress-free.

End-user application

It is crucial to make sure that your IoT platform can be integrated with your final application. Typically, you want the platform to have a standardized interface (REST API) that allows you to connect your end-user smart application and make use of the data for your particular business case.

Your chosen platform should also support the visualization of data during a pilot, as this helps you understand your IoT systems as closely as possible and communicate this to other members of your organization.

Resilience to technological change

If there’s one thing we can be sure of when it comes to technology, it’s that constant change is unavoidable. This is a good thing for businesses and ensures constant progress and development, but when it comes to IoT systems it’s essential to prepare for this ongoing change.

Your hardware, connectivity, and applications need to be adaptable and resistant to change. Otherwise, you’ll run into issues like technological lock-in where you’re forced to use technology that is no longer sufficient for the demands of the time.

One way to ensure resistance to change is to make it possible to exchange the components of your IoT solution at any time, without negatively impacting the overall final application. This allows you to modify and upgrade your infrastructure bit-by-bit over time without major delays and downtime.

When it comes to IoT platforms, there is no one-size-fits-all answer. You need to take the time to figure out which platforms are the best fit for your unique set of needs and challenges, and pick one that can help you get the most out of your network.

Flowchart of IoT in Mining

by Vaishali Ramesh

Introduction – Internet of Things in Mining

The Internet of things (IoT) is the extension of Internet connectivity into physical devices and everyday objects. Embedded with electronics, Internet connectivity, and other forms of hardware; these devices can communicate and interact with others over the Internet, and they can be remotely monitored and controlled. In the mining industry, IoT is used as a means of achieving cost and productivity optimization, improving safety measures and developing their artificial intelligence needs.

IoT in the Mining Industry

Considering the numerous incentives it brings, many large mining companies are planning and evaluating ways to start their digital journey and digitalization in mining industry to manage day-to-day mining operations. For instance:

• Cost optimization & improved productivity through the implementation of sensors on mining equipment and systems that monitor the equipment and its performance. Mining companies are using these large chunks of data – 'big data' to discover more cost-efficient ways of running operations and also reduce overall operational downtime.
• Ensure the safety of people and equipment by monitoring ventilation and toxicity levels inside underground mines with the help of IoT on a real-time basis. It enables faster and more efficient evacuations or safety drills.
• Moving from preventive to predictive maintenance
• Improved and fast-decision making The mining industry faces emergencies almost every hour with a high degree of unpredictability. IoT helps in balancing situations and in making the right decisions in situations where several aspects will be active at the same time to shift everyday operations to algorithms.

IoT & Artificial Intelligence (AI) application in Mining industry

Another benefit of IoT in the mining industry is its role as the underlying system facilitating the use of Artificial Intelligence (AI). From exploration to processing and transportation, AI enhances the power of IoT solutions as a means of streamlining operations, reducing costs, and improving safety within the mining industry.

Using vast amounts of data inputs, such as drilling reports and geological surveys, AI and machine learning can make predictions and provide recommendations on exploration, resulting in a more efficient process with higher-yield results.

AI-powered predictive models also enable mining companies to improve their metals processing methods through more accurate and less environmentally damaging techniques. AI can be used for the automation of trucks and drills, which offers significant cost and safety benefits.

Challenges for IoT in Mining 

Although there are benefits of IoT in the mining industry, implementation of IoT in mining operations has faced many challenges in the past.

• Limited or unreliable connectivity especially in underground mine sites
• Remote locations may struggle to pick up 3G/4G signals
• Declining ore grade has increased the requirements to dig deeper in many mines, which may increase hindrances in the rollout of IoT systems

Mining companies have overcome the challenge of connectivity by implementing more reliable connectivity methods and data-processing strategies to collect, transfer and present mission critical data for analysis. Satellite communications can play a critical role in transferring data back to control centers to provide a complete picture of mission critical metrics. Mining companies worked with trusted IoT satellite connectivity specialists such as ‘Inmarsat’ and their partner eco-systems to ensure they extracted and analyzed their data effectively.

Cybersecurity will be another major challenge for IoT-powered mines over the coming years

 As mining operations become more connected, they will also become more vulnerable to hacking, which will require additional investment into security systems.

Following a data breach at Goldcorp in 2016, that disproved the previous industry mentality that miners are not typically targets, 10 mining companies established the Mining and Metals Information Sharing and Analysis Centre (MM-ISAC) to share cyber threats among peers in April 2017.

In March 2019, one of the largest aluminum producers in the world, Norsk Hydro, suffered an extensive cyber-attack, which led to the company isolating all plants and operations as well as switching to manual operations and procedures. Several of its plants suffered temporary production stoppages as a result. Mining companies have realized the importance of digital security and are investing in new security technologies.

Digitalization of Mining Industry - Road Ahead

Many mining companies have realized the benefits of digitalization in their mines and have taken steps to implement them. There are four themes that are expected to be central to the digitalization of the mining industry over the next decade are listed below:

The above graph demonstrates the complexity of each digital technology and its implementation period for the widespread adoption of that technology. There are various factors, such as the complexity and scalability of the technologies involved in the adoption rate for specific technologies and for the overall digital transformation of the mining industry.

The world can expect to witness prominent developments from the mining industry to make it more sustainable. There are some unfavorable impacts of mining on communities, ecosystems, and other surroundings as well. With the intention to minimize them, the power of data is being harnessed through different IoT statements. Overall, IoT helps the mining industry shift towards resource extraction, keeping in mind a particular time frame and footprint that is essential.

Originally posted here.