LoRaWAN (Long Range Wide Area Network) and LoRa (Low Power Wide Area Network) are communication technologies used in Internet of Things (IoT) applications, including monitoring traffic, parking, and air quality in smart cities. Although they use similar underlying LoRa modulation technology, their focus and applications are different.

LoRaWAN:

Focus: LoRaWAN is a wide-area network protocol whose main focus is to provide a long-distance, low-power wide-area IoT communication solution. It focuses on connecting large numbers of low-power sensors and devices to enable communication over long distances.

Network Topology: LoRaWAN usually adopts a star network topology, where end nodes (sensors and devices) are connected to a central network server through a gateway. This structure is suitable for large-scale sensor deployment, such as sensor networks in smart cities.

Security: LoRaWAN has strong security features, including data encryption and authentication, to ensure that transmitted data maintains confidentiality and integrity.

Device Management: LoRaWAN supports device management and remote device configuration to simplify large-scale device deployment and maintenance.

Open Standards: LoRaWAN uses open standards, allowing devices and networks from different vendors to work together, improving interoperability.

LoRa:

Focus: LoRa is the underlying physical layer technology of LoRaWAN, which mainly focuses on providing long-distance, low-power communication. It can be used for a variety of different communication protocols and applications, not just LoRaWAN.

Communication flexibility: LoRa is generally more flexible and can be used for point-to-point communication, point-to-multipoint communication or other communication with specific needs. It can be customized according to the requirements of the specific application.

Low power consumption: LoRa's low power consumption characteristics make it suitable for battery-powered sensors and devices, allowing long-term operation.

Proprietary or custom protocols: LoRa can be used with custom or proprietary communication protocols, customized to the needs of specific applications.

In general, LoRaWAN is more suitable for large-scale IoT deployments, such as monitoring systems in smart cities. It provides a standardized, secure, and low-power communication solution. LoRa is more flexible and can be used for various specific purposes of communication, but usually requires more customization and configuration. In practical applications, the choice of which technology to use depends on the specific needs and deployment situation.

Automated liquid packaging has emerged as a critical component in enhancing efficiency and accuracy in modern production lines. This process involves the use of advanced technology to automate the filling and packaging of liquids in an array of production processing businesses in the food, beverage, cannabis, pharmaceutical, chemical, and cosmetic industries. For instance, the food and beverage industry benefits immensely from automation by ensuring precise measurement and hygiene standards, elevating the quality and safety of products.

Similarly, the cannabis industry relies heavily on automated liquid packaging for accurate dosages and secure packaging, instrumental in sustaining consumer trust. In the pharmaceutical sector, precision and consistency are paramount; automating liquid packaging addresses these needs, ensuring that each product adheres to stringent industry standards and regulations. These are just some examples of how automation transforms liquid packaging operations. 

This blog post will highlight how this helps improve manufacturing speed and accuracy.

1. Increased Production Speeds through Continuous Operations

Automating liquid packaging processes greatly enhances production speeds by enabling continuous operations, significantly reducing the downtime usually seen with manual processes. Automated systems can function round the clock, maintaining a constant production speed, which is particularly beneficial in meeting high demand promptly and efficiently. The systems are designed to facilitate faster production turnarounds. By harmonizing the speed of all packaging processes, you achieve a more streamlined and faster production line, enhancing overall productivity and meeting market demands in a timely manner.

Beyond just speed, automation brings in the element of reliability in production. Businesses can anticipate a steady output without the unpredictability brought about by human labor — such as unexpected sick days or variations in individual worker pace. This results in a significant improvement in meeting delivery timelines, establishing a trustworthy reputation for the business. With continuous operations, there is also a reduced need for rushed processes, which often compromise the quality of the final product. This balance of speed and quality ensures a steady, reliable supply of superior products to the market.

2. Enhanced Precision and Consistency

Automation ensures a high degree of precision and consistency in liquid packaging. By utilizing sophisticated algorithms and sensors, these automated systems are capable of dispensing exact quantities of liquids into the packaging, maintaining a uniformity that is hard to achieve through manual efforts. This reduces wastage arising from overfilling or underfilling and ensures that products meet regulatory standards consistently. It also guarantees customer satisfaction as each product maintains a consistent quantity, establishing trust in the brand.

Additionally, precision in automation extends to the accurate labeling of products, a crucial factor in industries where a minor error can result in serious consequences, including legal repercussions. Automated labeling processes ensure each package contains the correct information, enhancing brand loyalty while avoiding costly recalls. This meticulous approach to packaging engenders a reputation for reliability and quality, as products maintain a standard of consistency that is trusted by consumers and meets the stringent demands of regulatory bodies.

3. Integration with Advanced Quality Control Systems

Automated packaging lines also benefit from integration with advanced quality control systems. These systems provide real-time monitoring and feedback, enabling a swift response to any deviations in product quality. It ensures that only products that meet the specified standards reach the consumers, safeguarding the brand's reputation and maintaining a high level of customer satisfaction. The data collected during the packaging process also offers insights into the production process, assisting in making informed decisions and optimizing operations over time.

Real-time quality control goes a step further in establishing a brand's reliability and commitment to quality. Automated systems can immediately identify and reject products that deviate from the set standards, ensuring a consistent quality in the products that reach the market. The integration allows for seamless product tracking and tracing, offering stakeholders a transparent view of the production process and enhancing compliance with regulatory requirements. It creates a system where quality is assured and verifiable. 

4. Reduction in Human Error

Automation significantly reduces the potential for human error, a common occurrence due to fatigue or oversight in manual operations. Automated systems follow programmed protocols meticulously, guaranteeing uniform output at all times. This not only prevents errors in the quantity of liquid packaged but also avoids mislabeling, ensuring that the right products reach the right consumers, therefore mitigating risks such as product recalls or damages to the brand reputation arising from inconsistent product quality.

The reduction of human error also means safer products for consumers. Issues such as contamination can be significantly reduced, ensuring that products maintain a high standard of hygiene. Automation also facilitates a rapid response to any issues identified, with systems often able to automatically rectify errors, reducing the downtime associated with stopping production lines to address issues manually. In a broader perspective, it creates an environment where quality and safety are paramount, providing products that consumers can count on.

5. What Is an IoT Device and What Is Its Impact on Manufacturing?

An IoT device is a physical device that connects to the Internet. These devices are all around you, and include pool heaters, fitness trackers, thermostats, appliances, locks, smart homes, and more! The Internet of Things is already very present in our lives and will introduce incredible opportunities over the next five years. For manufacturing, IoT devices can provide efficiency and real-time updates and insights. However, it’s essential to have customer confidence, and to do so, companies must ensure that their security and privacy protections are up-to-date and robust. Unfortunately, not all companies do so in a rush to get products on the market.

Without security norms and responsible practices, we’re reaching a crossroads where regulation may be required. Yet, in reality, legislation by itself will not be effective. Passing a law will take too long and will never keep pace with the evolving threat landscape. Companies will individually need to be proactive and increase the level of security for their IoT devices and related services to protect consumers and the privacy of their data going forward.

6. Scalability to Meet Production Demands

Automated liquid packaging systems have the distinct advantage of scalability, adapting easily to meet changing production demands. These systems can be scaled up to accommodate business growth or scaled down in low-demand periods without compromising the speed or accuracy of the packaging process. This ensures that manufacturers can respond swiftly to market demands, fostering business agility and maintaining a competitive edge in the market.

Scalability also allows for experimentation and innovation. Businesses can easily adapt their production lines to introduce new products to the market, testing them on a smaller scale before ramping up production if they are well-received. This flexibility encourages innovation, allowing businesses to rapidly respond to changing consumer preferences and trends. It also facilitates efficient resource management, as businesses can allocate resources more effectively based on the scaled production needs, optimizing costs and enhancing profitability.

7. Optimized Labor Allocation

Automation of repetitive and labor-intensive tasks in liquid packaging processes means employees can focus on more strategic, value-added activities. This not only leads to a more skilled and engaged workforce but also facilitates improved production strategies and fosters innovation. In addition, automating dangerous tasks can create a safer work environment, reducing the potential for accidents and enhancing worker well-being.

By freeing up human resources from repetitive tasks, businesses can nurture creativity and strategic thinking, fostering a culture of continuous improvement and innovation. It encourages employees to upskill, adding more value to the organization and building a team that can strategize for growth and efficiency. Optimized labor allocation also means a happier workforce, as employees find their roles more fulfilling, which can lead to increased job satisfaction and retention, creating a more harmonious and productive work environment.

8. Compliance with Regulatory Standards and Norms

In industries such as food, beverage, cannabis, pharmaceutical, chemical, and cosmetic, adherence to stringent regulatory standards is paramount. Automating liquid packaging facilitates easy compliance with such norms through precise control over the packaging process, ensuring that products meet the requisite safety and quality benchmarks.

For instance, in the pharmaceutical and food sectors, there is a necessity for exact measurements and stringent quality controls to ensure consumer safety. Automation enables integrating systems that can automatically maintain these exact measurements and control standards, reducing the risk of non-compliance. Automated systems can also be equipped to generate automatic reports and documentation required for regulatory compliance, thereby ensuring transparency and adherence to the required norms.

Conclusion

While automating liquid packaging addresses the pivotal needs of a variety of industries, it's important to understand that different industries have distinct requirements. There's no one-size-fits-all approach. A customized liquid packaging solution caters to specific industry needs, offering flexibility and optimizing the production process to meet unique demands effectively. Customization accommodates diverse requirements, offering a tailored approach that ensures optimal performance.

It seems like everything in our lives is now connected, from TVs to fridges to doorbells to fitness mirrors. Recent research found there are an average of 15 smart devices per household, up 25% from 2020, with “power users” having as many as 34. And these smart products are often a bad actor's prime target due to lax security. It only takes one vulnerability to become an entry point for threat actors to access the entire home network. In 2021 cyberattacks on IoT devices more than doubled from 2020.

With the surging volume of IoT products and cyberattacks, consumers are increasingly vulnerable to security breaches. Over the past few years, there has been a steady stream of security flaws, from hacked baby monitors with strangers spying or talking to kids to 600,000 GPS trackers manufactured in China and shipped globally with various vulnerabilities, including a default password of 123456. Making the situation worse: these devices were helping parents track their children.

Security as a Best-Effort or Worse, an Afterthought

A lot of time and money is invested in the features and functionality of smart products. However, in the rush to capitalize on consumer interest, security is often woefully neglected. The devices are vulnerable due to limited computing resources, lack of security features, and the reliance on internet connectivity. With many, the software is not updated frequently to address emerging threats like malware, or the product is secured with a default password.

These inadequate security practices put consumers in a risky situation as hackers can easily access the home network and carry out various nefarious activities, including spying on the house, spoofing the tracker’s location, intercepting emergency calls, or obtaining personal identification information to commit fraud.

Due to the lax policies, there are a range of common vulnerabilities spanning:

• Passwords: Password reuse coupled with weak or default credentials all make it fairly easy for bad actors to gain unauthorized access.
• Encryption: If data is not encrypted or it’s out of date when it’s transmitted between the IoT device and the network, it makes it easy to access without authorization.
• Patches: If manufacturers don’t regularly update and patch flaws, this leaves the solution at the mercy of hackers to exploit.
• Privacy: Some products collect more data than needed or share data without consent, which can lead to breaches.
• Apps: Many solutions, like smart thermostats, have accompanying apps to control and configure them. These can also have security flaws, such as poor or insecure data storage or authentication mechanisms.
• Firmware checks: Without these, attackers can modify firmware and potentially take control or steal sensitive data. For example, Bluetooth protocol vulnerabilities have been the source of several high-profile breaches involving IoT devices.
• Network protocols: Some communicate using weak or outdated network protocols that make it easy for bad actors to circumvent.
• Physical security: If an attacker gains access to the physical product, this can lead to data loss.

IoT devices must be tested to ensure vulnerabilities are found and fixed before they’re made available to purchase. It's clear that the status quo is not working. With consumers continuing to add smart products, industry regulators and government action is required to help address the seismic problem.

State of Regulation

Some progress has been made, including regulation passed in California to ensure that IoT manufacturers equip their products with some basic security features out of the box. In addition, the National Institute of Standards and Technology (NIST) laid out detailed recommendations for the labeling of consumer devices.

This has led to the White House announcing the Cyber Trust Mark IoT labeling program. To obtain the certificate, each consumer IoT device must pass a standardized set of security vulnerability tests that reflect the NIST recommendations for parameters like encryption and data protection. This is an important step to address cyber risks and build consumer confidence that they can trust intelligent products. In addition, the initiative provides a common framework for manufacturers to standardize and scale IoT security with defined tests to ensure each model meets the required benchmarks. The program is modeled on the Energy Star rating system for efficient household appliances.

With the label backed by a trusted set of security vulnerability tests, consumers can quickly and easily update the security on their IoT devices by scanning a QR code. This addresses a fatal flaw with many, the lack of software updates and patches, and it marks the first national cybersecurity specification to be introduced. In addition, it will provide visibility about the types of data the device collects and how it's used.

The Future is Connected

Connected solutions are reshaping the world and with the proliferation of IoT devices showing no sign of easing, cyber risks will continue to escalate. And as Anne Neuberger, Deputy National Security Advisor for Cyber and Emerging Technologies, stated, "The U.S. Cyber Trust Mark will give consumers a way to know if the smart devices they're purchasing are secure, and give companies a label to show their products meet cybersecurity standards. …..making our homes, classrooms, and workplaces safer and less vulnerable to cyberattacks."

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.

Low-power microcontrollers are a suitable choice for object detection in various scenarios where energy efficiency and resource constraints are important considerations. Here are some key situations where low-power controllers are particularly advantageous:

IoT and Battery-powered Devices: Low-power microcontrollers are ideal for IoT devices and battery-powered applications. Their efficient power management and optimized hardware allow for extended battery life, making them well-suited for energy-constrained environments. Object detection in such devices can operate continuously without draining the battery quickly.

Embedded Systems: In resource-constrained embedded systems, where limited processing power and memory are available, low-power microcontrollers excel. They provide a balance between computational capabilities and power consumption, making them capable of running object detection algorithms with minimal resources.

Real-time Requirements: Real-time object detection applications demand quick and accurate processing of incoming data. Low-power microcontrollers designed for real-time processing can handle time-sensitive tasks efficiently. They offer fast response times, minimizing latency and ensuring real-time decision-making.

Edge Computing: Low-power microcontrollers are well-suited for edge computing scenarios, where data processing occurs close to the data source. Object detection at the edge reduces the need for sending large amounts of data to a remote server for analysis, enabling faster and more efficient decision-making at the device level.

Cost-sensitive Deployments: Low-power microcontrollers are generally more affordable compared to high-end processors. They are a cost-effective solution for object detection in applications where budget constraints exist, making them accessible for a wide range of projects and deployments.

Harsh Environments: Low-power microcontrollers often have enhanced ruggedness and can withstand harsh operating conditions. This makes them suitable for object detection in environments with temperature variations, vibrations, or other challenging conditions.

Scalability and Distributed Systems: Low-power microcontrollers offer scalability, enabling distributed systems with multiple connected devices. Object detection can be performed at each device, allowing for parallel processing and distributed decision-making, which is beneficial in large-scale deployments.

By leveraging low-power microcontrollers for object detection, developers can achieve energy efficiency, cost savings, real-time capabilities, and scalability in a variety of IoT, embedded, and edge computing applications. Careful consideration of the project requirements, power constraints, and processing needs will help determine if low-power microcontrollers are the right choice for a specific object detection implementation.

Air quality monitoring has been increasingly important over the years. The use cases for monitoring air quality include both indoors and outdoors. Monitoring the air is also not just for human health, monitoring air quality in regards to temperature, humidly and more can be important for building maintenance, agriculture and any environment where the air affects it’s surroundings. Let’s walk through some of the core factors in smart air monitoring:

Accuracy: One of the most important factors of smart air quality monitoring is accuracy. It is important that the sensors used are able to detect even small changes in air quality. This means that the sensors need to be sensitive enough to detect even low levels of pollutants. Additionally, the sensors need to be reliable and consistent in their measurements.

Connectivity: Smart air quality monitoring systems need to be able to connect to the internet and transmit data in real-time. This is essential for providing up-to-date information about air quality to users. Additionally, it allows for the collection of large amounts of data, which can be used to identify trends and patterns in air quality.

Accessibility: Smart air quality monitoring systems need to be accessible to everyone, regardless of their technical ability. This means that they need to be easy to set up and use, with clear instructions provided. Additionally, they need to be affordable, so that they can be used by people on all income levels.

Integration: Smart air quality monitoring systems need to be able to integrate with other systems and devices. For example, they may need to be able to connect to smart home devices, such as thermostats, to automatically adjust settings based on air quality data. Additionally, they may need to integrate with public health systems to provide real-time data to medical professionals.

Battery Life: Smart air quality monitoring systems need to be able to operate for extended periods of time without needing to be recharged or replaced. This is especially important for outdoor sensors, which may be located in remote areas. Battery life can be extended by using low-power sensors and optimizing the power usage of the device. 

User Interface: Smart air quality monitoring systems need to have a user-friendly interface that allows users to quickly and easily access the information they need. This may include a mobile app or a web interface that displays air quality data in a clear and understandable format. Additionally, the interface should allow users to set up alerts when air quality reaches certain levels.

Data Visualization: Smart air quality monitoring systems need to be able to display data in a way that is easy to understand. This may include graphs, charts, and other visualizations that show trends over time. Additionally, the system should allow users to customize the way that data is displayed to best suit their needs.

Developers and engineers should consider these factors when planning and operating smart air quality monitoring systems for them to be effective.  

IoT forensic science uses technical methods to solve problems related to the investigation of incidents involving IoT devices. Some of the technical ways that IoT forensic science solves problems include:

1. Data Extraction and Analysis: IoT forensic science uses advanced software tools to extract data from IoT devices, such as logs, sensor readings, and network traffic. The data is then analyzed to identify relevant information, such as timestamps, geolocation, and device identifiers, which can be used to reconstruct events leading up to an incident.

2. Reverse Engineering: IoT forensic science uses reverse engineering techniques to understand the underlying functionality of IoT devices. This involves analyzing the hardware and software components of the device to identify vulnerabilities, backdoors, and other features that may be relevant to an investigation.

3. Forensic Imaging: IoT forensic science uses forensic imaging techniques to preserve the state of IoT devices and ensure that the data collected is admissible in court. This involves creating a complete copy of the device's storage and memory, which can then be analyzed without altering the original data.

4. Cryptography and Data Security: IoT forensic science uses cryptography and data security techniques to ensure the integrity and confidentiality of data collected from IoT devices. This includes the use of encryption, digital signatures, and other security measures to protect data during storage, analysis, and transmission.

5. Machine Learning: IoT forensic science uses machine learning algorithms to automate the analysis of large amounts of data generated by IoT devices. This can help investigators identify patterns and anomalies that may be relevant to an investigation.

IoT forensic science uses many more (and more advances) technical methods to solve problems related to the investigation of incidents involving IoT devices. By leveraging these techniques, investigators can collect, analyze, and present digital evidence from IoT devices that can be used to reconstruct events and support legal proceedings.

The Internet of Things (IoT) has transformed the way we interact with technology. With the rise of voice assistants such as Alexa, Siri, and Google Assistant, voice-enabled IoT applications have become increasingly popular in recent years. Voice-enabled IoT applications have the potential to revolutionize the way we interact with our homes, workplaces, and even our cars. In this article, we will explore the benefits and challenges of voice-enabled IoT applications and their potential for the future.

Voice-enabled IoT applications allow users to control various smart devices using their voice. These devices include smart speakers, smart TVs, smart thermostats, and smart lights, to name a few. By using voice commands, users can turn on the lights, adjust the temperature, play music, and even order food without having to touch any buttons or screens. This hands-free approach has made voice-enabled IoT applications popular among users of all ages, from children to seniors.

One of the significant benefits of voice-enabled IoT applications is their convenience. With voice commands, users can control their smart devices while they are doing other tasks, such as cooking, cleaning, or exercising. This allows for a more seamless and efficient experience, without having to interrupt the task at hand. Additionally, voice-enabled IoT applications can be customized to suit individual preferences, allowing for a more personalized experience.

Another significant benefit of voice-enabled IoT applications is their potential for accessibility. For people with disabilities, voice-enabled IoT applications can provide an easier and more natural way to interact with their devices. By using their voice, people with limited mobility or vision can control their devices without having to rely on buttons or screens. This can improve their quality of life and independence.

However, there are also challenges associated with voice-enabled IoT applications. One of the significant challenges is privacy and security. As voice-enabled IoT applications are always listening for voice commands, they can potentially record and store sensitive information. Therefore, it is crucial for developers to implement strong security measures to protect users' privacy and prevent unauthorized access.

Another challenge is the potential for misinterpretation of voice commands. Accidental triggers or misinterpretation of voice commands can result in unintended actions, which can be frustrating for users. Additionally, voice-enabled IoT applications can struggle to understand certain accents, dialects, or languages, which can limit their accessibility to non-native speakers.

Despite these challenges, the potential for voice-enabled IoT applications is vast. In addition to smart homes, voice-enabled IoT applications can be used in a wide range of industries, including healthcare, retail, and transportation. In healthcare, voice-enabled IoT applications can be used to monitor patients' health conditions and provide real-time feedback. In retail, voice-enabled IoT applications can provide personalized shopping experiences and assist with inventory management. In transportation, voice-enabled IoT applications can be used to provide real-time traffic updates and navigation.

In conclusion, voice-enabled IoT applications have become increasingly popular in recent years, providing a more convenient and accessible way for users to interact with their devices. While there are challenges associated with voice-enabled IoT applications, their potential for revolutionizing various industries is vast. As technology continues to evolve, the future of voice-enabled IoT applications is sure to be exciting and full of potential

Wearable devices, such as smartwatches, fitness trackers, and health monitors, have become increasingly popular in recent years. These devices are designed to be worn on the body and can measure various physiological parameters, such as heart rate, blood pressure, and body temperature. Wearable devices can also track physical activity, sleep patterns, and even detect falls and accidents.

Body sensor networks (BSNs) take the concept of wearables to the next level. BSNs consist of a network of wearable sensors that can communicate with each other and with other devices. BSNs can provide real-time monitoring of multiple physiological parameters, making them useful for a range of applications, including medical monitoring, sports performance monitoring, and military applications.

Smart portable devices, such as smartphones and tablets, are also an essential component of the IoT ecosystem. These devices are not worn on the body, but they are portable and connected to the internet, allowing for seamless communication and data transfer. Smart portable devices can be used for a wide range of applications, such as mobile health, mobile banking, and mobile commerce.

The development of wearables, BSNs, and smart portable devices requires a unique set of skills and expertise, including embedded engineering. Embedded engineers are responsible for designing and implementing the hardware and software components that make these devices possible. Embedded engineers must have a deep understanding of electronics, sensors, microcontrollers, and wireless communication protocols.

One of the significant challenges of developing wearables, BSNs, and smart portable devices is power consumption. These devices are designed to be small, lightweight, and portable, which means that they have limited battery capacity. Therefore, embedded engineers must design devices that can operate efficiently with minimal power consumption. This requires careful consideration of power management strategies, such as sleep modes and low-power communication protocols.

Another challenge of developing wearables, BSNs, and smart portable devices is data management. These devices generate large volumes of data that need to be collected, processed, and stored. The data generated by these devices can be highly sensitive and may need to be protected from unauthorized access. Therefore, embedded engineers must design devices that can perform efficient data processing and storage while providing robust security features.

The communication protocols used by wearables, BSNs, and smart portable devices also present a significant challenge for embedded engineers. These devices use wireless communication protocols, such as Bluetooth and Wi-Fi, to communicate with other devices and the internet. However, the communication range of these protocols is limited, which can make it challenging to establish and maintain reliable connections. Embedded engineers must design devices that can operate efficiently in environments with limited communication range and intermittent connectivity.

Finally, the user interface and user experience of wearables, BSNs, and smart portable devices are critical for their success. These devices must be easy to use and intuitive, with a user interface that is designed for small screens and limited input methods. Embedded engineers must work closely with user experience designers to ensure that the devices are user-friendly and provide a seamless user experience.

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 battery-powered microcontroller applications, energy savings are critical. Reduce battery charging times and replacement time by reducing current consumption. Microcontroller software design should follow these guidelines to reduce current consumption:

1、Using the appropriate energy model

Utilize low-energy peripherals

Close unused modules/peripherals

Disable clock to unused modules/peripherals

Reduce the clock frequenc

Operating voltage reduction

Optimize the code

2、Using the appropriate energy model

The most effective way to save energy is to spend as little time as possible in active mode.

Five custom energy modes allow the microcontroller to operate in an energy-optimal state at any given time.

3、Utilize low-energy peripherals

All peripherals are built on energy consumption and can be used in a variety of energy modes. Whenever possible, select the appropriate peripheral to let it work while the CPU is sleeping (or performing other tasks).

A few examples:

Use RTC and sleep instead of waiting for a certain loop

Transfer data between memory and U(S) using DMA

Monitor sensors with low energy sensor interface (LESENSE) instead of wake up and poll

4、Close unused modules/peripherals

There are modules/peripherals that are not in use at any given time for each microcontroller application. Turn these off and save energy. This also applies to the CPU itself. If the core is idle (for example, waiting for data reception), you can turn it off and save energy. This is one of the main features of the different EFM32 energy modes. Remember to consider start and stop conditions when disabling peripherals. For example, if it is completely turned off, the ADC needs some time to warm up before the conversion can be initiated. Similarly, USART simultaneous transmissions should be allowed on the progress. Thus, the receiver's shift register will not be in an indeterminate state.

5、Disable clocks to unused modules/peripherals

Even if a module/peripheral device is disabled (for example, TIMER0 stops), the various circuits in the module will still consume energy if its clock is running. Therefore, it is important to turn off the clocks of all unused modules.

6、Reduce clock frequency

Current is plotted at clock frequency. Generally speaking, a task or peripheral device should run at the lowest possible frequency.

For example, if a timer requests interruption every few milliseconds, it should be locked at several kHz instead of several MHz. This can be easily achieved by pre-scaling in CMU. Similarly, one way to choose CPU frequency is that it should be so low that the CPU will not be idle (some blanks should be added). However, in many cases, it is best to complete the current task quickly and then enter the appropriate energy model until new tasks have to be addressed.

7、Reduce working voltage

By reducing the working voltage, the energy consumption is further reduced. The Gecko series of microcontrollers can operate at low voltage.

There are absolute minimum values in the data table of each device

8、Optimization code

Optimizing code usually reduces energy consumption byincreasing the speed and efficiency of programs.A faster program spends less time in active mode, and in amore efficient program, each task executes fewer instructions. A simple way tooptimize code is to build it in release mode with the highest optimizationsettings rather than in debug mode.

9、Energy model

The EFR32 provides features that make it easier to configurelow-power peripherals and switch between energy modes. The EFR32 providesfeatures that make it easier to configure low-power peripherals and switchbetween energy modes.
Let's take a look at several modes

9.1 Operation mode (EM0)

This is the default mode. In this mode, the CPU fetches and executesinstructions from flash or RAM, all peripherals may be enabled, and theoperating power consumption is only 63 μA/MHz.

9.2 Sleep mode (EM1)

In sleep mode, the CPU's clock is disabled. All peripherals, as wellas RAM and flash memory, are available. Automated execution of multipleoperations can be achieved by using a Peripheral Reflection System (PRS) andDMA. For example, a timer can trigger an ADC conversion at regular intervals.When the conversion is complete, the result is moved to RAM by the DMA. When agiven number of conversions are performed, the DMA can request and interrupt towake up the CPU. Enter the sleep mode or the "Wait for Event (WFE)"instruction by executing "Wait for Interrupt (WFI)". Use the functionEMUILATEMEM1 () to enter sleep mode

9.3 Deep sleep mode (EM2)

In deep sleep mode, no high frequency oscillator is running, whichmeans only asynchronous and low frequency peripherals are available.This model further increases energy efficiency while still allowing arange of activities, including:

Low energy sensor interface(LESENSE) monitoring sensor,

LCD monitor drives LCD monitor,

LEUART that receives ortransmits one byte of data,

Perform address matching check.

RTC wakes up the CPU after theprogram is coded.

Analog Comparator (ACMP) tocompare voltage to programmed threshold

A GPIO to check the conversionon the I/O line.

The deep sleep mode isto first set the sleep depth in the system control register (SCR), and thenexecute the "Wait for Interrupt (WFI)" or "Wait for Event(WFE)" instruction. Use the function EMU_EnterEM2() to enter the deepsleep mode.

9.4 Stop mode (EM3)

The stop modediffers from the deep sleep mode in that no oscillator (except ULFRCO orAUXHFRCO) is running.
Modules/functions, if present on the device, canstill be used in stop mode when the appropriate clock source remains active:

I2C address

Supervision

GPIO interrupt

Pulse counter (fund)

Low energy timer (LETIMER)

Low energy sensor interface (LESENSE)

Real-time counter and calendar (RTCC)

Analog comparator (ACMP)

Voltage monitoring (VMON)

Ultra-low energy timer/counter(CRYOTIMER)

TemperatureSensor

Stop mode is the same as deep sleepmode, except that the low frequency oscillator must be manually disabled

9.5 Sleep mode (EM4H)

This feature is called EFM32'shibernate mode and wireless SoC Series 1, and is enabled using dedicatedcontrol register logic. Write the sequence 0x2, 0x3, 0x2, 0x3, 0x2, 0x3, 0x2,0x2, 0x2, 0x2, 0x2 to the EM4ENTRY bit field in the EMU_EM4CTRL register, andplace the device in hibernate mode when the EM4STATE bit is set; otherwise, Thedevice enters shutdown mode as usual. In sleep mode, most peripherals areturned off to reduce leakage power. There are some selected peripheralsavailable. System memory and registers do not retain values. The GPIO PADstatus and RTCC RAM are reserved. Wake up from EM4 sleep requires a reset tothe system and return to the EM0 activity. Sleep mode wake-up is possible, fromthe same shutdown mode to the power loop, nRESET, and the user-specified pinsource, as well as:

RTCC

CRYOTIMER

Measuretemperature outside the defined range (TEMPCHANGE)

9.6 Shutdown mode (EM4S)

The shutdown mode is the lowest energystate of the EFM32 Series 0, EFM32 or Wireless SoC Series 1 microcontroller.
The power is turned off to most devices, includinginternal RAM, and all clocks are disabled. Only recovery logic, if the GPIO padstatus is explicitly enabled, is retained. Wake up from off mode alwaysrequires a reset. When resetting from a RESETn pin or through one of a set ofdevice-specific pins explicitly enabled for this purpose, the current drawingin off mode can be as low as 20na. Some devices can replace pin-based wakeups;however, waking up from these sources requires a low-frequency oscillator toremain active, increasing the current attractiveness.

I remember when the Arduino Uno first came out circa 2005. Even though this 8-bit processor employs only a 16-MHz clock and offers only 32KB of Flash memory and 2KB of RAM, I still use these little rascals in a lot of my hobby projects to this day.

Of course, things have moved on since those days of yore. For example, one of the latest and greatest offerings from the folks at Arduino Pro is the Portenta X8.

Oh, my goodness gracious me! Have you seen this little beauty, which is described as being an “Industrial-grade, secure system-on-module (SOM) with outstanding computational density”?

What we are talking about here is something that’s only around the size of a stick of chewing gum (66.04 x 25.40 mm) while boasting nine processor cores and coming pre-loaded with the Linux operating system (OS).

According to the web, it has a Cortex-A53 quad-core up to 1.8GHz per core + a Cortex-M4 up to 400MHz, along with a dual-core Cortex-M7 up to 480Mhz + another Cortex-M4 up to 240MHz.

Either I’m losing the ability to count, or the above adds up to only eight cores. Where’s the missing core?

Well, I am in a great position to learn the answer to this conundrum, because I’m going to be hosting a webinar on this little scamp — Arduino Portenta X8: Superpower Your Linux Applications with Real-Time Execution — tomorrow as I pen these words.

During this webinar, which will be presented by IoT Central, I will be chatting with Andrea Richetta, who is the Head of Customer Success at Arduino Pro, and who will be introducing the Portenta X8 and answering all of our questions.

This 1-hour webinar will commence at 10:00am USA Central Time (so that’s 8:00am Pacific Time and 11:00am Eastern Time). And, speaking of time, now would be a great time to register before all of the good seats are taken.

I’ll be the one in the Hawaiian shirt. Dare I hope to see you there? Register here.

How Doews IoT help in Retail? Continuous and seamless communication is now a reality between people, processes and things.  IoT has been enabling retailers to connect with people and businesses and gain useful insight about product performance and engagement of people with such products. 

Importance of IoT in Retail

• It helps improve customer experience in new ways and helps brick and mortar shops compete with their online counterparts by engaging customers in different ways.
• IoT can track customer preferences, analyze their habits and share relevant information with the marketing teams and help improve the product or brand features and design and keep the customer updated on new products, delivery status etc.
• Using IoT retailers can increase efficiency and profitability in various ways for their benefit.
• IoT can significantly improve the overall customer experience, like automated checkouts and integration with messaging platforms and order systems.
• It helps increase efficiency in transportation and logistics by reducing the time to deliver goods to market or store. It helps in vehicle management, and tracking deliveries. This helps in reducing costs, improving the bottom line and increasing customer satisfaction.
• Inventory management becomes easier with IoT. Tracking inventory is much easier and simpler from the stocking of goods to initiating a purchase.
• It helps increase operational efficiency in warehouses, by optimizing temperature controls, improving maintenance, and managing the warehouse. 

Use Cases of IoT in Retail

1. IoT is used in Facility management to ensure day-to-day areas are clean and can be used to monitor consumable supplies levels. It can be used to monitor store environments like temperature, lighting, ventilation and refrigeration. IoT can identify key areas that can provide a complete 360 degrees view of facility management.
2. It can help in tracking the number of persons entering a facility. This is especially useful because of the pandemic situation, to ensure that no overcrowding takes place.
   Occupancy sensors provide vital data on store traffic patterns and also on the time spent in any particular area. This helps retailers with better planning and product placement strategies. This helps in guided selling with more effective display setups, layouts, and space management.
3. IoT helps in a big way for Supply chain and logistics, by providing information on the stock levels. 
4. IoT helps in asset tracking in items like shopping carts and baskets. Sensors can ensure that location data is available for all carts making retrieval easy. It can help lock carts if they are taken out of location.
5. IoT devices can and are being used to personalize user experience. Bluetooth beacons are used to send personalized real-time alerts to phones when the customer is near an aisle or a store. This can prompt a customer to enter the store or look at the aisle area and take advantage of offers etc. IoT-based beacons, helps Target, collect user data and also send hyper-personalized content to customers.
6. Smart shelves are another example of innovative IoT ideas. Maintaining shelves to refill products or ensure correct items are placed on the right shelves is a time-consuming task. Smart shelves automate these tasks easily. They can help save time and resolve manual errors.

Businesses should utilize new technologies to revolutionize the retail sector in a better way. Digitalization or digital transformation of brick and mortar stores is not a new concept. With every industry wanting to improve its services and facilities and trying to stay ahead of the competition, digitalization in retail industry is playing a big role in this transformation. To summarize, digitalization helps in enhanced data collection, helps data-driven customer insights, gives a better customer experience, and increases profits and productivity. It encourages a digital culture.

By Bee Hayes-Thakore

Google has been advancing their investment in how tamper-resistant secure hardware modules can protect not only Android and its functionality, but also protect third-party apps and secure sensitive transactions. The latest android smartphone device features enable tamper-resistant key storage for Android Apps using StrongBox. StrongBox is an implementation of the hardware-backed Keystore that resides in a hardware security module.

To accelerate adoption of new Android use cases with stronger security, Google announced the formation of the Android Ready SE Alliance. Secure Element (SE) vendors are joining hands with Google to create a set of open-source, validated, and ready-to-use SE Applets. On March 25th, Google launched the General Availability (GA) version of StrongBox for SE.

Hardware based security modules are becoming a mainstay of the mobile world. Juniper Research’s latest eSIM research, eSIMs: Sector Analysis, Emerging Opportunities & Market Forecasts 2021-2025, independently assessed eSIM adoption and demand in the consumer sector, industrial sector, and public sector, and predicts that the consumer sector will account for 94% of global eSIM installations by 2025. It anticipates that established adoption of eSIM frameworks from consumer device vendors such as Google, will accelerate the growth of eSIMs in consumer devices ahead of the industrial and public sectors.

Consumer sector will account for 94% of global eSIM installations by 2025

Juniper Research, 2021.

Expanding the secure architecture of trust to consumer wearables, smart TV and smart car

What’s more? A major development is that now this is not just for smartphones and tablets, but also applicable to WearOS, Android Auto Embedded and Android TV. These less traditional form factors have huge potential beyond being purely companion devices to smartphones or tablets. With the power, size and performance benefits offered by Kigen’s iSIM OS, OEMs and chipset vendors can consider the full scope of the vast Android ecosystem to deliver new services.

This means new secure services and innovations around:

🔐 Digital keys (car, home, office)

🛂 Mobile Driver’s License (mDL), National ID, ePassports

🏧 eMoney solutions (for example, Wallet)

How is Kigen supporting Google’s Android Ready SE Alliance?

The alliance was created to make discrete tamper resistant hardware backed security the lowest common denominator for the Android ecosystem. A major goal of this alliance is to enable a consistent, interoperable, and demonstrably secure applets across the Android ecosystem.

Kigen believes that enabling the broadest choice and interoperability is fundamental to the architecture of digital trust. Our secure, standards-compliant eSIM and iSIM OS, and secure personalization services are available to all chipset or device partners in the Android Ready SE Alliance to leverage the benefits of iSIM for customer-centric innovations for billions of Android users quickly.

Vincent Korstanje, CEO of Kigen

Kigen’s support for the Android Ready SE Alliance will allow our industry partners to easily leapfrog to the enhanced security and power efficiency benefits of iSIM technology or choose a seamless transition from embedded SIM so they can focus on their innovation.

We are delighted to partner with Kigen to further strengthen the security of Android through StrongBox via Secure Element (SE). We look forward to widespread adoption by our OEM partners and developers and the entire Android ecosystem.

Sudhi Herle, Director of Android Platform Security 

In the near term, the Google team is prioritizing and delivering the following Applets in conjunction with corresponding Android feature releases:

• Mobile driver’s license and Identity Credentials
• Digital car keys

Kigen brings the ability to bridge the physical embedded security hardware to a fully integrated form factor. Our Kigen standards-compliant eSIM OS (version 2.2. eUICC OS) is available to support chipsets and device makers now. This announcement is a start to what will bring a whole host of new and exciting trusted services offering better experience for users on Android.

Kigen’s eSIM (eUICC) OS brings

The smallest operating system, allowing OEMs to select compact, cost-effective hardware to run it on.

Kigen OS offers the highest level of logical security when employed on any SIM form factor, including a secure enclave.

On top of Kigen OS, we have a broad portfolio of Java Card™ Applets to support your needs for the Android SE Ready Alliance.

Kigen’s Integrated SIM or iSIM (iUICC) OS further this advantage

Integrated at the heart of the device and securely personalized, iSIM brings significant size and battery life benefits to cellular Iot devices. iSIM can act as a root of trust for payment, identity, and critical infrastructure applications

Kigen’s iSIM is flexible enough to support dual sim capability through a single profile or remote SIM provisioning mechanisms with the latter enabling out-of-the-box connectivity, secure and remote profile management.

For smartphones, set top boxes, android auto applications, auto car display, Chromecast or Google Assistant enabled devices, iSIM can offer significant benefits to incorporate Artificial intelligence at the edge.

Kigen’s secure personalization services to support fast adoption

SIM vendors have in-house capabilities for data generation but the eSIM and iSIM value chains redistribute many roles and responsibilities among new stakeholders for the personalization of operator credentials along different stages of production or over-the-air when devices are deployed.

Kigen can offer data generation as a service to vendors new to the ecosystem.

Partner with us to provide cellular chipset and module makers with the strongest security, performance for integrated SIM leading to accelerate these new use cases.

Security considerations for eSIM and iSIM enabled secure connected services

Designing a secure connected product requires considerable thought and planning and there really is no ‘one-size-fits-all’ solution. How security should be implemented draws upon a multitude of factors, including:

• What data is being stored or transmitted between the device and other connected apps?
• Are there regulatory requirements for the device? (i.e. PCI DSS, HIPAA, FDA, etc.)
• What are the hardware or design limitations that will affect security implementation?
• Will the devices be manufactured in a site accredited by all of the necessary industry bodies?
• What is the expected lifespan of the device?

End-to-end ecosystem and services thinking needs to be a design consideration from the very early stage especially when considering the strain on battery consumption in devices such as wearables, smart watches and fitness devices as well as portable devices that are part of the connected consumer vehicles.

Originally posted here.

by Carsten Gregersen

With how fast the IoT industry is growing, it’s paramount your business isn’t left behind.

IoT technology has brought a ton of benefits and makes systems more efficient and easier to manage. As a result, it’s no surprise that more businesses are adopting IoT solutions. On top of that, businesses starting new projects have the slight advantage of buying all new technology and, therefore, not having to deal with legacy systems. 

On the other hand, if you have an already operational legacy system and you want to implement IoT, you may think you have to buy entirely new technology to get it online, right? Not necessarily. After all, if your legacy systems are still functional and your staff is comfortable with them, why should you waste all of that time and money?

Legacy systems can still bend to your will and be used for adopting IoT. Sticking rather than twisting can help your business save money on your IoT project.

In this blog, we’ll go over the steps you would need to follow for integrating IoT technology into your legacy systems and the different options you have to get this done.

1. Analyze Your Current Systems

First things first, take a look at your current system and take note of their purpose, the way they work, the type of data that they collect, and the way they could benefit by communicating with each other.

This step is important because it will allow you to plan out IoT integration more efficiently. When analyzing your current systems make sure you focus on these key aspects:

• Automation – See how automation is currently accomplished and what other aspects should be automated.
• Efficiency – What aspects are routinely tedious or slow and could become more efficient?
• Data – How it’s taken, stored, and processed, and how it could be used better
• Money – Analyze how much some processes cost and keep them in mind to know what aspects could be done for cheaper with IoT
• Computing – The way data is processed, whether it be cloud, edge, or hybrid.

Following these steps will help you know your project in and out and apply IoT in the areas that truly matter.

2. Plan for IoT Integration

In order to integrate IoT into your legacy systems, you must get everything in order. 

In order to successfully integrate IoT into your system, you will need to have strong planning, design, and implementation phases. Steps you will need to follow in order to achieve this can be

• Decide what IoT hardware is going to be needed
• Set a budget taking software, hardware, and maintenance into account
• Decide on a communication protocol
• Develop software tools for interacting with the system
• Decide on a security strategy

This process can be daunting if you don’t know how IoT works, but by following the right tutorials and developing with the right tools, your IoT project can be easily realizable. 

Nabto has tools that can not only help you set up an IoT project but also adding legacy systems and newer IoT devices to it.

Here are several ways in which we can help get your legacy systems IoT ready. 

• You can integrate the Nabto SDK to add IoT remote control access to your devices.
• Use the Nabto application to move data from one network to another – otherwise known as TCP tunneling.
• Add secure remote access to your existing solutions. 
• Build mobile apps for remote control of embedded devices our IoT app solution.

3. Implement IoT Sensors to Existing Hardware

IoT has the capability to automize, control, and make systems more efficient. Therefore, interconnecting your legacy systems to allow for communication is a great idea.

There’s a high chance your legacy systems don’t currently have the ability to sense or communicate data. However, adding new IoT sensors can give them these capabilities.

IoT sensors are small devices that can detect when something changes. Then, they capture and send information to a main computer over the internet to be processed or execute commands. These could measure (but not limited to):

• Temperature
• Humidity
• Pressure
• Gyroscope
• Accelerometer

These sensors are cheap and easy to install, therefore, adding them to your existing legacy systems can be the simplest and quickest way to get to communicate over the internet.

Set up which inputs the sensor should respond to and under what conditions, and what it should do with the collected data. You could be surprised by the benefits that making a simple device to collect data can have for your project!

4. Connect Existing PLCs to the Internet

If you already have an automated system managed by a PLC (Programmable Logic Controller,) devices already share data with each other. Therefore, the next step is to get them online.

With access to the internet, these systems can be controlled remotely from anywhere in the world. Data can be accessed, modified, and analyzed more easily. On top of that, updates can be pushed globally at any time.

Given that some PLCs utilize proprietary protocols and have a weird way of making devices communicate with each other, an IoT gateway is the best way to take the PLC to the internet.

An IoT gateway is a device that acts as a bridge between IoT devices and the cloud, and allows for communication between them. This allows you to implement IoT to a PLC without having to restructure it or change it too much.

5. Connect Legacy using an IO port

A lot of times a legacy system has some kind of interface for data input/output. Sometimes, this is implemented for debugging when the product was developed. However, at other times, this is done to make it possible for service organizations to be able to interface with products in the field and to help customers with setup and/or debug problems.

These debug ports are similar to real serial ports, such as an RS-485 RS-232, etc. That being said, they can be more raw UART, SPI, or I2C. What’s more, the majority of the time the protocol on top of the serial connection is proprietary.

This kind of interface is great. It allows you a “black box” to be created via a physical interface matching the legacy system and firmware running on this black box. This can translate “internet” requests to the proprietary protocol of the legacy system. In addition,  this new system can be used as a design for newer internet-accessible versions of the system simply by adopting the black box onto the internal legacy design.

Bottom Line

Getting your legacy systems to work in IoT is not as much of a challenge as you might have initially thought.

Following some fairly simple strategies can let you set them up relatively quickly. However, don’t forget the planning phase for your IoT strategy and deciding how it’s going to be implemented in your own legacy system. This will allow you to streamline the process even more, and make you take full advantage of all the benefits that IoT brings to your project.

Originally posted here.

The head is surely the most complex group of organs in the human body, but also the most delicate. The assessment and prevention of risks in the workplace remains the first priority approach to avoid accidents or reduce the number of serious injuries to the head. This is why wearing a hard hat in an industrial working environment is often required by law and helps to avoid serious accidents.

This article will give you an overview of how to detect that the wearing of a helmet is well respected by all workers using a machine learning object detection model.

For this project, we have been using:

• Edge Impulse Studi to acquire some custom data, visualize the data, train the machine learning model and validate the inference results.
• Part of this public dataset from Roboflow, where the images containing the smallest bounding boxes has been removed.
• Part of the Flicker-Faces-HQ (FFHQ) (under Creative Commons BY 2.0 license) to rebalance the classes in our dataset.
• Google Colab to convert the Yolo v5 PyTorch format from the public dataset to Edge Impulse Ingestion format.
• A Rasberry Pi, NVIDIA Jetson Nano or with any Intel-based Macbooks to deploy the inference model.

Before we get started, here are some insights of the benefits / drawbacks of using a public dataset versus collecting your own. 

Using a public dataset is a nice-to-have to start developing your application quickly, validate your idea and check the first results. But we often get disappointed with the results when testing on your own data and in real conditions. As such, for very specific applications, you might spend much more time trying to tweak an open dataset rather than collecting your own. Also, remember to always make sure that the license suits your needs when using a dataset you found online.

On the other hand, collecting your own dataset can take a lot of time, it is a repetitive task and most of the time annoying. But, it gives the possibility to collect data that will be as close as possible to your real life application, with the same lighting conditions, the same camera or the same angle for example. Therefore, your accuracy in your real conditions will be much higher. 

Using only custom data can indeed work well in your environment but it might not give the same accuracy in another environment, thus generalization is harder.

The dataset which has been used for this project is a mix of open data, supplemented by custom data.

First iteration, using only the public datasets

At first, we tried to train our model only using a small portion of this public dataset: 176 items in the training set and 57 items in the test set where we took only images containing a bounding box bigger than 130 pixels, we will see later why. 

If you go through the public dataset, you can see that the entire dataset is strongly missing some “head” data samples. The dataset is therefore considered as imbalanced.

Several techniques exist to rebalance a dataset, here, we will add new images from Flicker-Faces-HQ (FFHQ). These images do not have bounding boxes but drawing them can be done easily in the Edge Impulse Studio. You can directly import them using the uploader portal. Once your data has been uploaded, just draw boxes around the heads and give it a label as below: 

Now that the dataset is more balanced, with both images and bounding boxes of hard hats and heads, we can create an impulse, which is a mix of digital signal processing (DSP) blocks and training blocks:

In this particular object detection use case, the DSP block will resize an image to fit the 320x320 pixels needed for the training block and extract meaningful features for the Neural Network. Although the extracted features don’t show a clear separation between the classes, we can start distinguishing some clusters:

To train the model, we selected the Object Detection training block, which fine tunes a pre-trained object detection model on your data. It gives a good performance even with relatively small image datasets. This object detection learning block relies on MobileNetV2 SSD FPN-Lite 320x320.    

According to Daniel Situnayake, co-author of the TinyML book and founding TinyML engineer at Edge Impulse, this model “works much better for larger objects—if the object takes up more space in the frame it’s more likely to be correctly classified.” This has been one of the reason why we got rid of the images containing the smallest bounding boxes in our import script.

After training the model, we obtained a 61.6% accuracy on the training set and 57% accuracy on the testing set. You also might note a huge accuracy difference between the quantized version and the float32 version. However, during the linux deployment, the default model uses the unoptimized version. We will then focus on the float32 version only in this article.

This accuracy is not satisfying, and it tends to have trouble detecting the right objects in real conditions:

Second iteration, adding custom data

On the second iteration of this project, we have gone through the process of collecting some of our own data. A very useful and handy way to collect some custom data is using our mobile phone. You can also perform this step with the same camera you will be using in your factory or your construction site, this will be even closer to the real condition and therefore work best with your use case. In our case, we have been using a white hard hat when collecting data. For example, if your company uses yellow ones, consider collecting your data with the same hard hats. 

Once the data has been acquired, go through the labeling process again and retrain your model. 

We obtain a model that is slightly more accurate when looking at the training performances. However, in real conditions, the model works far better than the previous one.

Finally, to deploy your model on yourA Rasberry Pi, NVIDIA Jetson Nano or your Intel-based Macbook, just follow the instructions provided in the links. The command line interface `edge-impulse-linux-runner` will create a lightweight web interface where you can see the results.

Note that the inference is run locally and you do not need any internet connection to detect your objects. Last but not least, the trained models and the inference SDK are open source. You can use it, modify it and integrate it to a broader application matching specifically to your needs such as stopping a machine when a head is detected for more than 10 seconds.

This project has been publicly released, feel free to have a look at it on Edge Impulse studio, clone the project and go through every steps to get a better understanding: https://studio.edgeimpulse.com/public/34898/latest

The essence of this use case is, Edge Impulse allows with very little effort to develop industry grade solutions in the health and safety context. Now this can be embedded in bigger industrial control and automation systems with a consistent and stringent focus on machine operations linked to H&S complaint measures. Pre-training models, which later can be easily retrained in the final industrial context as a step of “calibration,” makes this a customizable solution for your next project.

Originally posted on the Edge Impulse blog by Louis Moreau - User Success Engineer at Edge Impulse & Mihajlo Raljic - Sales EMEA at Edge Impulse