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In the vast terrain of digital transformation, the Internet of Things (IoT) has emerged as a leading beacon. As businesses grapple with evolving demands, IoT serves as a cornerstone for innovation, operational efficiency, and superior customer engagement. This article delves into the intricacies of IoT and elucidates how businesses can embrace it to spur growth.

Understanding the IoT Landscape

The Internet of Things comprises a vast network of interconnected physical devices, all embedded with sensors, software, and other technologies to collect and exchange data. From household items like smart thermostats to complex systems such as industrial machinery, IoT technology is versatile and can be applied in various sectors. It even plays a role in enhancing the robustness and capabilities of virtual networks, including VPS hosting services. By integrating IoT technology, businesses can create a more efficient, data-driven, and automated operation.

The Value Proposition for Businesses

The impact of IoT on business is profound. When appropriately harnessed, IoT can provide actionable data that serves as the foundation for informed decision-making. In a world that is shifting toward data-driven strategies, the real-time analytics that IoT offers can be transformative.

The technology also has the potential to elevate productivity. IoT can automate various mundane and repetitive tasks, freeing human resources for more creative and complex responsibilities. When it comes to customer experiences, IoT brings an unprecedented level of personalization and convenience, thereby boosting customer satisfaction and loyalty.

But the benefits don't stop there. Implementing IoT can lead to a more cost-efficient operation. One way it achieves this is by enabling predictive maintenance. This ensures that machinery and equipment are serviced before they break down, thus reducing downtime and extending the lifespan of the asset.

Implementing IoT in Your Business Strategy

As with any significant business undertaking, the effective implementation of IoT starts with the identification of specific business needs and objectives. Are you seeking operational efficiency or striving for superior customer engagement? Knowing what you aim to achieve helps you choose the right devices and platforms tailored to meet those objectives.

Choosing the right IoT devices is vital to the success of your venture. IoT has a broad spectrum of applications, and the range of devices available is equally diverse. Whether it's a smart camera to enhance security or a temperature sensor in a manufacturing line, selecting devices that suit your specific needs is critical.

The next stage involves integrating IoT technology into your existing infrastructure. Seamless integration is crucial to achieving a streamlined operation. Whether your business is purely online and reliant on VPS hosting, or you operate from a brick-and-mortar establishment, the IoT architecture should be compatible with your existing systems.

Security is another critical consideration. The interconnected nature of IoT increases the potential risk of cyber threats. As such, robust security measures are required to safeguard against unauthorized access and data breaches. You'll need to deploy strong encryption techniques and continually monitor the network to protect against vulnerabilities.

However, implementing IoT is not a "set it and forget it" deal. Continuous monitoring and data analysis are key to maximizing the benefits. IoT generates large volumes of data, and you need a comprehensive analytics strategy to sift through this data and extract actionable insights.

Finally, the system should undergo periodic evaluations for performance and security. These reviews help in iterating and optimizing your IoT setup, ensuring it evolves with changing business requirements and technological advancements.

Taking the Plunge

IoT technology offers an unmatched opportunity for businesses to elevate operational efficiency, enrich customer experiences, and drive growth. With a clear strategy in place— one that identifies your business needs incorporates the right devices, and follows a secure and data-driven approach— the benefits can be substantial.

Conclusion

In a rapidly digitizing world, IoT is not merely a fad but a transformative force that can help businesses stay competitive and reach new heights. Therefore, it's not a question of whether to adopt IoT but how best to do so for sustainable business growth.

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Welcome to the world of IoT, where devices connect and communicate like never before. Imagine your coffee machine reordering beans when it's low – that's IoT. Now, think about how this clever tech is transforming payments. From smart fridges to wearable gadgets, IoT is changing how we buy things. In this article, we'll explore the exciting ways IoT is making payment smoother and why businesses are embracing this change. Let's dive in and discover how our daily transactions are getting a high-tech upgrade.

But, What Are IoT Payments? 

Defining IoT Payments

IoT payments, which stand for Internet of Things payments, bring together regular things and money tasks. This means adding smart abilities to common stuff using sensors and communication. They can then do money tasks all on their own. Think about a fridge restocking itself or a car paying tolls automatically – it's like a whole new way of handling money stuff!

But, what gave rise to the need for IoT payments? What are the driving forces or trends driving this change?

Trends Driving IoT Payments

IoT payments are buzzing with exciting trends that are shaping how we handle transactions. Here are three key trends that stand out:

1. Smart Shopping

In the world of IoT payments, shopping gets a smart upgrade. Imagine walking into a store, grabbing what you need, and just leaving – no lines, no checkouts. Smart sensors track what you've taken, and your payment is automatically done through your device. It's like magic shopping!

2. Connected Convenience

IoT payments are all about convenience. Your devices talk to each other, making payments seamless. Your phone talks to your car and parking fees are settled. Your wearable device pays for your morning coffee. Everything works together to make life easier.

3. Enhanced Security

Security is a big deal in IoT payments. Devices communicate sensitive information, so strong security measures are a must. Biometrics like fingerprints or facial recognition adds an extra layer of protection. With IoT payments, your data stays safe while you enjoy hassle-free transactions.

These trends show how IoT payments are transforming the way we handle shopping smarter, life more convenient, and transactions more secure.

Benefits of IoT-Based Payments: Transforming the Way We Transact

The world of IoT payments brings a bunch of great benefits that touch many parts of life. Let's look at three ways it's making things better for everyone:

Elevated Convenience and Efficiency for Consumers

IoT payments make life super convenient for us. We don't need to carry cash or cards – everything happens fast and smoothly. Think about paying with a simple wave of your device. It's like having your own virtual wallet!

Optimized Operations for Businesses

Businesses also get a boost from IoT payments. It helps them run things better. Payments become easy, so there's less hassle. That means less time dealing with money matters and more time for making customers happy.

Data-Driven Insights for Industries

IoT payments create big data that industries can use. This helps them figure out what's working and what's not. It's like a roadmap to improve things. When industries know what people like, they can make things even better for all of us.

So, IoT payments bring good things for consumers, businesses, and entire industries. It's like a win-win-win!

Navigating Challenges: Ensuring a Secure IoT Payment Ecosystem

Even though IoT payments have a lot of potential, there's a big challenge we need to tackle – security. Let's take a look at what's going on:

Addressing Vulnerabilities with Robust Solutions

When things are all connected, they can be vulnerable. This means there's a risk of bad people trying to mess with our money or information. To stop this, we need strong protection. Things like powerful encryption (a kind of code) and super strong ways to prove who we are can help. Wondering how to protect your data and transactions even more? Learning how to make a fintech app can give you insights into adding these protective layers. Plus, regular updates to the software that runs these devices can fix any weaknesses and keep us safe.

Preventing Unauthorized Access

One big worry is someone sneaking in where they shouldn't be. To stop this, we need to make sure only the right people can access these connected devices. It's like having a secret code only we know.

Building Trust for the Future

Security is a big deal for IoT payments to work well. If we can make sure everything is super secure, we can trust this tech more and use it for all kinds of things.

So, while IoT payments are exciting, we need to make sure they're super safe too. It's like locking the door to our digital money world!

Proposed Solutions: Safeguarding the Future of Transactions

Making sure IoT payments are safe needs a smart plan. Let's see how we can do that:

Integrating Security into Design

People who make smart things need to build safety right into them. They should use secret codes (encryption) to lock everything and keep fixing any problems (updates) all the time.

Using Clever Tools to Stop Bad Things

Companies that handle payments need to use smart tools to catch bad guys trying to trick the system. These tools can quickly find problems and stop them.

Helping People Trust and Feel Safe

If we know how things are kept safe, we'll trust them more. Companies should tell us how they're protecting our money and info. When we feel safe, we'll use it more.

So, if we work together, we can make sure IoT payments are safe and easy. It's like building a strong shield to keep our digital world super secure!

Business Use Cases: How Different Businesses Can Use IoT Payments For Their Advantage?

The spectrum of applications for IoT payments stretches across industries, offering an array of compelling business use cases.

Revolutionizing Retail with Smart Checkouts

Retailers can significantly enhance customer experiences through the deployment of smart checkout systems, empowering customers to shop and exit without conventional point-of-sale interactions.

Optimizing Logistics and Supply Chains

For logistics companies, the integration of IoT payments holds the potential to optimize supply chains by facilitating automated transactions between distribution centers and delivery vehicles, reducing operational intricacies.

Smart Cities and Enhanced Services

On the frontiers of smart cities, IoT payments can streamline utility payments and public service transactions, amplifying administrative efficiency and elevating citizen satisfaction.

Need for IoT-Based Payments in Business

Seizing the Competitive Edge

For businesses aiming to thrive in the digital era, embracing IoT payments is not just a choice – it's an imperative strategy.

Magnetizing Tech-Savvy Consumers

The allure of seamless, frictionless transactions magnetizes tech-savvy consumers, who prioritize convenience in their interactions.

Unlocking Efficiency and Uncharted Revenue Streams

Besides attracting customers, IoT payments also bring new ways to make money and work better. This helps a business become even stronger in the market.

Conclusion: Paving the Way for Tomorrow's Transactions

In the realm of commerce, a transformative era dawns with IoT payments reshaping how we interact financially. Challenges remain, but finding the right balance between convenience and security will steer the course of IoT payment adoption. As we stand on the brink of tomorrow's transactions, the promise of seamless, connected payments offers a glimpse into a future where transactions are effortless and secure, forever changing the way we engage with money.

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Voice-Enabled IoT Applications

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.

Free vector users buying smart speaker applications online. smart assistant applications online store, voice activated digital assistants apps market concept. vector isolated illustration.
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

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Wearable technology: role in respiratory health and disease | European  Respiratory Society

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.

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Wireless Sensor Networks and IoT

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

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Talking about Internet of Things Security

Some say that if World War III breaks out, it will be fought in cyberspace. As IoT systems gather more and more under the “umbrella” of the network, security has never been more important to various user groups. From the traffic lights that play an important role in our urban traffic to the power system that provides energy for them, to the management and monitoring systems that keep cars running well, security in the use of networks and devices has become the basis and basis of modern communication devices and systems. necessary condition. Providing solid security in the online world is no easy task. Security is one of the very few scientific and technological means that must be confronted with external forces to achieve overdue results. What is more complicated is that these external forces can break through the defense line time after time through traditional and innovative means. Because of the many potential attack methods, information and network security has become an attractive and challenging topic, which is closely related to enterprises, industries and life.


For decades, the information technology (IT) environment has been very active and the hardest hit area for attacks and threats, which has also allowed IT to grow rapidly. In contrast, the operational technology (OT) environment is relatively traditional and closed, and the connection methods and channels between devices and the network are very limited. Therefore, compared with IT, OT records relatively fewer attack events, but its learning opportunities Countermeasures are also relatively scarce. But security in the OT world tends to have a broader security scope than IT. For example, in OT, security is almost equivalent to safety. In fact, the connected security standards of IIoT also incorporate the safety of equipment and people. This installment will focus on common challenges facing OT security.


The erosion problem of network architecture. The main issues facing the protection of industrial environments are initial design and ongoing maintenance. The original design concept stems from a premise that the network itself is safe, because it is isolated on the physical level of the enterprise, with little or no connection with the external environment, and the attacker lacks sufficient correlation knowledge to perform security attacks. In the vast majority of cases, the initial network design is sound, even good practice and relative standards. But in fact, from the point of view of security design, it is better to cope with the growing demand than to conceal the lack of communication and improve the response. It is relatively common that, over time, an otherwise hidden problem may be exposed by temporary updates and cracks to the hardware, allowing the problem to go unchecked and spread across the entire device family leading to a complete network and system crash Case.


Pervasive system legacy issues. In an industrial environment, the span of new and old equipment is large, the equipment life cycle is long, and the operating system of the equipment is not uniform enough, which makes the maintenance of the equipment extremely troublesome, and also exposes security issues such as system vulnerabilities. For example, in the context of urban power systems, it is not uncommon for older mechanical equipment to intersect with modern smart electronics. For the legacy components, because the old equipment cannot be connected to the network, the equipment is encouraged to run, but the entire system is integrated into the network, and some conditions cannot be monitored. From a security point of view, this situation is a potential threat, because many devices are likely to be unpatched or have vulnerabilities due to legacy issues, and it is more likely that the corresponding solutions for devices that are aging due to the passage of time cannot be applied. Therefore, we should strengthen the management of patches and devices, generate corresponding tools, and protect the vulnerabilities that may be exploited as much as possible.


Unsafe operating protocol. Among industrial control protocols, especially those based on serial ports, they are only considered for communication at the beginning of design, and there is no relative requirement for security. This has become the weakness and inherent loophole of the current network transmission protocol. In addition, the security considerations in the embedded operating system are relatively lacking. In old industrial protocols, data protocols such as monitoring and data acquisition often have coexisting security issues. Including the lack of communication authentication, static and dynamic data cannot establish effective protection, which makes the data in transmission often public. Although the data may not be so important, the risk of data tampering must be prevented.


The device is not secure. In addition to the defects of the communication protocol, the control equipment and the communication components themselves also have loopholes and defects. Before 2010, the world paid little attention to the security of industrial design, which also led to the fact that industrial design did not undergo the fire-zero test like IT, which led to frequent occurrence of vulnerability-related problems in the industry. This also reminds the OT industry to pay attention to the safety of the equipment itself.
IoT security issues are often more than that, including supplier dependence, security knowledge presentation and demonstration issues, etc. All these aspects remind the importance of safety all the time.

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The serial server processes various data, maps the Ethernet interface of the TCP/IP protocol into a standard serial port, processes the serial data flow of the serial device, converts the format, and controls sending and receiving, making it a data frame that can be transmitted by the Ethernet, and judging the Ethernet Data frame, converted into a serial data transfer response to the serial device.
The system configures relevant network audio and video interface devices at each node to ensure the connection of audio and video signals. At the same time, each interface machine has a programmable control interface (RS-232, RS-485, IR infrared) to control the surrounding equipment. The control of each module can work independently, which means that the control failure of a module during the period will not affect the operation of the entire system, only the faulty node needs to be dealt with, which ensures the stable operation of the entire system and facilitates system maintenance.
Find the existing problems in time and eliminate them immediately. The system not only displays the corresponding parameters through the display screen. It should also communicate data through its own communication interface (RS-232 or RS-485 serial port). The original substations are generally managed by local computers without networking. Using RS serial port communication can meet the requirements. Now due to the gradual development of industrial Ethernet on the national grid.

Ebyte Internet of Things wireless communication transmission equipment-Ethernet serial port server

NA11x 【Serial ⇌ Ethernet】 Serial server
Network protocol: IP, TCP/UDP, HTTPD, IPV4
Transmission method: RS485/RJ45
Product size: 92*66*30mm
Product introduction: NA111/NA112 is a serial server that realizes serial data ⇌ Ethernet data transmission. The product comes with an RJ45 interface and is installed on a guide rail. It can meet the networking functions of various serial devices/PLC.

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According to a 2021 IBM report, the average organization did not detect a data breach for up to 212 days—and then did not fully contain the issue for another 75. In many instances, malicious hackers attack a company using publicly available information: open-source intelligence often referred to as OSINT.

However, pentration testers can also use OSINT to protect organizations. This guide discusses what OSINT is and explains how penetration testers can apply OSINT tools and frameworks to improve an organization’s security.

What Is Open-Source Intelligence, and How Is It Used?

What is OSINT (Open Source Intelligence)? - sunnyvalley.io

Cyberattackers usually start by profiling the organization or individual they’re looking to attack. Attackers can use publicly available data on the internet to locate exploitable targets with the objective of collecting as much data as possible about the individual or organization. Likewise, ethical hackers and penetration testers can use OSINT to identify a company’s vulnerabilities so that they can be fixed before malicious actors find them.

OSINT is raw data that is openly available to the public. It may include information like names, addresses, interests, and other personal details. Location and behavioral data, affiliations, and daily patterns are all important pieces of information that can provide an inside look into a target’s life.

Social Media Intelligence (SOCMINT)

Social media intelligence, known as SOCMINT, is a subcategory of OSINT. SOCMINT refers to publicly available information on social media websites.

One aspect of an OSINT-based penetration testing framework is the use of social media for reconnaissance. Most employees have social media accounts, which can give hackers access to a wealth of sensitive information. Penetration testing with SOCMINT can locate information such as:

  • Social media posts, messages, and images
  • Person-to-person communications
  • Group-to-group communications

How Do Penetration Testers Find Information?

Cybersecurity professionals perform penetration testing using OSINT as a proactive measure to protect organizations. Using publicly available information, the tester can determine which areas are open to exploits. Once they have this data, they can then implement appropriate measures to prevent an attack.

Penetration testers gather OSINT in various ways. One method is to manually view content posted in specific groups or on certain pages. Another approach is to review results from searches the tester has performed. Testers may also find information by extracting data from websites using web scraping tools.

A variety of tools have been developed to automate tasks for penetration testers, improving efficiency compared with manual testing. These automated testing tools can also be used to find items that manual testing doesn’t identify. Penetration testers have many OSINT tools available to collect information (Nordine, 2017).

  • Google dorks. One of the most popular OSINT tools is Google dorks. Google dorking is the technique of using Google search operators to find sensitive information and vulnerabilities.
  • Metagoofil is an OSINT tool that scans Google and Bing for email addresses. Testers use it to find people’s contact information, collect it in a list, and save that list as a CSV file.
  • Recon-ng is a framework to automate intelligence gathering that supports several data sources. Recon-ng provides five data sources: Google, Bing, Maltego CE, ShodanHQ, and Dnsdumpster.
  • SpiderFoot scans over 100 data sources to locate information about a target. The tool can find information such as IP addresses, domain names, and emails.

Why Pursue a Career in Penetration Testing?

Recent data from cybersecurity ventures  indicates that cybersecurity professionals are in high demand, with the cybersecurity job market expected to grow 350% by 2025 (Morgan, 2022). If you’re interested in obtaining one of these in-demand positions, consider getting certified with EC-Council as a Certified Penetration Testing Professional (C|PENT).

In the comprehensive, hands-on C|PENT program, you’ll take a deep dive into how to use OSINT in penetration testing. You’ll also cover many of the other most-desired skills for penetration testers today, including:

  • How to penetration test Internet of Things (IoT) devices
  • How to use social engineering tactics in a penetration test
  • How to conduct penetration testing in the cloud

With the C|PENT certification, you’ll have a proven record of your expertise in the tools and techniques used in this rewarding field.

 
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By Bee Hayes-Thakore

The Android Ready SE Alliance, announced by Google on March 25th, paves the path for tamper resistant hardware backed security services. Kigen is bringing the first secure iSIM OS, along with our GSMA certified eSIM OS and personalization services to support fast adoption of emerging security services across smartphones, tablets, WearOS, Android Auto Embedded and Android TV.

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.

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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

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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

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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.

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In my last post, I explored how OTA updates are typically performed using Amazon Web Services and FreeRTOS. OTA updates are critically important to developers with connected devices. In today’s post, we are going to explore several best practices developers should keep in mind with implementing their OTA solution. Most of these will be generic although I will point out a few AWS specific best practices.

Best Practice #1 – Name your S3 bucket with afr-ota

There is a little trick with creating S3 buckets that I was completely oblivious to for a long time. Thankfully when I checked in with some colleagues about it, they also had not been aware of it so I’m not sure how long this has been supported but it can help an embedded developer from having to wade through too many AWS policies and simplify the process a little bit.

Anyone who has attempted to create an OTA Update with AWS and FreeRTOS knows that you have to setup several permissions to allow an OTA Update Job to access the S3 bucket. Well if you name your S3 bucket so that it begins with “afr-ota”, then the S3 bucket will automatically have the AWS managed policy AmazonFreeRTOSOTAUpdate attached to it. (See Create an OTA Update service role for more details). It’s a small help, but a good best practice worth knowing.

Best Practice #2 – Encrypt your firmware updates

Embedded software must be one of the most expensive things to develop that mankind has ever invented! It’s time consuming to create and test and can consume a large percentage of the development budget. Software though also drives most features in a product and can dramatically different a product. That software is intellectual property that is worth protecting through encryption.

Encrypting a firmware image provides several benefits. First, it can convert your firmware binary into a form that seems random or meaningless. This is desired because a developer shouldn’t want their binary image to be easily studied, investigated or reverse engineered. This makes it harder for someone to steal intellectual property and more difficult to understand for someone who may be interested in attacking the system. Second, encrypting the image means that the sender must have a key or credential of some sort that matches the device that will decrypt the image. This can be looked at a simple source for helping to authenticate the source, although more should be done than just encryption to fully authenticate and verify integrity such as signing the image.

Best Practice #3 – Do not support firmware rollbacks

There is often a debate as to whether firmware rollbacks should be supported in a system or not. My recommendation for a best practice is that firmware rollbacks be disabled. The argument for rollbacks is often that if something goes wrong with a firmware update then the user can rollback to an older version that was working. This seems like a good idea at first, but it can be a vulnerability source in a system. For example, let’s say that version 1.7 had a bug in the system that allowed remote attackers to access the system. A new firmware version, 1.8, fixes this flaw. A customer updates their firmware to version 1.8, but an attacker knows that if they can force the system back to 1.7, they can own the system. Firmware rollbacks seem like a convenient and good idea, in fact I’m sure in the past I used to recommend them as a best practice. However, in today’s connected world where we perform OTA updates, firmware rollbacks are a vulnerability so disable them to protect your users.

Best Practice #4 – Secure your bootloader

Updating firmware Over-the-Air requires several components to ensure that it is done securely and successfully. Often the focus is on getting the new image to the device and getting it decrypted. However, just like in traditional firmware updates, the bootloader is still a critical piece to the update process and in OTA updates, the bootloader can’t just be your traditional flavor but must be secure.

There are quite a few methods that can be used with the onboard bootloader, but no matter the method used, the bootloader must be secure. Secure bootloaders need to be capable of verifying the authenticity and integrity of the firmware before it is ever loaded. Some systems will use the application code to verify and install the firmware into a new application slot while others fully rely on the bootloader. In either case, the secure bootloader needs to be able to verify the authenticity and integrity of the firmware prior to accepting the new firmware image.

It’s also a good idea to ensure that the bootloader is built into a chain of trust and cannot be easily modified or updated. The secure bootloader is a critical component in a chain-of-trust that is necessary to keep a system secure.

Best Practice #5 – Build a Chain-of-Trust

A chain-of-trust is a sequence of events that occur while booting the device that ensures each link in the chain is trusted software. For example, I’ve been working with the Cypress PSoC 64 secure MCU’s recently and these parts come shipped from the factory with a hardware-based root-of-trust to authenticate that the MCU came from a secure source. That Root-of-Trust (RoT) is then transferred to a developer, who programs a secure bootloader and security policies onto the device. During the boot sequence, the RoT verifying the integrity and authenticity of the bootloader, which then verifies the integrity and authenticity of any second stage bootloader or software which then verifies the authenticity and integrity of the application. The application then verifies the authenticity and integrity of its data, keys, operational parameters and so on.

This sequence creates a Chain-Of-Trust which is needed and used by firmware OTA updates. When the new firmware request is made, the application must decrypt the image and verify that authenticity and integrity of the new firmware is intact. That new firmware can then only be used if the Chain-Of-Trust can successfully make its way through each link in the chain. The bottom line, a developer and the end user know that when the system boots successfully that the new firmware is legitimate. 

Conclusions

OTA updates are a critical infrastructure component to nearly every embedded IoT device. Sure, there are systems out there that once deployed will never update, however, those are probably a small percentage of systems. OTA updates are the go-to mechanism to update firmware in the field. We’ve examined several best practices that developers and companies should consider when they start to design their connected systems. In fact, the bonus best practice for today is that if you are building a connected device, make sure you explore your OTA update solution sooner rather than later. Otherwise, you may find that building that Chain-Of-Trust necessary in today’s deployments will be far more expensive and time consuming to implement.

Originally posted here.

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4 key questions to ask tech vendors

Posted by Terri Hiskey

Without mindful and strategic investments, a company’s supply chain could become wedged in its own proverbial Suez Canal, ground to a halt by outside forces and its inflexible, complex systems.

 

It’s a dramatic image, but one that became reality for many companies in the last year. Supply chain failures aren’t typically such high-profile events as the Suez Canal blockage, but rather death by a thousand inefficiencies, each slowing business operations and affecting the customer experience.

Delay by delay and spreadsheet by spreadsheet, companies are at risk of falling behind more nimble, cloud-enabled competitors. And as we emerge from the pandemic with a new understanding of how important adaptable, integrated supply chains are, company leaders have critical choices to make.

The Hannover Messe conference (held online from April 12-16) gives manufacturing and supply chain executives around the world a chance to hear perspectives from industry leaders and explore the latest manufacturing and supply chain technologies available.

Technology holds great promise. But if executives don’t ask key strategic questions to supply chain software vendors, they could unknowingly introduce a range of operational and strategic obstacles into their company’s future.

If you’re attending Hannover Messe, here are a few critical questions to ask:

Are advanced technologies like machine learning, IoT, and blockchain integrated into your supply chain applications and business processes, or are they addressed separately?

It’s important to go beyond the marketing. Is the vendor actually promoting pilots of advanced technologies that are simply customized use cases for small parts of an overall business process hosted on a separate platform? If so, it may be up to your company to figure out how to integrate it with the rest of that vendor’s applications and to maintain those integrations.

To avoid this situation, seek solutions that have been purpose-built to leverage advanced technologies across use cases that address the problems you hope to solve. It’s also critical that these solutions come with built-in connections to ensure easy integration across your enterprise and to third party applications.

Are your applications or solutions written specifically for the cloud?

If a vendor’s solution for a key process (like integrated business planning or plan to produce, for example) includes applications developed over time by a range of internal development teams, partners, and acquired companies, what you’re likely to end up with is a range of disjointed applications and processes with varying user interfaces and no common data model. Look for a cloud solution that helps connect and streamline your business processes seamlessly.

Update schedules for the various applications could also be disjointed and complicated, so customers can be tempted to skip updates. But some upgrades may be forced, causing disruption in key areas of your business at various times.

And if some of the applications in the solution were written for the on-premises world, business processes will likely need customization, making them hard-wired and inflexible. The convenience of cloud solutions is that they can take frequent updates more easily, resulting in greater value driven by the latest innovations.

Are your supply chain applications fully integrated—and can they be integrated with other key applications like ERP or CX?

A lack of integration between and among applications within the supply chain and beyond means that end users don’t have visibility into the company’s operations—and that directly affects the quality and speed of business decisions. When market disruptions or new opportunities occur, unintegrated systems make it harder to shift operations—or even come to an agreement on what shift should happen.

And because many key business processes span multiple areas—like manufacturing forecast to plan, order to cash, and procure to pay—integration also increases efficiency. If applications are not integrated across these entire processes, business users resort to pulling data from the various systems and then often spend time debating whose data is right.

Of course, all of these issues increase operational costs and make it harder for a company to adapt to change. They also keep the IT department busy with maintenance tasks rather than focusing on more strategic projects.

Do you rely heavily on partners to deliver functionality in your supply chain solutions?

Ask for clarity on which products within the solution belong to the vendor and which were developed by partners. Is there a single SLA for the entire solution? Will the two organizations’ development teams work together on a roadmap that aligns the technologies? Will their priority be on making a better solution together or on enhancements to their own technology? Will they focus on enabling data to flow easily across the supply chain solution, as well as to other systems like ERP? Will they be able to overcome technical issues that arise and streamline customer support?

It’s critical for supply chain decision-makers to gain insight into these crucial questions. If the vendor is unable to meet these foundational needs, the customer will face constant obstacles in their supply chain operations.

Originally posted here.

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By Ricardo Buranello

What Is the Concept of a Virtual Factory?

For a decade, the first Friday in October has been designated as National Manufacturing Day. This day begins a month-long events schedule at manufacturing companies nationwide to attract talent to modern manufacturing careers.

For some period, manufacturing went out of fashion. Young tech talents preferred software and financial services career opportunities. This preference has changed in recent years. The advent of digital technologies and robotization brought some glamour back.

The connected factory is democratizing another innovation — the virtual factory. Without critical asset connection at the IoT edge, the virtual factory couldn’t have been realized by anything other than brand-new factories and technology implementations.

There are technologies that enable decades-old assets to communicate. Such technologies allow us to join machine data with physical environment and operational conditions data. Benefits of virtual factory technologies like digital twin are within reach for greenfield and legacy implementations.

Digital twin technologies can be used for predictive maintenance and scenario planning analysis. At its core, the digital twin is about access to real-time operational data to predict and manage the asset’s life cycle. It leverages relevant life cycle management information inside and outside the factory. The possibilities of bringing various data types together for advanced analysis are promising.

I used to see a distinction between IoT-enabled greenfield technology in new factories and legacy technology in older ones. Data flowed seamlessly from IoT-enabled machines to enterprise systems or the cloud for advanced analytics in new factories’ connected assets. In older factories, while data wanted to move to the enterprise systems or the cloud, it hit countless walls. Innovative factories were creating IoT technologies in proof of concepts (POCs) on legacy equipment, but this wasn’t the norm.

No matter the age of the factory or equipment, everything looks alike. When manufacturing companies invest in machines, the expectation is this asset will be used for a decade or more. We had to invent something inclusive to new and legacy machines and systems.

We had to create something to allow decades-old equipment from diverse brands and types (PLCs, CNCs, robots, etc.) to communicate with one another. We had to think in terms of how to make legacy machines to talk to legacy systems. Connecting was not enough. We had to make it accessible for experienced developers and technicians not specialized in systems integration.

If plant managers and leaders have clear and consumable data, they can use it for analysis and measurement. Surfacing and routing data has enabled innovative use cases in processes controlled by aged equipment. Prescriptive and predictive maintenance reduce downtime and allow access to data. This access enables remote operation and improved safety on the plant floor. Each line flows better, improving supply chain orchestration and worker productivity.

Open protocols aren’t optimized for connecting to each machine. You need tools and optimized drivers to connect to the machines, cut latency time and get the data to where it needs to be in the appropriate format to save costs. These tools include:

  • Machine data collection
  • Data transformation and visualization
  • Device management
  • Edge logic
  • Embedded security
  • Enterprise integration
This digital copy of the entire factory floor brings more promise for improving productivity, quality, downtime, throughput and lending access to more data and visibility. It enables factories to make small changes in the way machines and processes operate to achieve improvements.

Plants are trying to get and use data to improve overall equipment effectiveness. OEE applications can calculate how many good and bad parts were produced compared to the machine’s capacity. This analysis can go much deeper. Factories can visualize how the machine works down to sub-processes. They can synchronize each movement to the millisecond and change timing to increase operational efficiency.

The technology is here. It is mature. It’s no longer a question of whether you want to use it — you have it to get to what’s next. I think this makes it a fascinating time for smart manufacturing.

Originally posted here.

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By Tony Pisani

For midstream oil and gas operators, data flow can be as important as product flow. The operator’s job is to safely move oil and natural gas from its extraction point (upstream), to where it’s converted to fuels (midstream), to customer delivery locations (downstream). During this process, pump stations, meter stations, storage sites, interconnection points, and block valves generate a substantial volume and variety of data that can lead to increased efficiency and safety.

“Just one pipeline pump station might have 6 Programmable Logic Controllers (PLCs), 12 flow computers, and 30 field instruments, and each one is a source of valuable operational information,” said Mike Walden, IT and SCADA Director for New Frontier Technologies, a Cisco IoT Design-In Partner that implements OT and IT systems for industrial applications. Until recently, data collection from pipelines was so expensive that most operators only collected the bare minimum data required to comply with industry regulations. That data included pump discharge pressure, for instance, but not pump bearing temperature, which helps predict future equipment failures.

A turnkey solution to modernize midstream operations

Now midstream operators are modernizing their pipelines with Industrial Internet of Things (IIoT) solutions. Cisco and New Frontier Technologies have teamed up to offer a solution combining the Cisco 1100 Series Industrial Integrated Services Router, Cisco Edge Intelligence, and New Frontier’s know-how. Deployed at edge locations like pump stations, the solution extracts data from pipeline equipment and is sent via legacy protocols, transforming data at the edge to a format that analytics and other enterprise applications understand. The transformation also minimizes bandwidth usage.

Mike Walden views the Cisco IR1101 as a game-changer for midstream operators. He shared with me that “Before the Cisco IR1101, our customers needed four separate devices to transmit edge data to a cloud server—a router at the pump station, an edge device to do protocol conversion from the old to the new, a network switch, and maybe a firewall to encrypt messages…With the Cisco IR1101, we can meet all of those requirements with one physical device.”

Collect more data, at almost no extra cost

Using this IIoT solution, midstream operators can for the first time:

  • Collect all available field data instead of just the data on a polling list. If the maintenance team requests a new type of data, the operations team can meet the request using the built-in protocol translators in Edge Intelligence. “Collecting a new type of data takes almost no extra work,” Mike said. “It makes the operations team look like heroes.”
  • Collect data more frequently, helping to spot anomalies. Recording pump discharge pressure more frequently, for example, makes it easier to detect leaks. Interest in predicting (rather than responding to) equipment failure is also growing. The life of pump seals, for example, depends on both the pressure that seals experience over their lifetime and the peak pressures. “If you only collect pump pressure every 30 minutes, you probably missed the spike,” Mike explained. “If you do see the spike and replace the seal before it fails, you can prevent a very costly unexpected outage – saving far more than the cost of a new seal.”
  • Protect sensitive data with end-to-end security. Security is built into the IR1101, with secure boot, VPN, certificate-based authentication, and TLS encryption.
  • Give IT and OT their own interfaces so they don’t have to rely on the other team. The IT team has an interface to set up network templates to make sure device configuration is secure and consistent. Field engineers have their own interface to extract, transform, and deliver industrial data from Modbus, OPC-UA, EIP/CIP, or MQTT devices.

As Mike summed it up, “It’s finally simple to deploy a secure industrial network that makes all field data available to enterprise applications—in less time and using less bandwidth.”

Originally posted here.

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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.

“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. 

What is edge computing? 

The “edge” of a network generally refers to technology located adjacent to the machine which you are analyzing or actuating, such as a gas turbine, a jet engine, or magnetic resonance (MR) scanner. 

Until recently, edge computing has been limited to collecting, aggregating, and forwarding data to the cloud. But what if instead of collecting data for transmission to the cloud, industrial companies could turn massive amounts of data into actionable intelligence, available right at the edge? Now they can. 

This is not just valuable to industrial organizations, but absolutely essential.

Edge computing vs. Cloud computing 

Cloud and edge are not at war … it’s not an either/or scenario. Think of your two hands. You go about your day using one or the other or both depending on the task. The same is true in Industrial Internet workloads. If the left hand is edge computing and the right hand is cloud computing, there will be times when the left hand is dominant for a given task, instances where the right hand is dominant, and some cases where both hands are needed together. 

Scenarios in which edge computing will take a leading position include things such as low latency, bandwidth, real-time/near real-time actuation, intermittent or no connectivity, etc. Scenarios where cloud will play a more prominent role include compute-heavy tasks, machine learning, digital twins, cross-plant control, etc. 

The point is you need both options working in tandem to provide design choices across edge to cloud that best meet business and operational goals.

Edge Computing and Cloud Computing: Balance in Action 

Let’s look at a couple of illustrations. In an industrial context, examples of intelligent edge machines abound—pumps, motors, sensors, blowout preventers and more benefit from the growing capabilities of edge computing for real-time analytics and actuation. 

Take locomotives. These modern 200 ton digital machines carry more than 200 sensors that can pump one billion instructions per second. Today, applications can not only collect data locally and respond to changes on that data, but they can also perform meaningful localized analytics. GE Transportation’s Evolution Series Tier 4 Locomotive uses on-board edge computing to analyze data and apply algorithms for running smarter and more efficiently. This improves operational costs, safety, and uptime. 

Sending all that data created by the locomotive to the cloud for processing, analyzing, and actuation isn’t useful, practical, or cost-effective. 

Now let’s switch gears (pun intended) and talk about another mode of transportation—trucking. Here’s an example where edge plays an important yet minor role, while cloud assumes a more dominant position. In this example, the company has 1,000 trucks under management. There are sensors on each truck tracking performance of the vehicle such as engine, transmission, electrical, battery, and more. 

But in this case, instead of real-time analytics and actuation on the machine (like our locomotive example), the data is being ingested, then stored and forwarded to the cloud where time series data and analytics are used to track performance of vehicle components. The fleet operator then leverages a fleet management solution for scheduled maintenance and cost analysis. This gives him or her insights such as the cost over time per part type, or the median costs over time, etc. The company can use this data to improve uptime of its vehicles, lower repair costs, and improve the safe operation of the vehicle.

What’s next in edge computing 

While edge computing isn’t a new concept, innovation is now beginning to deliver on the promise—unlocking untapped value from the data being created by machines. 

GE has been at the forefront of bridging minds and machines. Predix Platform supports a consistent execution environment across cloud and edge devices, helping industrials achieve new levels of performance, production, and profit.

Originally posted here.

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Computer vision is fundamental to capturing real-world data within the IoT. Arm technology provides a secure ecosystem for smart cameras in business, industrial and home applications

By Mohamed Awad, VP IoT & Embedded, Arm

Computer vision leverages artificial intelligence (AI) to enable devices such as smart cameras to interpret and understand what is happening in an image. Recreating a sensor as powerful as the human eye with technology opens up a wide and varied range of use cases for computers to perform tasks that previously required human sight – so it’s no wonder that computer vision is quickly becoming one of the most important ways to capture and act on real-world data within the Internet of Things (IoT).

Smart cameras now use computer vision in a range of business and industrial applications, from counting cars in parking lots to monitoring footfall in retail stores or spotting defects on a production line. And in the home, smart cameras can tell us when a package has been delivered, whether the dog escaped from the back yard or when our baby is awake.

Across the business and consumer worlds, the adoption of smart camera technology is growing exponentially. In its 2020 report “Cameras and Computing for Surveillance and Security”, market research and strategy consulting company Yole Développement estimates that for surveillance alone, there are approximately one billion cameras across the world. That number of installations is expected to double by 2024.

This technology features key advancements in security, heterogeneous computing, image processing and cloud services – enabling future computer vision products that are more capable than ever.

Smart camera security is top priority for computer vision

IoT security is a key priority and challenge for the technology industry. It’s important that all IoT devices are secure from exploitation by malicious actors, but it’s even more critical when that device captures and stores image data about people, places and high-value assets.

Unauthorized access to smart cameras tasked with watching over factories, hospitals, schools or homes would not only be a significant breach of privacy, it could also lead to untold harm—from plotting crimes to the leaking of confidential information. Compromising a smart camera could also provide a gateway, giving a malicious actor access to other devices within the network – from door, heating and lighting controls to control over an entire smart factory floor.

We need to be able to trust smart cameras to maintain security for us all, not open up new avenues for exploitation. Arm has embraced the importance of security in IoT devices for many years through its product portfolio offerings such as Arm TrustZone for both Cortex-A and Cortex-M.

In the future, smart camera chips based on the Armv9 architecture will add further security enhancements for computer vision products through the Arm Confidential Compute Architecture (CCA).

Further to this, Arm promotes common standards of security best practice such as PSA Certified and PARSEC. These are designed to ensure that all future smart camera deployments have built-in security, from the point the image sensor first records the scene to storage, whether that data is stored locally or in the cloud by using advanced security and data encryption techniques.

Endpoint AI powers computer vision in smart camera devices

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The combination of image sensor technology and endpoint AI is enabling smart cameras to infer increasingly complex insights from the vast amounts of computer vision data they capture. New machine learning capabilities within smart camera devices meet a diverse range of use cases – such as detecting individual people or animals, recognizing specific objects and reading license plates. All of these applications for computer vision require ML algorithms running on the endpoint device itself, rather than sending data to the cloud for inference. It’s all about moving compute closer to data.

For example, a smart camera employed at a busy intersection could use computer vision to determine the number and type of vehicles waiting at a red signal at various hours throughout the day. By processing its own data and inferring meaning using ML, the smart camera could automatically adjust its timings in order to reduce congestion and limit build-up of emissions automatically without human involvement.

Arm’s investment in AI for applications in endpoints and beyond is demonstrated through its range of Ethos machine learning processors: highly scalable and efficient NPUs capable of supporting a range of 0.1 to 10 TOP/s through many-core technologies. Software also plays a vital role in ML and this is why Arm continues to support the open-source community through the Arm NN SDK and TensorFlow Lite for Microcontrollers (TFLM) open-source frameworks.

These machine learning workload frameworks are based on existing neural networks and power-efficient Arm Cortex-A CPUs, Mali GPUs and Ethos NPUs as well as Arm Compute library and CMSIS-NN – a collection of low-level machine learning functions optimized for Cortex-A CPU, Cortex-M CPU and Mali GPU architectures.

The Armv9 architecture supports enhanced AI capabilities, too, by providing accessible vector arithmetic (individual arrays of data that can be computed in parallel) via Scalable Vector Extension 2 (SVE2). This enables scaling of the hardware vector length without having to rewrite or recompile code. In the future, extensions for matrix multiplication (a key element in enhancing ML) will push the AI envelope further.

Smart cameras connected in the cloud

Cloud and edge computing is also helping to expedite the adoption of smart cameras. Traditional CCTV architectures saw camera data stored on-premises via a Network Video Recorder (NVR) or a Digital Video Recorder (DVR). This model had numerous limitations, from the vast amount of storage required to the limited number of physical connections on each NVR.

Moving to a cloud-native model simplifies the rollout of smart cameras enormously: any number of cameras can be provisioned and managed via a configuration file downloaded to the device. There’s also a virtuous cycle at play: Data from smart cameras can be now used to train the models in the cloud for specific use-cases so that cameras become even smarter. And the smarter they become, the less data they need to send upstream.

The use of cloud computing also enables automation of processes via AI sensor fusion by combining computer vision data from multiple smart cameras. Taking our earlier example of the smart camera placed at a road intersection, cloud AI algorithms could combine data from multiple cameras to constantly adjust traffic light timings holistically across an entire city, keeping traffic moving.

Arm enables the required processing continuum from cloud to endpoint. Cortex-M microcontrollers and Cortex-A processors power smart cameras, with Cortex-A processors also powering edge gateways. Cloud and edge servers harness the capabilities of the Neoverse platform.

New hardware and software demands on smart cameras

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The compute needs for computer vision devices continue to grow year over year, with ultra-high resolution video capture (8K 60fps) and 64-bit (Armv8-A) processing marking the current standard for high-end smart camera products.

As a result, the system-on-chip (SoC) within next-generation smart cameras will need to embrace heterogenous architectures, combining CPUs, GPUs, NPUs alongside dedicated hardware for functions like computer vision, image processing, video encoding and decoding.

Storage, too, is a key concern: While endpoint AI can reduce storage requirements by processing images locally on the camera, many use cases will require that data be retained somewhere for safety and security – whether on the device, in edge servers or in the cloud.

To ensure proper storage of high-resolution computer vision data, new video encoding and decoding standards such as H.265 and AV1 are becoming the de facto standard.

New use cases driving continuous innovation

Overall, the demands from the new use cases are driving the need for continuous improvement in computing and imaging technologies across the board.

When we think about image-capturing devices such as CCTV cameras today, we should no longer imagine grainy images of barely recognizable faces passing by a camera. Advancements in computer vision – more efficient and powerful compute coupled with the intelligence of AI and machine learning – are making smart cameras not just image sensors but image interpreters. This bridge between the analog and digital worlds is opening up new classes of applications and use cases that were unimaginable a few years ago.

Originally posted here.

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TinyML focuses on optimizing machine learning (ML) workloads so that they can be processed on microcontrollers no bigger than a grain of rice and consuming only milliwatts of power.

By Arm Blueprint staff
 

TinyML focuses on the optimization of machine learning (ML) workloads so that they can be processed on microcontrollers no bigger than a grain of rice and consuming only a few milliwatts of power.

TinyML gives tiny devices intelligence. We mean tiny in every sense of the word: as tiny as a grain of rice and consuming tiny amounts of power. Supported by Arm, Google, Qualcomm and others, tinyML has the potential to transform the Internet of Things (IoT), where billions of tiny devices, based on Arm chips, are already being used to provide greater insight and efficiency in sectors including consumer, medical, automotive and industrial.

Why target microcontrollers with tinyML?

Microcontrollers such as the Arm Cortex-M family are an ideal platform for ML because they’re already used everywhere. They perform real-time calculations quickly and efficiently, so they’re reliable and responsive, and because they use very little power, can be deployed in places where replacing the battery is difficult or inconvenient. Perhaps even more importantly, they’re cheap enough to be used just about anywhere. The market analyst IDC reports that 28.1 billion microcontrollers were sold in 2018, and forecasts that annual shipment volume will grow to 38.2 billion by 2023.

TinyML on microcontrollers gives us new techniques for analyzing and making sense of the massive amount of data generated by the IoT. In particular, deep learning methods can be used to process information and make sense of the data from sensors that do things like detect sounds, capture images, and track motion.

Advanced pattern recognition in a very compact format

Looking at the math involved in machine learning, data scientists found they could reduce complexity by making certain changes, such as replacing floating-point calculations with simple 8-bit operations. These changes created machine learning models that work much more efficiently and require far fewer processing and memory resources.

TinyML technology is evolving rapidly thanks to new technology and an engaged base of committed developers. Only a few years ago, we were celebrating our ability to run a speech-recognition model capable of waking the system if it detects certain words on a constrained Arm Cortex-M3 microcontroller using just 15 kilobytes (KB) of code and 22KB of data.

Since then, Arm has launched new machine learning (ML) processors, called the Ethos-U55 and Ethos-U65, a microNPU specifically designed to accelerate ML inference in embedded and IoT devices.

The Ethos-U55, combined with the AI-capable Cortex-M55 processor, will provide a significant uplift in ML performance and improvement in energy efficiency over the already impressive examples we are seeing today.

TinyML takes endpoint devices to the next level

The potential use cases of tinyML are almost unlimited. Developers are already working with tinyML to explore all sorts of new ideas: responsive traffic lights that change signaling to reduce congestion, industrial machines that can predict when they’ll need service, sensors that can monitor crops for the presence of damaging insects, in-store shelves that can request restocking when inventory gets low, healthcare monitors that track vitals while maintaining privacy. The list goes on.

TinyML can make endpoint devices more consistent and reliable, since there’s less need to rely on busy, crowded internet connections to send data back and forth to the cloud. Reducing or even eliminating interactions with the cloud has major benefits including reduced energy use, significantly reduced latency in processing data and security benefits, since data that doesn’t travel is far less exposed to attack. 

It’s worth nothing that these tinyML models, which perform inference on the microcontroller, aren’t intended to replace the more sophisticated inference that currently happens in the cloud. What they do instead is bring specific capabilities down from the cloud to the endpoint device. That way, developers can save cloud interactions for if and when they’re needed. 

TinyML also gives developers a powerful new set of tools for solving problems. ML makes it possible to detect complex events that rule-based systems struggle to identify, so endpoint AI devices can start contributing in new ways. Also, since ML makes it possible to control devices with words or gestures, instead of buttons or a smartphone, endpoint devices can be built more rugged and deployable in more challenging operating environments. 

TinyML gaining momentum with an expanding ecosystem

Industry players have been quick to recognize the value of tinyML and have moved rapidly to create a supportive ecosystem. Developers at every level, from enthusiastic hobbyists to experienced professionals, can now access tools that make it easy to get started. All that’s needed is a laptop, an open-source software library and a USB cable to connect the laptop to one of several inexpensive development boards priced as low as a few dollars.

In fact, at the start of 2021, Raspberry Pi released its very first microcontroller board, one of the most affordable development board available in the market at just $4. Named Raspberry Pi Pico, it’s powered by the RP2040 SoC, a surprisingly powerful dual Arm Cortex-M0+ processor. The RP2040 MCU is able to run TensorFlow Lite Micro and we’re expecting to see a wide range of ML use cases for this board over the coming months.

Arm is a strong proponent of tinyML because our microcontroller architectures are so central to the IoT, and because we see the potential of on-device inference. Arm’s collaboration with Google is making it even easier for developers to deploy endpoint machine learning in power-conscious environments.

The combination of Arm CMSIS-NN libraries with Google’s TensorFlow Lite Micro (TFLu) framework, allows data scientists and software developers to take advantage of Arm’s hardware optimizations without needing to become experts in embedded programming.

On top of this, Arm is investing in new tools derived from Keil MDK to help developers get from prototype to production when deploying ML applications.

TinyML would not be possible without a number of early influencers. Pete Warden, a “founding father” of tinyML and a technical lead of TensorFlow Lite Micro at Google,&nbspArm Innovator, Kwabena Agyeman, who developed OpenMV, a project dedicated to low-cost, extensible, Python-powered machine-vision modules that support machine learning algorithms, and Arm Innovator, Daniel Situnayake a founding tinyML engineer and developer from Edge Impulse, a company that offers a full tinyML pipeline that covers data collection, model training and model optimization. Also, Arm partners such as Cartesiam.ai, a company that offers NanoEdge AI, a tool that creates software models on the endpoint based on the sensor behavior observed in real conditions have been pushing the possibilities of tinyML to another level. 

Arm, is also a partner of the TinyML Foundation, an open community that coordinates meet-ups to help people connect, share ideas, and get involved. There are many localised tinyML meet-ups covering UK, Israel and Seattle to name a few, as well as a global series of tinyML Summits. For more information, visit the tinyML foundation website.

Originally posted here.

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What is 5G NR (New Radio)?

by Gus Vos

Unless you have been living under a rock, you have been seeing and hearing a lot about&nbsp5G these days. In addition, if you are at all involved in Internet of Things (IoT) or other initiatives at your organization that use cellular networking technologies, you have also likely heard about 5G New Radio, otherwise known as 5G NR, the new 5G radio access technology specification.

However, all the jargon, hype, and sometimes contradictory statements made by solution providers, the media, and analysts regarding 5G and 5G NR can make it difficult to understand what 5G NR actually is, how it works, what its advantages are, to what extent it is different than other cellular radio access technologies, and perhaps most importantly, how your organization can use this new radio access technology.

In this blog, we will provide you with an overview on 5G NR, offering you answers to these and other basic 5G NR questions – with a particular focus on what these answers mean for those in the IoT industry. 

We can’t promise to make you a 5G NR expert with this blog – but we can say that if you are confused about 5G NR before reading it, you will come away afterward with a better understanding of what 5G NR is, how it works, and how it might transform your industry.

What is the NR in 5G NR?

As its name implies, 5G New Radio or 5G NR is the new radio access technology specification found in the 5G standard. 

Set by the 3rd Generation Partnership Project (3GPP) telecommunications standards group, the 5G NR specification defines how 5G NR edge devices (smart phones, embedded modules, routers, and gateways) and 5G NR network infrastructure (base stations, small cells, and other Radio Access Network equipment) wirelessly transmit data. To put it another way, 5G NR describes how 5G NR edge devices and 5G NR network infrastructure use radio waves to talk to each other. 

5G NR is a very important part of 5G. After all, it describes how 5G solutions will use radio waves to wirelessly transmit data faster and with less latency than previous radio access technology specifications. However, while 5G NR is a very important part of the new 5G standard, it does not encompass everything related to 5G. 

For example, 5G includes a new core network architecture standard (appropriately named 5G Core Network or 5GCN) that specifies the architecture of the network that collects, processes, and routes data from edge devices and then sends this data to the cloud, other edge devices, or elsewhere. The 5GCN will improve 5G networks’ operational capacity, efficiency, and performance.

However, 5GCN is not a radio access technology like 5G NR, but rather a core network technology. In fact, networks using the 5GCN core network will be able to work with previous types of radio access technologies – like LTE. 

Is 5G NR one of 5G’s most important new technological advancements? Yes. But it is not the only technological advancement to be introduced by 5G.  

How does 5G NR work?

Like all radio access communications technology specifications, the 5G NR specification describes how edge devices and network infrastructure transmit data to each other using electromagnetic radio waves. Depending on the frequency of the electromagnetic waves (how long the wave is), it occupies a different part of the wireless spectrum.

Some of the waves that 5G NR uses have frequencies of between 400 MHz and 6 GHz. These waves occupy what is called sub-6 spectrum (since their frequencies are all under 6 GHz).

This sub-6 spectrum is used by other cellular radio access technologies, like LTE, as well. In the past, using different cellular radio access technologies like this over the same spectrum would lead to unmanageable interference problems, with the different technologies radio waves interfering with each other. 

One of 5G NR’s many advantages is that it’s solved this problem, using a technology called Dynamic Spectrum Sharing (DSS). This DSS technology allows 5G NR signals to use the same band of spectrum as LTE and other cellular technologies, like LTE-M and NB-IoT. This allows 5G NR networks to be rolled out without shutting down LTE or other networks that support existing LTE smart phones or IoT devices. You can learn more about DSS, and how it speeds the rollout of 5G NR while also extending the life of IoT devices, here.

One of 5G NR’s other major advancements is that it does not just use waves in the sub-6 spectrum to transmit data. The 5G NR specification also specifies how edge devices and network infrastructure can use radio waves in bands between 24 GHz and 52 GHz to transmit data.

These millimeter wave (mmWave) bands greatly expand the amount of spectrum available for wireless data communications. The lack of spectrum capacity has been a problem in the past, as there is a limited number of bands of sub-6 spectrum available for organizations to use for cellular communications, and many of these bands are small. Lack of available capacity and narrow spectrum bands led to network congestion, which limits the amount of data that can be transmitted over networks that use sub-6 spectrum. 

mmWave opens up a massive amount of new wireless spectrum, as well as much broader bands of wireless spectrum for cellular data transmission. This additional spectrum and these broader spectrum bands increase the capacity (amount of data) that can be transmitted over these bands, enabling 5G NR mmWave devices to achieve data speeds that are four or more times faster than devices that use just sub-6 spectrum. 

The additional wireless capacity provided by mmWave also reduces latency (the time between when device sends a signal and when it receives a response). By reducing latency from 10 milliseconds with sub-6 devices to 3-4 milliseconds or lower with 5G NR mmWave devices, 5G enables new industrial automation, autonomous vehicle and immersive gaming use cases, as well as Virtual Reality (VR), Augmented Reality (AR), and similar Extended Reality (XR) use cases, all of which require very low latency. 

On the other hand, these new mmWave devices and network infrastructure come with new technical requirements, as well as drawbacks associated with their use of mmWave spectrum. For example, mmWave devices use more power and generate more heat than sub-6 devices. In addition, mmWave signals have less range and do not penetrate walls and other physical objects as easily as sub-6 waves. 5G NR includes some technologies, such as beamforming and massive Multiple Input Multiple Output (MIMO) that lessen some of these range and obstacle penetration limitations – but they do not eliminate them. 

To learn more about the implications of 5G NR mmWave on the design of IoT and other products, read our blog, Seven Tips For Designing 5G NR mmWave Products.

In addition, there has been a lot written on these two different “flavors” (sub-6 and mmWave) of 5G NR. If you are interested in learning more about the differences between sub-6 5G NR and mmWave 5G NR, and how together they enable both evolutionary and revolutionary changes for Fixed Wireless Access (FWA), mobile broadband, IoT and other wireless applications, read our previous blog A Closer Look at the Five Waves of 5G.

What is the difference between 5G NR and LTE?

Though sub-6 and mmWave are very different, both types of 5G NR provide data transfer speed, latency, and other performance improvements compared to LTE, the previous radio access technology specification used for cellular communications. 

For example, outside of its use of mmWave, 5G NR features other technical advancements designed to improve network performance, including:

• Flexible numerology, which enables 5G NR network infrastructure to set the spacing between subcarriers in a band of wireless spectrum at 15, 30, 60, 120 and 240 kHz, rather than only use 15 kHz spacing, like LTE. This flexible numerology is what allows 5G NR to use mmWave spectrum in the first place. It also improves the performance of 5G NR devices that use higher sub-6 spectrum, such as 3.5 GHz C-Band spectrum, since the network can adjust the subcarrier spacing to meet the particular spectrum and use case requirements of the data it is transmitting. For example, when low latency is required, the network can use wider subcarrier spacing to help improve the latency of the transmission.
• Beamforming, in which massive MIMO (multiple-input and multiple-output) antenna technologies are used to focus wireless signal and then sweep them across areas till they make a strong connection. Beamforming helps extend the range of networks that use mmWave and higher sub-6 spectrum.  
• Selective Hybrid Automatic Repeat Request (HARQ), which allows 5G NR to break large data blocks into smaller blocks, so that when there is an error, the retransmission is smaller and results in higher data transfer speeds than LTE, which transfers data in larger blocks. 
• Faster Time Division Duplexing (TDD), which enables 5G NR networks to switch between uplink and downlink faster, reducing latency. 
• Pre-emptive scheduling, which lowers latency by allowing higher-priority data to overwrite or pre-empt lower-priority data, even if the lower-priority data is already being transmitted. 
• Shorter scheduling units that trim the minimum scheduling unit to just two symbols, improving latency.
• A new inactive state for devices. LTE devices had two states – idle and connected. 5G NR includes a new state – inactive – that reduces the time needed for an edge device to move in and out of its connected state (the state used for transmission), making the device more responsive. 

These and the other technical advancements made to 5G NR are complicated, but the result of these advancements is pretty simple – faster data speeds, lower latency, more spectrum agility, and otherwise better performance than LTE. 

Are LPWA radio access technology specifications, like NB-IoT and LTE-M, supported by 5G?

Though 5G features a new radio access technology, 5G NR, 5G supports other radio access technologies as well. This includes the Low Power Wide Area (LPWA) technologies, Narrowband IoT (NB-IoT), and Long Term Evolution for Machines (LTE-M). In fact, these LPWA standards are the standards that 5G uses to address one of its three main use cases – Massive, Machine-Type Communications (mMTC). 

Improvements have been and continue to be made to these 5G LPWA standards to address these mMTC use cases – improvements that further lower the cost of LPWA devices, reduce these devices’ power usage, and enable an even larger number of LPWA devices to connect to the network in a given area.

What are the use cases for 5G NR and 5G LPWA Radio Access Technologies?

Today, LTE supports three basic use cases:

• Voice: People today can use LTE to talk to each other using mobile devices. 
• Mobile broadband (MBB): People can use smartphones, tablets, mobile and other edge devices to view videos, play games, and use other applications that require broadband data speeds.
• IoT: People can use cellular modules, routers, and other gateways embedded in practically anything – a smart speaker, a dog collar, a commercial washing machine, a safety shoe, an industrial air purifier, a liquid fertilizer storage tank – to transmit data from the thing to the cloud or a private data center and back via the internet.  

5G NR, as well as 5G’s LPWA radio access technologies (NB-IoT and LTE-M) will continue to support these existing IoT and voice use cases. 

However, 5G also expands on the MBB use case with a new Enhanced Mobile Broadband (eMBB) use case. These eMBB use cases leverage 5G NR’s higher peak and average speeds and lower latency to enable smart phones and other devices to support high-definition cloud-based immersive video games, high quality video calls and new VR, AR, and other XR applications.

In addition, 5G NR also supports a new use case, called Ultra-Reliable, Low-Latency Communications (URLLC). 5G NR enables devices to create connections that are ultra-reliable with very low latency. With these new 5G NR capabilities, as well as 5G NR’s support for very fast handoffs and high mobility, organizations can now deploy new factory automation, smart city 2.0 and other next generation Industrial IoT (IIoT) applications, as well as Vehicle-to-everything (V2X) applications, such as autonomous vehicles. 

As we mentioned above, 5G will also support the new mMTC use case, which represents an enhancement of the existing IoT use case. However, in the case of mMTC, new use cases will be enabled by improvements to LTE-M and NB-IoT radio access technology standards, not 5G NR. Examples of these types of new mMTC use cases include large-scale deployments of small, low cost edge devices (like sensors) for smart city, smart logistics, smart grid, and similar applications.

But this is not all. 3GPP is looking at additional new use cases (and new technologies for these use cases), as discussed in this recent blog on Release 17 of the 5G standard. One of these new technologies is a new Reduced Capability (RedCap) device – sometimes referred to as NR Light – for IoT or MTC use cases that require faster data speeds than LPWA devices can provide, but also need devices that are less expensive than the 5G NR devices being deployed today.

3GPP is also examining standard changes to NR, LTE-M, and NB-IoT in 5G Release 17 that would make it possible for satellites to use these technologies for Non-Terrestrial Network (NTN) communications. This new NTN feature would help enable the deployment of satellites able to provide NR, LTE-M, and NB-IoT coverage in very remote areas, far away from cellular base stations.

What should you look for in a 5G NR module, router or gateway solution?

While all 5G NR edge devices use the 5G NR technology specification, they are not all created equal. In fact, the flexibility, performance, quality, security, and other capabilities of a 5G NR edge device can make the difference between a successful 5G NR application rollout and a failed one. 

As they evaluate 5G NR edge devices for their application, organizations should ask themselves the following questions:

• Is the edge device multi-mode? 
While Mobile Network Operators (MNOs) are rapidly expanding their 5G NR networks, there are still many areas where 5G NR coverage is not available. Multi-mode edge devices that can support LTE, or even 3G, help ensure that wherever the edge device is deployed, it will be able to connect to a MNO’s network – even if this connection does not provide the data speed, latency, or other performance needed to maximize the value of the 5G NR application. 

In addition, many MNOs are rolling out non-standalone (NSA) 5G NR networks at first. These NSA 5G NR networks need a LTE connection in addition to a 5G NR connection to transmit data from and to 5G NR devices. If your edge device does not include support for LTE, it will not be able to use 5G NR on these NSA networks. 

• How secure are the edge devices? 
Data is valuable and sensitive – and the data transmitted by 5G NR devices is no different. To limit the risk that this data is exposed, altered, or destroyed, organizations need to adopt a Defense in Depth approach to 5G NR cybersecurity, with layers of security implemented at the cloud, network, and edge device levels. 

At the edge device level, organizations should ensure their devices have security built-in with features such as HTTPS, secure socket, secure boot, and free unlimited firmware over-the-air (FOTA) updates. 

Organizations will also want to use edge devices from trustworthy companies that are headquartered in countries that have strict laws in place to protect customer data. In doing so you will ensure these companies are committed to working with you to prevent state or other malicious actors from gaining access to your 5G NR data.

• Are the 5G NR devices future-proof? 
Over time, organizations are likely to want to upgrade their applications. In addition, the 5G NR specification is not set in stone, and updates to it are made periodically. Organizations will want to ensure their 5G NR edge devices are futureproof, with capabilities that include the ability to update them with new firmware over the air, so they can upgrade their applications and take advantage of new 5G NR capabilities in the future. 

• Can the 5G NR device do edge processing? 
While 5G NR increases the amount of data that can be transmitted over cellular wireless networks, in many cases organizations will want to filter, prioritize, or otherwise process some of their 5G NR application’s data at the edge. This edge processing can enable these organizations to lower their data transmission costs, improve application performance, and lower their devices energy use. 

5G NR edge devices that offer organizations the ability to easily process data at the edge allow them to lower their data transmission expenses, optimize application performance, and maximize their devices’ battery lives. 

Originally posted here.

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Look around you, and you’ll find at least 4 objects in your house with the word ‘smart’. Your TV, your phone, maybe even your fridge. All of our appliances are increasingly being connected to the internet.

This interconnection of ‘smart’ objects is referred to as the Internet of Things (IoT). In less than a decade, we’ve pushed communication technology to its limits.

Now you can talk to your microwave from your phone. With over 20 billion devices part of the IoT phenomenon, our lives are more connected than ever before. But, there's a flip side to this technological revolution.

There are a lot of IoT security risks that arise from the vulnerabilities of the devices. Over 200 million individual IoT attacks were tracked in 2020 alone.

Hackers can take advantage of vulnerabilities to steal your data, or to use your devices to conduct attacks. This blog will take you through the procedures you can adopt to secure your life from cybercriminals.

Overview of IoT

Internet Protocol Version 4 (IPv4) is the protocol that is the process through which our devices connect with the internet.

When the internet is accessed, a unique identity protocol is generated Recently, however, IPv4 has been running out of physical addresses. To fix this, IPv6 was introduced that has the capacity for trillions of trillions of physical addresses. It also offers improvements to connectivity, performance, and security. 

IoT has already been used extensively to provide ease to human life, with some of the many applications being innocuous.

  • Home Automation - Phones are now being used to connect with most homes to your electric circuits, your TV, AC Fridge, and more. This has simplified lives while improving productivity and efficiency. Many of these devices also optimise themselves, for instance, an AC that adjusts temperature itself based on the environment.
  • Smart Cities - By far the most awaited feature of IoT. Smart Cities promise to revolutionize the way you live. Not only will they reduce costs, but they would also improve efficiency. Road signals would be able to manage traffic congestions, and parking sensors would inform you of empty spots.
  • Drones - Drones are increasingly being used to simplify our lives. Right from being used by Amazon as delivery agents, to being used by government bodies for firefighting activities, drones are revolutionizing the tech space.
  • Medical Applications - Smartwatches monitor a person’s health and can call them an ambulance when at risk. Ambulances that can connect to the road signals and clear a path. 
  • Smart Phones - Your phone will most likely be the key to controlling the IoT. Already many appliances connect to your phone, and many more may soon follow. VR and AR have made great leaps forward, allowing people to do much more than simply call people with their phones.

Threats of IoT


IoT suffers from similar vulnerabilities that hackers attempt to use to their advantage. They use these devices to either steal personal data or to connect them to a botnet. According to Symantec, IoT attacks have increased a 1000% since 2016, with routers and security cameras being the most attacked.

  • Botnet - A botnet is essentially a large collection of IoT that hackers can be used to make large coordinated attacks on other services. Botnets are easy to create, with IoT being a favored target due to their weaker security.
  • Shadow IoT - Losing control of your devices can be terrifying. Researchers have already proved it possible to stall the engines of smart cars too. Many industries use SCADA systems, a massive computer overseer. Were this to be compromised, it could lead to catastrophe, including nuclear meltdowns in the worst case. 
  • Data Theft - The most common threat. Data can be extracted not only from the compromised device but all on the network. This can be catastrophic for your business. Losing the data of millions of users can erode their trust in your services. 

Applying Penetration Testing to circumvent IoT Attacks


Thankfully, there are ways in which firms protect themselves from IoT Security risks. Using penetration testing (pen testing), a simulated attack on your device, you can identify vulnerabilities and fix them before an actual attack hit. Pen testing will search for a range of vulnerabilities, ranging from but not limited to,

  1. Weak passwords: passwords that can be guessed or opened through trial and error.
  2. Hardcoded passwords: Public passwords that cannot be changed, such as firmware backdoors and client software.  
  3. Network services: This helps the devices on the network to communicate and share information. 
  4. Ecosystem Interfaces: Authentication, encryption issues, and input/output filtering problems arising from the device’s connections. These could be the internet, the backend API, the cloud, or other devices.
  5. Updates: The ability to receive and apply updates to firmware, security, and provide warnings for security changes. 
  6. Components: Insecure or outdated components such as software, libraries, customization, third-party apps, etc.  
  7. Privacy: Data that can be leaked due to a device on the network not being configured with the proper controls.
  8. Data Security: Data encryption and access control during storage, transit, and processing to prevent hijacks between connections. 
  9. Default Settings: Insecure default settings that might still be on the device.
  10. Physical Hardening: Physical hardening measures to prevent hackers from scoping devices or taking local control of devices. 

Your business must be secure and Pen testing is a great way of identifying these vulnerabilities, which you can then rectify.

Here's how firms can go about it. 

  • Identify Devices on The Network - The first step to protecting yourself is to identify which devices are actually on the network. As the network gets bigger, it can be harder to keep track of the devices. Don’t hesitate to pull back, take stock, and remove devices you don’t think need to be on the network.
  • Implement Strong Passwords - Without saying, this is the single most important tip. Always ensure your password is not something related to your personal information, such as your full name. Vary your password with numbers and symbols. And don’t use the same password everywhere!
  • Download The Latest Security Updates - As attacks increase, so does the defines. Many manufacturers release new updates that patch IoT vulnerabilities. Staying up to date can prevent older attacks from getting through.
  • Install Firewalls - Firewalls will prevent unauthorized access through the network. Intrusion detection systems/intrusion prevention systems (IDS/IPS) should be run to monitor and analyse network traffic. Firewalls also have the added benefit of warning you of unauthorized access. This can be the first sign of a breach in security.

IoT has a lot of potential for making human life convenient and efficient. However, it is advisable to take your time with implementing new technologies, make sure you have all the risks covered.

Rigorous testing should be your motto before introducing anything new to your business. Security should be your main priority. 

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