Wireless connectivity makes dumb&nbsp;products intelligent. Smart devices can transmit information about their surroundings and conditions while receiving instructions and updates. That in turn enables functionality that’s impossible for products operating in isolation.&nbsp;&nbsp;One example is a smart streetlight; by adding connectivity, the light can report whether it’s overheating and therefore likely to fail soon. It can also report its position allowing maintenance crews to arrive quickly and affect a repair before pedestrians are plunged into darkness. The streetlight could also be used as a platform for other monitoring technology such as air quality or noise pollution. Looking the other way, utilities could, for example, send an instruction over the wireless link to temporarily brighten the illumination to aid first responders attending a road traffic accident.&nbsp;&nbsp;The beneficial impacts of wireless tech are a key reason for its booming popularity. For example, Bluetooth LE—a key connectivity technology for consumer, smart home, computer peripherals, and industrial automation among other applications—is set to reach 5.9 billion shipments in 2024, up from 4.9 billion in 2022[1].&nbsp;But there is one challenge that holds back even greater wireless adoption – RF engineering is notoriously tricky. That puts off inexperienced engineers.&nbsp;&nbsp;Because of Nordic Semiconductor’s efforts to abstract away much of the complexity by offering comprehensive software and hardware&nbsp;<a href="https://www.nordicsemi.com/Products/Wireless/Bluetooth-Low-Energy/Development-tools?lang=en#infotabs" target="_blank">design tools</a>, plus reference designs, implementing a wireless design has become easier - but it’s still not trivial. Modules can offer a simpler route to market. &nbsp;<h2>Wireless for the experienced&nbsp;</h2>If you have enough&nbsp;RF&nbsp;expertise, designing a fully-functioning,&nbsp;optimized, and certified wireless circuit is practical.&nbsp;Especially if you base the circuit on a highly integrated wireless SoC from Nordic’s nRF52 or nRF53 Series.&nbsp;That’s&nbsp;because&nbsp;the chips already include&nbsp;processor(s),&nbsp;memory,&nbsp;and&nbsp;many of the peripheral devices&nbsp;and I/O interfaces&nbsp;you’ll&nbsp;need&nbsp;for your end-product. These&nbsp;components&nbsp;are all embedded onto the&nbsp;Nordic&nbsp;chip&nbsp;and&nbsp;optimized&nbsp;to work seamlessly together.&nbsp;But there’s much more to an RF design than the SoC. The designer will also need to worry about the design and position of the antenna, impedance matching circuits, power regulation, component orientation, PCB trace length, crosstalk, electromagnetic interference (EMI), and plenty of other taxing design decisions. And things can get even tougher when developing a multiprotocol design that, for example, combines Bluetooth LE and Wi-Fi into a single product. Such a device will require more than one antenna with each needing its own impedance matching requirements.&nbsp;Once you’re done with the development, the next step is a lengthy and costly testing and certification process.&nbsp;&nbsp;If you get it right, there are some advantages to doing it from scratch. For example, you can closely match the wireless circuit performance to your application by trading off range or throughput for superior battery life. Or trading off antenna sensitivity against a more compact solution. It’s also possible to lower the Bill of Materials (BoM) and produce a more compact solution by designing from scratch.&nbsp;But if you get the design even slightly wrong, you might face multiple design iterations to achieve a satisfactory performance. That costs time and money - and prolongs the time-to-market.&nbsp;<h2>The benefits of a module&nbsp;</h2>For the developer with less RF engineering experience a module can make things much easier. The module maker has already completed the challenging parts of the RF design allowing you to drop-in the solution to your design - confident it will function well.&nbsp;&nbsp;When using a module, the development cycle for the wireless product will be drastically shortened – allowing the product to reach market quicker. Alternatively, because much less time will be needed on the RF subsystem design, the development team can allocate more hours to working on other product hardware and firmware features that will really differentiate the end-product from rivals.&nbsp;Module makers have lots of experience, so the performance of their devices is high. They are also experts at squeezing the best performance into the smallest space, keeping end-product form factors nice and compact. For example, to reduce the device footprint, module makers have access to specialist packaging technology that allows them to incorporate wafer level chip scale packages (WLCSP) alongside passive components to help accommodate the SoC, RF Front End, Power Management IC (PMIC), and chip antenna in the smallest area.&nbsp;Further, modules makers are quick to embrace the latest SoCs from Nordic so you can be assured of the best performing SoC for the job. For example, there are dozens of modules with the high-end nRF5340 and nRF52840 SoCs together with those for Nordic’s mid-range and entry-level SoCs. Module makers are also already working on designs using Nordic’s fifth generation solutions, the&nbsp;<a href="https://www.nordicsemi.com/Products/nRF54H20" target="_blank">nRF54H</a>&nbsp;and&nbsp;<a href="https://www.nordicsemi.com/Products/nRF54L15" target="_blank">nRF54L</a>&nbsp;Series SoCs. There are also several module options that combine Nordic Bluetooth LE SoCs with the&nbsp;<a href="https://www.nordicsemi.com/Products/nRF7002" target="_blank">nRF7002</a>&nbsp;Wi-Fi Companion IC for developers needing this multiprotocol alternative.&nbsp;<h2>Tested and pre-certified&nbsp;</h2>With a discrete RF design, considerable time and money could be needed for the weeks of testing and design iterations needed to get things compliant with RF regulations and Bluetooth SIG requirements. And even after a successful test, it can take more weeks for certification to come through, delaying sales.&nbsp;In contrast, modules are typically supplied tested and pre-certified to industry requirements. Providing the module is incorporated into the product design without modification, this compliance carries over to the end-product.&nbsp;&nbsp;<h2>Working with Nordic’s module partners&nbsp;</h2>Nordic has teamed up with dozens of&nbsp;<a href="https://www.nordicsemi.com/Resources/Nordic-Partners/Third-party-modules" target="_blank" rel="noreferrer noopener">module makers</a>&nbsp;through its&nbsp;<a href="https://www.nordicsemi.com/Resources/Nordic-Partners" target="_blank" rel="noreferrer noopener">Nordic Partner Program</a>.&nbsp;These companies are trusted design partners and have worked with Nordic for many years. They know our technology inside and out. That means if you source your module from a&nbsp;Nordic&nbsp;partner you are guaranteed to get the highest performing product using the latest hardware and firmware – you&nbsp;can’t&nbsp;buy&nbsp;better.&nbsp;Designing an RF subsystem from scratch is possible and does potentially bring some optimization, space, and cost advantages. But it’s a job for a highly experienced engineering team. If you’d rather accelerate your project schedule, eliminate RF testing and compliance, and focus on what really differentiates your product from the competition then a module is the way to go. Things are made even easier by purchasing a module from a Nordic partner because you are then assured of the best service, the latest technology, and the widest range of options.&nbsp;Better yet, when basing your design on a module from a Nordic partner, you are still guaranteed support from Nordic and will still be able to use the company’s development tools and Software Development Kit to build and test application software. Finally, if you base your design on a module but later want to migrate to a discrete RF design when volumes really ramp up, Nordic will assist in that design transition.<hr><h3 id="isPasted">References</h3>1.&nbsp;<a href="https://www.bluetooth.com/blog/now-available-new-bluetooth-forecasts-and-trends/" target="_blank" rel="noreferrer noopener">https://www.bluetooth.com/blog/now-available-new-bluetooth-forecasts-and-trends/</a>&nbsp;

Wireless connectivity makes dumb products intelligent. Smart devices can transmit information about their surroundings and conditions while receiving instructions and updates. That in turn enables functionality that’s impossible for products operating in isolation.  

Using a Bluetooth LE wireless module to accelerate time-to-market

Explore the transformative power of precision agriculture, highlighting innovations like Particle's M-Series and the future of sustainable farming. Dive into the evolution, technologies, and trends shaping tomorrow's agriculture.

Unearthing the Potential of Precision Agriculture

The architectures of embedded systems continue to evolve as newer technologies become available to design engineers. What used to be a single central processing unit surrounded by interface and logic circuitry is now a multiprocessor multi-core design with advanced integrated peripheral functionality—many with their own dedicated microcontrollers and resources to contend with.The once clearly delineated computer edge is now a much vaster and more distributed architecture. Computers that were islands to themselves are now a brick in the wall as they integrate into larger global high-speed connectivity. The hierarchal nature of globally and locally distributed microcontrollers means dedicated processing can occur closer to where data is being generated. The network is the computer.As higher-resolution data is sensed and processed, chunks of information extraction at each level often result in huge data sets that require a massive pool of immediate and virtual low-latency storage. Smart houses integrate into a higher level of the distributed processing hierarchy to behave in a city management process domain. Eventually, the need for our power grid to become a dynamically intelligent entity will be more apparent as electric vehicles and charges become the norm.In all cases, the Internet of Things (IoT) is central. Everything is connected: from handheld devices to cars and homes, traffic lights to biohazard sensors, factories to cities. This results in each system having multiple edges—some secure, some less so. In this evolving landscape of connectivity, machine learning is crucial as security concerns continually increase with hostile forces trying to wreak havoc.<h2>Machine Learning Is Key</h2>Designers of modern factories, appliances, and more seek machine learning (ML) solutions to tackle new challenges and allow companies to advance and compete. However, low latencies are required to allow useful data integration from extensive and varied sources. As a result, data must be accessed and transported globally very quickly to make these higher-level "thinking machines" respond in an effective and reasonable amount of time.&nbsp;This poses a challenge. Process-intensive security schemes and algorithms can add latency, meaning hardware security acceleration is needed and must be robust enough to protect against threats. Often, vital infrastructure is online and requires high-security protection.To meet these processing and security requirements, this new generation of designs calls for highly integrated MCUs. Communications data rates have grown from 50 bits per second (bps) to 10 gigabits per second (Gbps). Software-controlled bit banging will not work at these higher rates, so very high-speed communications hardware must be incorporated into modern microcontrollers.Additionally, wireless options like Wi-Fi® and Bluetooth®, as well as wired technologies like Ethernet and USB 3.x communications, must peacefully coexist in one system. Intelligent edge devices like facility routers can implement each of these simultaneously. Modern microcontrollers house several encapsulated protocol standards using dedicated accelerated hardware to further unburden CPUs.Analog and digital signal processing (DSP) requirements must also be addressed, especially with the many IoT devices that include sensor systems as part of their functional requirements. DSP functionality is critical to meet response times for industrial IoT control loops, whether it be simple thermostats or complex high-speed analog data streams from real-time factories. All this increased performance and functionality must be low power and feature power management techniques, as server farms already use tremendous amounts of energy.<h2>Meet the MCX N Series</h2>NXP Semiconductors' new <a href="https://www.mouser.com/new/nxp-semiconductors/nxp-mcx-n-mcus/" target="_blank">MCX N</a> Series advanced processor engines are general-purpose and application-specific microcontrollers based on Arm® Cortex®-M33 CPUs. The MCX N Series devices feature a rich mix of hardware-accelerated peripherals, communications, and signal processing, focusing on scalability and ease of development (Figure 1).<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE0OTg2MjY2ODM1LU1DWF9CbG9ja19EaWFncmFtLndlYnAiLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib">Figure 1: The high-speed Arm processing cores are surrounded by advanced memory interfaces and controllers, analog converters and DSP accelerators, communications interfaces, and security. (Source: NXP Semiconductors)At the heart of the microcontroller is a pair of 150MHz Cortex-M33 processors with up to 2MB of flash memory (code and data) and 512kB of SRAM. Additional dedicated hardware for DSP and CORDIC (pseudo multiplication and division) acceleration functions allows fast implementation of math-intensive algorithm control loops. One of the key components of this microcontroller is a hardware accelerator, eIQ® Neutron Neural Processing Unit (NPU).The MCX N Series features a plethora of communications interfaces, including Ethernet, USB FS (12Mbits/sec), USB HS (480Mbits/sec), I³C, CAN FD, and UART, as well as advanced mixed-signal interfaces like four single-ended or two differential 16-bit analog-to-digital converter (ADC) channels, 12- and 14-bit digital-to-analog converters (DACs), op amps, comparators, and stable temperature voltage references. A digital Synchronous Audio Interface (SAI) can generate waveforms and levels, and touch-sensing interfaces reduce form factors for handheld and space-constrained designs.The microcontrollers also feature NXP’s EdgeLock® Secure Enclave, Core Profile to ensure device-wide security intelligence, run time attestation, silicon root trust, and key management. Other security features include extensive cryptographic services, trust provisioning, and simplified path certifications. A high-performance crypto engine is also built in to perform on-the-fly encryption and decryption that provides a secure boot.<h3>Machine Learning with the MCX N Series</h3>An important feature of the NXP MCX N microcontrollers is the elQ® Neutron NPU (Figure 2). The NPU's scalability to billions or trillions of operations per cycle has the power to implement different learning techniques. One technique, the convolutional neural network (CNN) architecture, is a feed-forward neural network that learns feature engineering through filters. Also supported are recurrent neural networks (RNN), which are well suited for sequential time series data sets as part of the deep learning algorithms. Temporal convolution networks (TCN), which employ casual convolutions and dilations, provide adaptive learning and are also supported by the NXP elQ Neutron NPU.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE0OTg2Mjg3NTg0LU5YUCBlbFEgTmV1dHJvbiBOUFUud2VicCIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Figure 2: The MCX N microcontrollers’ scalable eIQ NPU is a dedicated controller core with in-line dequantization, activation, and pooling. It boasts learning times that are 30 times faster than conventional computational cores. (Source: NXP Semiconductors)<h2 id="isPasted">The Road to Development</h2>Unified software tools are essential for design engineers and software developers. The NXP MCUXpresso suite of development software and tools can help designers with a variety of tasks, including RTOS development.A choice of integrated development environments (IDEs), including MCUXpresso for VS Code, MCUXpresso IDE, IAR Embedded Workbench and Arm Keil MDK, work with a configuration tool to configure peripherals and multifunction pins. Bootables and utilities work with debug tools like Segger, NXP MCU-Link, FreeMASTER, and PEmicro to provide breakpoint, trace, and control/monitor functions that aid in software and firmware development. &nbsp;GUI Guider helps design HMI graphics libraries and displays.The elQ machine learning software development environment is essential, leveraging inference engines, neural network compilers, optimized libraries, and deep learning toolkits. The hardware abstraction layers support importing Pytorch, TensorFlow and ONNX models and running inference with TensorFlow Lite for Microcontrollers. These neural nets help learn and output useful information for sensor data in anomaly detection and predictive maintenance, voice and keyword recognition response, as well as image and video processing for object detection and recognition.The eIQ Toolkit provides a machine learning workflow and tools that enable graphical or command line interface to build, profile, and deploy your machine learning models with runtime insights. The eIQ portal is a graphic tool that lets designers create, optimize, debug, and export machine learning models. It can also import models from TensorFlow.NXP offers a wide range of application software, examples, reference designs, application notes, articles, and blogs on their website for applications such as audio, cloud connectivity, machine learning, motor control, RTOS, security, voice processing, touch sensing, and both wired and wireless connectivity.<h2>Conclusion</h2>Finding the right processor solution can take time and research. Not every design needs high-end supercomputing horsepower, but many applications in the Industrial and IoT edge require a significant amount of processing power and high-end peripherals to perform.The NXP MCX N Series is designed to meet these needs and more as it addresses the challenges of modern intelligent edge applications, such as IoT, security, power constraints, machine learning, and cost. With a mature and comprehensive development environment, the MCX N Series is ready to help engineers develop their designs, including newer technologies like neural networking and machine learning.

NXP Semiconductors' new MCX N Series advanced processor engines are general-purpose and application-specific microcontrollers based on Arm® Cortex®-M33 CPUs. 

The Evolving Edge: Transformation Is Now

The globalization of supply chains is being formed on production lines that are able to supply only the required quantity of products at the time required. This means that an obstacle arising in one place in the production line may strike a major blow to the entire supply chain.The causes of production delays and stoppages include conflicts, disasters, raw material shortages, and equipment and device failures. In particular, production stoppages and the outflow of defective products due to equipment and device failures may lead to major problems for manufacturers. These problems include the expense required to recover those defective products and opportunity losses due to product supply stoppages. Therefore, equipment maintenance, which bears responsibility for the stable operation of the production line, is an important issue that holds the key to the life of manufacturers.The importance of equipment maintenance has been strongly expressed up to now. The techniques of equipment maintenance have also been advancing from breakdown maintenance to preventive maintenance and then onto predictive maintenance as well in recent years.We introduce in an easy-to-understand manner equipment maintenance that is continuing to evolve in this way—from basic knowledge such as the types of equipment maintenance and their advantages and disadvantages to the technologies to realize the predictive maintenance that is considered the ideal form of equipment maintenance.<h2>What Is Equipment Maintenance?</h2>Equipment maintenance refers to the activities to keep various production equipment in factories functioning normally. It is also called “machine maintenance.” The purposes of equipment maintenance are cited as being, for example, to prolong the life of machine components used in equipment, to shorten machine and device downtime, and to prevent unexpected failures in addition to repairing equipment that has failed.Moreover, equipment maintenance up to now has mainly focused on machines and devices. Now, the focus is also on the software that controls the Programmable Logic Controllers (PLCs) and machines and devices with the conversion of regular factories into smart factories using industrial robots, machine vision, and other technologies.There are other terms similar to maintenance such as “upkeep” and “care,” but these are the same as equipment maintenance. The reason is that they all involve inspecting equipment and then servicing or repairing it as necessary so that it does not fail.<h2>Types of Equipment Maintenance and Their Advantages and Disadvantages</h2>Equipment maintenance includes breakdown maintenance and preventive maintenance. Preventive maintenance is further broken down into time-based maintenance and condition-based maintenance (Figure 1). We explain here the features and the advantages and disadvantages of each type.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714985901545-predictive-maintenance-img0001_en.png" style="width: 100%px;" class="fr-fic fr-dib">Fig. 1: Types of Equipment Maintenance<h3 id="isPasted">What Is Breakdown Maintenance?</h3>This is maintenance that is performed after a piece of equipment’s functions have declined or after it has stopped functioning (Fig. 2).<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714985917150-predictive-maintenance-img0002_en.png" style="width: 100%px;" class="fr-fic fr-dib">Fig. 2: Breakdown MaintenanceAdvantages: The advantages of breakdown maintenance include the fact that the burden on maintenance work is low because the condition of the equipment is not checked until it fails or a malfunction is discovered. Another advantage is that it keeps the costs involved in maintenance low in the case of minor failures. Disadvantages: Failures and malfunctions occur unexpectedly. Therefore, there may be delays in obtaining repair components, and so it may take some time to carry out repairs. In addition, there are risks such as major disruptions to production plans if the failure or malfunction occurs in a core piece of equipment. Furthermore, if there is a delay in discovering the failure or malfunction in the equipment, it may lead to defective products and their flowing out of the factory. Dealing with such issues may require a considerable amount of expense and time.<h3>What is Preventive Maintenance?</h3>This is maintenance that is performed based on a piece of equipment’s service life, the number of times it is used, its condition, or other factors. It is also called “time-based maintenance” (Fig. 3).<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714985934940-predictive-maintenance-img0003_en.png" style="width: 100%px;" class="fr-fic fr-dib">Fig. 3: Preventive MaintenanceAdvantages: The maintenance work is performed once a certain period has been reached. Therefore, it is possible to prevent failures and malfunctions from occurring in equipment. It is an effective maintenance method when the service life of each component used in equipment or the number of times those components are used can be grasped accurately. Disadvantages: It is necessary to establish criteria to ascertain as accurately as possible the life of components. Maintenance may be performed after a failure if the criteria is not accurate. Conversely, component and time losses may arise such as from the replacement of components that can still be used.<h3>What Is Predictive Maintenance?</h3>This is maintenance that is performed in line with extent of a piece of equipment’s deterioration by constantly monitoring its condition. It is also called “condition-based maintenance” (Fig. 4).<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714985956264-predictive-maintenance-img0004_en.png" style="width: 100%px;" class="fr-fic fr-dib">Fig. 4: Predictive MaintenanceAdvantages: The maintenance work is performed when the equipment deteriorates. Therefore, it is possible to reduce component and time losses such as from the periodic stoppage of equipment for maintenance and the replacement of components that can still be used. Of course, it is also possible to prevent failures and malfunctions that would lead to the equipment stopping unexpectedly. Disadvantages: It is essential to have equipment to constantly monitor the condition of equipment and to store the collected data. Specifically, the constant monitoring of equipment requires a large number of various types of sensors. In addition, data loggers and similar recording devices are needed to store data. Furthermore, it is necessary to have equipment and other technologies to connect to those recording devices to communicate the data.<h2>Four Technologies to Turn Preventive Maintenance into Predictive Maintenance</h2>The most common method of maintenance currently is preventive maintenance whereby equipment is periodically maintained by determining the inspection timing based on the service life of each component used in equipment or the number of times those components can be used. Furthermore, there are an increasing number of cases in which semi-predictive maintenance is being adopted. This is when a large number of sensors that measure vibrations, temperature, current, voltage, and other elements are attached to equipment and thresholds are then set for each of them with maintenance being performed when those upper or lower limits are exceeded. In fact, a survey conducted on companies engaged in converting regular factories into smart factories produced results showing that over 96% of them cited “equipment maintenance utilizing sensors and AI” as the reason they are continuing to convert their factories into smart factories (Fig. 5).<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714985974057-predictive-maintenance-img0005_en.png" style="width: 100%px;" class="fr-fic fr-dib">Fig. 5: Reasons Why Companies Want to Continue Converting Regular Factories into Smart Factories* About the survey:&nbsp; Surveying entity: Murata Manufacturing Co., Ltd. Survey focus: 11,084 workers in the manufacturing industry in Japan (screening survey) / 500 workers in the manufacturing industry involved in converting their company’s regular factories into smart factories (main survey) Survey method: Internet survey Survey period: 3 days from January 25 (Wed.) to 27 (Fri.), 2023 * The total values may not necessarily match due to rounding.In this way, equipment maintenance is currently undergoing a switch from preventive maintenance to predictive maintenance. We explain here the four technologies that can help with this switch from preventive maintenance to predictive maintenance.<h3>1. Introduction of Sensors into Equipment Maintenance</h3>Predictive maintenance begins with constant monitoring of the condition of equipment. There are individual differences in judgment when this monitoring is performed with the eyes and touch of people. This means it is not possible to respond accurately. We can quantify the condition of equipment by introducing sensors. Visualizing and analyzing the data collected from those sensors then enables us to respond more accurately. However, this requires installing many types and a large number of sensors in large factories. Accordingly, routing and connecting the sensor wiring requires many man-hours when changing the setup. Therefore, it is best to have noise-resistant sensors capable of wireless communication with a long transmission distance.Moreover, devices have been developed that can keep a log of the actions taken by workers on manufacturing lines in recent years. It is also possible to record how those workers respond when trouble occurs. Utilizing these records allows various matters to be saved as data, including the deterioration of equipment that occurs on the manufacturing line and the actions taken by workers in response. This data can be used to give hints to improving processes in addition to maintenance.<h3>2. Visualization of the Condition of Complex Equipment: Sensor Fusion</h3>Sensor fusion is a technology that realizes cognitive functions that cannot be obtained with a single sensor on an engineering basis. Specifically, sensor fusion has already been introduced into Advanced Driving Assistant Systems (ADAS). For instance, examples include reading the shapes of people, obstacles, and other objects with an image sensor and temperature sensor and then measuring the distance with a radar sensor utilizing milliwaves and microwaves to grasp the situation in the direction in which those objects are moving. It has also become possible to display warning messages on monitors in vehicles and to apply brakes automatically. This utilizes sensor fusion as a technology to, for example, automatically determine the situation with data collected from multiple sensors such as image sensors and radars and to then convey those results to the driver. When we use this technology in equipment maintenance, we can analyze the measurement data from vibration sensors, temperature sensors, current sensors, and voltage sensors on an integrated basis. For example, if a current or voltage value is abnormal despite the motor vibrations and temperature being normal, we can determine that there has been a problematic change to the load on the part where the motor is operating. In addition, if an abnormal value is seen only in a vibration sensor, it is likely to be bearing wear, shaft deformation, level abnormality, or similar (Fig. 6).<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714986016706-predictive-maintenance-img0006_en.png" style="width: 100%px;" class="fr-fic fr-dib">Fig. 6: Introduction of Sensor Fusion into Equipment MaintenanceConventionally, such abnormalities were grasped and determined by workers reading each sensor value and then comparing those values with their past experiences.&nbsp; However, if we use the technology of sensor fusion, we can aggregate data collected by many sensors and display it on monitors or other devices as results that indicate the condition of equipment. This allows even inexperienced workers to accurately grasp the condition of equipment.<h3>3. Introduction of AI into Equipment Maintenance</h3>Sensor fusion is an outstanding technology. However, as the number of sensors increases and measurements become more precise, the amount of data increases. This leads to calculations becoming complex. It also becomes difficult to output data that indicates accurate results. Accordingly, the introduction of deep learning into equipment maintenance has come to be seen as important. Introducing the AI technology of deep learning into equipment maintenance enables data collected from multiple sensors to be integrated and analyzed by AI. If there is an error in the output results, it is corrected through machine learning. Furthermore, accurate results are produced. When we utilize AI in equipment maintenance, we become able to respond correctly to the condition of equipment that is constantly changing with self-learning such as by comparing the data collected during normal times and data collected from sensors to extract the difference in addition to simply detecting abnormalities that are determined by establishing a threshold (Fig. 7).<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714986035251-predictive-maintenance-img0007_en.png" style="width: 100%px;" class="fr-fic fr-dib">Fig. 7: Introduction of AI into Equipment MaintenanceIn this way, introducing AI into equipment maintenance doesn’t just reduce the differences in judgments due to differences in the proficiency level of the workers; we can also solve issues in conventional maintenance activities that rely on manpower, which involves investing a lot of personnel and hours in work, including monitoring and inspections.&nbsp;<h3>4. Introduction of Edge AI Cameras and Other Edge AI Devices</h3>Edge AI is a technology that applies edge computing to AI. In edge AI, AI processing is performed with various sensors, cameras, mobile terminals, and other edge devices at the end of networks (Fig. 8). On the other hand, in contrast to edge AI, there is a technology called “cloud AI.” This is a technology that applies cloud computing to AI. This means AI processing is performed on the cloud server connected to the network and the results are then returned to the edge devices. If we compare edge AI and cloud AI, we find that there are advantages to edge AI. These advantages include outstanding response speed and data confidentiality because the AI processing is performed on edge devices without involving a network. This also means edge AI is not impacted by the quality of the network. High data confidentiality is an absolute requirement especially for equipment maintenance that handles data in factories. Moreover, a high response speed is sought in vibration and acceleration calculations, which require data collection at high sampling cycles. Therefore, we can say that edge AI is an attractive technology for predictive maintenance involving the handling of large volumes of data.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714986052981-predictive-maintenance-img0008_en.png" style="width: 100%px;" class="fr-fic fr-dib">Fig. 8: Introduction to Edge AI DevicesHowever, edge devices equipped with edge AI are small. This means they have disadvantages such as inferior memory size and calculating capacity compared to cloud AI. Accordingly, it is necessary to select either cloud AI, edge AI, or to use both together according to your purpose.<h2>Summary</h2>The importance of equipment maintenance for the stable operation of manufacturing lines has long been recognized. Nevertheless, on the other hand, it is also true that too much emphasis on production has led to daily maintenance activities being neglected. Nowadays, with the globalization of manufacturing having long since progressed, manufacturing lines are continuing to constantly supply products to supply chains. In particular, supply chain management has been built based on production output during normal operations in regular factories that are being converted into smart factories. Under these circumstances, unexpected equipment failures on the manufacturing line can deal a major blow to the supply chain and cause damage to a company’s image. Moreover, the introduction of sensor fusion, AI, and other technologies into equipment management is not just about optimizing equipment maintenance, reducing personnel expenses, and solving labor shortages. It is a cutting-edge approach with many advantages including the continuing maintenance of high productivity, the optimization of energy usage, and the effective utilization of personnel. It is no exaggeration to say that realizing predictive maintenance by introducing sensor fusion, AI, and other technologies into equipment maintenance is the key to future corporate management for the manufacturing industry, which is surrounded by a complex economic environment such as the widening of supply chains.&nbsp;<hr><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714986081243-1714986081243.png" class="fr-fic fr-dii">If you have any questions based on this article that you would like to discuss with an expert at Murata, don't hesitate to reach out. There's an entire team ready to support you with your question.<a href="https://www.murata.com/en-eu/contactform?&Detail=Wevolver%20request" target="_blank" rel="nofollow" class="button-main-action">GET IN TOUCH</a>

The importance of equipment maintenance is clear. In recent years, equipment maintenance techniques have also advanced from breakdown maintenance to preventive maintenance and now focus on predictive maintenance.

The Technology That Realizes Predictive Maintenance

The <a href="https://brainchip.com/podcast/" target="_blank" rel="noopener noreferrer">BrainChip podcast</a> is a valuable resource for engineers interested in the future of neuromorphic computing. In their recent episode, BrainChip's Rob Telson, spoke with Edge Impulse's CTO, Jan Jongboom, about machine learning on the edge.The Powerhouse: Edge ImpulseBefore diving into the episode's content, it's crucial to understand the prowess of Edge Impulse. Edge Impulse empowers developers to craft intelligent device solutions harnessing embedded Machine Learning (ML). With a focus on real-world applications, their platform streamlines the process of gathering sensor data, cloud-based ML model training, and deploying those models to edge devices. This has opened doors to myriad applications, from predictive maintenance and asset tracking to health monitoring, making it a game-changer in the IoT landscape.<h2>A conversation between two industy leaders</h2>The conversation between Rob and Jan covers a wide range of ideas. If you're an engineer or a tech aficionado, this episode is a goldmine of insights into a rapdily evolving industry. It's not just about understanding the technologies but visualizing their real-world applications. Imagine a world where devices self-optimize, predict failures before they occur, and adapt to user behaviors in real-time. That's the future BrainChip and Edge Impulse are paving the way for. Topics covered inlcude:<ul><li>Neuromorphic Computing: This technology, inspired by the human brain's neural structures, is the future of computing. Offering unparalleled speed, efficiency, and adaptability, neuromorphic computing is set to revolutionize the tech world.</li><li>Machine Learning on the Edge: The synergy of machine learning and neuromorphic computing is the episode's highlight. Jan and Rob elucidate how this fusion can birth smarter IoT devices capable of rapid, efficient information processing without relying on cloud computations.</li><li>Redefining IoT: The transformative potential of neuromorphic computing in the IoT domain is undeniable. Devices are not just becoming smarter; they're evolving to intuitively cater to user needs, optimize energy use, and bolster security.</li></ul>Concluding ThoughtsThe fusion of machine learning with neuromorphic computing is not just a technological advancement; it's a paradigm shift. As we inch closer to a hyper-connected future, this episode offers a lens into the innovations and challenges on the horizon. For those on the fence about tuning in, consider this: knowledge is power, and this podcast episode promises to supercharge your understanding of the next big thing in IoT.

This podcast episode promises to supercharge your understanding of the next big thing in IoT.

BrainChip Podcast: Deep Dive into the Impact of Neuromorphic Computing on IoT

Explore the transformative impact of predictive maintenance in industrial automation. Discover how Infineon’s advanced predictive maintenance solution enables operational efficiency, reduces downtime, and fosters sustainable industrial growth through innovative edge-to-cloud integration.


Industrial Efficiency with Predictive Maintenance Solution

As the world continues to embrace the Internet of Things (IoT), the demand for efficient power management solutions for IoT devices is on the rise. 

What Are the Best Applications for IoT in the New World of IC Power Management?

In today's competitive landscape, organizations can ill afford the risks of unplanned downtime and premature asset failure. This comprehensive guide unveils smart preventive maintenance strategies to minimize operational disruptions while maximizing productivity and ROI.

Preventive Maintenance: Optimizing Asset Performance and Longevity

<h2 id="isPasted">1. What is LoRaWAN?</h2>LoRaWAN is a standard within LPWA (Low Power Wide Area) wireless communication that uses the unlicensed band (sub-GHz band) that does not require a radio station license and features wide-area and long-distance communication as well as low speeds and low power consumption. LoRaWAN is a form of non-cellular LPWA that was established by the non-profit LoRa Alliance and recognized as an international standard by the International Telecommunication Union (ITU) in December 2021.LoRaWAN is a wireless network system that is used by connecting one or a small number of gateways with many end devices*1 equipped with a LoRaWAN module. LoRaWAN does not require a carrier communications network and it enables companies to build and operate their own networks, which makes it easy to introduce regardless of the application and business scale, and the number of users is increasing every year.*1 For example, communications devices that control device applications such as general lighting, cameras, and other sensors.<h3>1.1 Comparison of LoRaWAN (Non-cellular LPWA) and Cellular LPWA</h3>The LoRaWAN non-cellular LPWA standard is able to keep operating costs low compared to signing an agreement with a carrier and using their line. Furthermore, you do not need to consider the location where it will be used or whether stable communication with the carrier's base station is possible. In addition, because it enables you to build a complete network environment on your own, LoRaWAN can implement a private and secure wireless communications environment that is not mediated by the lines of an external operator.Reference: <a href="https://article.murata.com/en-global/article/what-is-lpwa-wireless-communication-1#section-55863" target="_blank">3.3 Cellular LPWA and Non-cellular LPWA Specifications Comparison｜What Is Low-Power Wide-Area (LPWA) Wireless Communication? - Basics</a><h3>1.2 Primary LoRaWAN Applications</h3>The primary applications of LoRaWAN include smart meters, smart cities, smart buildings, and enhancing the intelligence of public transportation in community infrastructure as well as support for IoT in the manufacturing industry, tracking of logistics cargo and people through location information, environmental sensing, smart agriculture, etc. Because it enables operation with a comparatively low cost that is not affected by the service area restrictions of carriers in contrast to cellular LPWA, LoRaWAN is attracting increased attention around the world as a means of wireless communication that is suitable for IoT and M2M in diverse fields.The following page introduces several examples utilizing LoRaWAN. <a href="https://article.murata.com/en-global/article/introduce-to-lorawan-2#section-59239" target="_blank">4. Examples of IoT Solutions Utilizing LoRaWAN｜LoRaWAN (Non-cellular LPWA) Primer – From Fundamentals to IoT Application Examples (2)</a><h2>2. LoRaWAN Communication Protocol Stack – Roles of LoRa and LoRaWAN –</h2>By looking at the communication protocol stack that is implemented, we can understand the features of those communication systems in many cases. (The communication protocol stack is explained in the&nbsp;<a href="https://article.murata.com/en-global/article/introduce-to-lorawan-1#section-59227" target="_blank">"Column – What Is the Communication Protocol Stack?"</a> section at the bottom of this page.) Figure 1 shows the LoRaWAN communication protocol stack. Here, we explain the LoRaWAN protocol stack as well as the respective meanings and roles of LoRa and LoRaWAN in the protocol.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzNzY5NjU3MzkxLWxvcmF3YW4xLWltZzAwMDFfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 1 LoRaWAN communication protocol stack<h3 id="isPasted">2.1 What is LoRa (Physical Layer)?</h3>The "LoRa" shown in the Physical Layer at the bottom of Figure 1 is an abbreviation of "Long Range" and refers to the wireless modulation method developed by U.S. company SEMTECH based on CSS (Chirp Spread Spectrum), which is a type of spectrum spread technique. Furthermore, the LoRa ISM band*2 is established by country and region as shown in Table 1.<table><caption>Table 1 LoRa frequency band by region (partial)</caption><thead><tr><th>Region(country or territory)</th><th>Name</th><th>Frequency band(MHz)</th></tr></thead><tbody><tr><td>Europe</td><td>EU868/EU433</td><td>863-870/433-435</td></tr><tr><td>U.S.</td><td>US915</td><td>902-928</td></tr><tr><td>China</td><td>CN779/CN470</td><td>779-787/470-510</td></tr><tr><td>Japan</td><td>AS923</td><td>920-928</td></tr><tr><td>Australia</td><td>AU915</td><td>915-928</td></tr><tr><td>India</td><td>IN865</td><td>865-867</td></tr><tr><td>Indonesia</td><td>AS2</td><td>923-925</td></tr><tr><td>South Korea</td><td>KR920</td><td>920-923</td></tr><tr><td>Malaysia</td><td>AS1</td><td>920-923</td></tr><tr><td>New Zealand</td><td>AU915</td><td>915-928</td></tr><tr><td>Singapore</td><td>AS1</td><td>920-923</td></tr><tr><td>Taiwan</td><td>AS2</td><td>923-925</td></tr><tr><td>Thailand</td><td>AS2</td><td>923-925</td></tr></tbody></table>*2 The ISM band is a frequency band established by the International Telecommunication Union (ITU) that can be used without a license in the fields of industry, science, medicine, etc. The ISM band includes frequency bands that are universal and those that are region (country and territory) specific. The latter type of band is established for LoRa.<h3 id="isPasted">2.2 What is LoRaWAN (MAC Layer)?</h3>The MAC*3 Layer LoRaWAN is the name of the non-cellular LPWA standard while also referring to the LoRaWAN network communication protocol (rules of communication). Under LoRaWAN, each end device connects to the gateway via the MAC (Device ID) without an IP to communicate wirelessly.Moreover, the MAC options include Class A, Class B, and Class C, which differ in terms of the upstream and downstream communication schemes and intervals in the two‐way communication between each end device and the gateway as well as the switching behavior of the receiving slot on the device side related to power consumption, etc. The low power consumption Class A is often used when running a device with a battery for a long period of time with little reception standby time on the device side. At the same time, other classes can be separately used according to the objective and environment such as when communicating with low latency while continuously supplying power to the device, etc.*3 MAC is an abbreviation of "Media Access Control," and within the hierarchical communication protocols, it establishes methods for signal transmission timing control, the division of data to be transmitted, the assembly (transmission) and disassembly (reception) of frames with a destination address and other added control data, error detection, etc. in addition to the definition and allocation of the MAC address, which identifies devices.<h2>3. LoRaWAN Network Construction – Configuration of the LPWA Devices, Gateway, Servers, etc. –</h2>Because LoRaWAN is a non-cellular LPWA standard, it does not use carrier base stations and communications networks, as is the case in the wireless communications of cellular LPWA devices. Under LoRaWAN, data communication is carried out wirelessly between the LoRaWAN-compliant gateway and the end devices including sensors equipped with LoRaWAN modules, etc. Data is transmitted through this gateway to the network and application servers, etc., which run on communication protocols that differ from the LoRaWAN network. The example below introduces a primary network built using LoRaWAN.<h3>3.1 Example of Building Your Own LoRaWAN Network</h3>Because non-cellular LPWA does not require a radio station license, it enables companies to build a system on their own by preparing the LoRaWAN-compliant gateway, network servers, and application servers, etc. in addition to the end devices equipped with LoRaWAN modules.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzNzY5NzA1NjA2LWxvcmF3YW4xLWltZzAwMDJfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 2 Example of building and operating your own LoRaWAN networkAs shown in Figure 2, the advantage of a LoRaWAN network is that it enables use of a private and secure wireless solution by building and operating the entire network oneself. Furthermore, while in some cases each server may be hosted on premises (internal environment), in other cases a cloud server contracted by the company may be used.<h3>3.2 Example of Building a LoRaWAN Network Using a Vendor</h3>In cases where it is not suitable to build a LoRaWAN environment on your own, such as usage for a specific period of time or if you lack the internal system integration or networking personnel, an alternative method is to use a vendor who sells or rents gateways that support the LoRaWAN communication protocol and offers cloud services, etc.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzNzY5NzI2NzkyLWxvcmF3YW4xLWltZzAwMDNfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib">Figure 3 Example of building a LoRaWAN network using vendor servicesAs shown in Figure 3, there are cases in which a company may sign a fee-based contract with a vendor for a LoRaWAN-compliant gateway and network servers or application servers according to the circumstances. The service details differ by vendor, but the ability to entrust the gateway implementation, server maintenance, etc. to the vendor is an advantage of such contracts. Furthermore, when it comes to LoRaWAN-compliant gateways that can connect and communicate with many devices, there are gateways that can only be used by the subscriber as well as plans that reduce usage fees by sharing a gateway between multiple subscribers.Moreover, cloud services are typically provided with the exception of the gateway. A monthly fee is required when subscribing to a gateway and servers. However, compared to cellular LPWA, which requires a mandatory contract with a carrier to use the cellular phone communication network, the service usage fees for network server vendors, etc. required to operate the LoRaWAN non-cellular LPWA standard are said to be low.<h2>Column – What Is the Communication Protocol Stack?</h2>Regardless of whether they are wireless or wired, telecommunications function by conforming to communication protocols (Reference:&nbsp;<a href="https://article.murata.com/en-global/article/basics-of-wireless-communication-2#section-58505" target="_blank">6. What Is a Communication Protocol?｜Basic Knowledge of Wireless Communication: Wireless Mechanism (2)</a>). Table 2 shows the role and correspondence of each layer of the communication protocol stack in the LoRaWAN model and the OSI Reference Model*4.<table><caption>Table 2 OSI Reference Model and the role of each layer, LoRaWAN model</caption><thead><tr><th colspan="2">OSI Reference Model</th><th>Roles and Examples</th><th>LoRaWAN model</th></tr></thead><tbody><tr><td colspan="2">Application Layer</td><td>Defines the functions that are required as an &nbsp; application such as sending and receiving files, &nbsp; etc. (HTTP, FTP, DNS, SMTP, POP, etc.)</td><td>Application</td></tr><tr><td colspan="2">Presentation Layer</td><td>Defines (SMTP, FTP, Telnet, etc.) &nbsp; the data expression format &nbsp; (encryption method, character code, etc.)</td><td rowspan="4">-</td></tr><tr><td colspan="2">Session Layer</td><td>Defines the steps related to the start and &nbsp; end (login and logout, etc.) of &nbsp; communication (TLS, NetBIOS, etc.)</td></tr><tr><td colspan="2">Transport Layer</td><td>Rules that ensure communication quality such &nbsp; as error detection and recovery in transmitted &nbsp; and received data (TCP, UDP, NetWare/IP etc.)</td></tr><tr><td colspan="2">Network Layer</td><td>Defines functions for relaying to different &nbsp; networks (IP, ARP, etc.)</td></tr><tr><td rowspan="2">Data Link Layer</td><td>MAC Layer</td><td rowspan="2">Defines the control procedures for &nbsp; transferring data (Ethernet, etc.)</td><td>LoRaWAN MAC&nbsp; (Class A / Class B / Class C)</td></tr><tr><td>LLC Layer</td><td>-</td></tr><tr><td colspan="2">Physical Layer</td><td>Defines connector shapes and other physical &nbsp; specifications (RS232C, etc.) for transferring &nbsp; data to communication channels as well as &nbsp; modulation systems in the case of wireless</td><td>LoRa modulation</td></tr></tbody></table>*4 The OSI Reference Model is an international standard model for a protocol stack. Currently, the protocol stack of this model is not directly used. However, because it is the fundamental reference model for the protocol stack, it is often compared to the various protocols that are implemented in devices.Table 2 shows that the LoRaWAN model stack has three layers while the TCP/IP model, which is the communication protocol of the Internet, has four layers, so we can see that the LoRaWAN model has a simpler protocol configuration. This means that it is easier to update the protocol and leads to improved response times.<hr id="isPasted"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713769894367-1713769894367.png" class="fr-fic fr-dii">If you have any questions based on this article that you would like to discuss with an expert at Murata, don't hesitate to reach out. There's an entire team ready to support you with your question.<a href="https://www.murata.com/en-eu/contactform?&Detail=Wevolver%20request" target="_blank" rel="nofollow" class="button-main-action">GET IN TOUCH</a>

LoRaWAN is a standard within LPWA (Low Power Wide Area) wireless communication that uses the unlicensed band (sub-GHz band) that does not require a radio station license and features wide-area and long-distance communication as well as low speeds and low power consumption.

LoRaWAN (Non-cellular LPWA) Primer - From Fundamentals to IoT Application Examples (1)

Harnessing cutting-edge technology to optimize energy efficiency, comfort, and security in modern buildings

Building Management: The Power of Building Automation Systems

Consider the following details when conducting power profiling:<ol start="1" type="1"><li>Hardware and sensor configurations: Analyze the components and sensors used in the device and their power consumption characteristics.&nbsp;Understand how different hardware configurations impact energy usage.</li><li>Firmware settings: Examine the firmware settings and configurations to identify any potential areas where power consumption can be optimized. Adjust settings and parameters to achieve more efficient power usage.</li><li>Communication parameters: Evaluate the communication parameters used by the device, such as transmission frequency and data rate. Analyze how different communication settings affect power consumption and battery life.</li><li>Use cases and corner cases: Consider the different use cases and scenarios in which the device will be deployed. Investigate how the device's behavior and power consumption vary under different conditions. Pay special attention to corner cases that may have unique power consumption patterns.</li></ol>Continuous benchmarking is crucial throughout the product development process. Utilize UART clever printouts and correlate them with power consumption to identify specific components or features that may be draining energy. This analysis will provide valuable insights on areas that can be optimized for improved power efficiency. Remember to turn off UART once the analysis is complete to avoid any impact on the final product.Additionally, consider network variables that can impact power consumption. Import network log files to understand how factors like distance to the gateway or Node B, weather conditions, and device density within the network influence power consumption. These variables can significantly affect how the battery is drained and should be taken into account when estimating battery life.By iteratively calculating battery life based on power consumption profiles throughout the development project, developers gain valuable knowledge and intuition about the factors that affect the power profile. This understanding allows them to maintain the desired power profile despite changes in the development stack, ensuring optimal power usage and battery life.<h2>Refined battery selection with battery profiling</h2>Understanding the power consumption behavior of an IoT device goes beyond relying solely on data sheet information. It involves conducting a critical analysis to assess the battery's performance in the unique scenario relevant to your project. This process provides insights into the battery's efficiency, stability, and suitability for the deployment.<h2 id="isPasted">Defining a Battery Profile</h2>A battery profile is not just a general overview; it is a tailored description of how a battery discharges under specific loads in particular environments. You can find such profiles in the battery's data sheets. To select the ideal battery for your solution, you need to <a href="https://www.qoitech.com/battery-toolbox/" target="_blank">create profiles</a> that align with your device's power requirements. Additionally, it is beneficial to profile several battery alternatives from different brands, batches, and temperatures to gain a thorough understanding of their behavior.<img border="0" width="605" src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713351007606-1713351007606.jpeg" alt="En bild som visar skärmbild, Multimedieprogram, programvara, GrafikprogramvaraAutomatiskt genererad beskrivning" class="fr-fic fr-dii">By following these steps, you can gain a deeper understanding of the power consumption behavior of your IoT device and make informed decisions when selecting the most appropriate battery for your project.<h2>Accelerated Battery Profile</h2>To create a battery discharge profile that aligns with your device's current consumption profile, you may need several months or even years. Thus, it is essential to develop an accelerated discharge profile. Here are some best practices to create an accurate and helpful profile:Maintain High Discharge Consistency: Coin cell batteries are particularly sensitive to high discharge, so it is crucial to match your device's peak current. Lithium-ion rechargeable batteries can withstand higher discharge currents, while alkaline batteries fall in between.Shorten Cycle Time: Determine the low discharge time the battery requires to recover.Increase Sleep Current: Ensure the battery can recover even in high discharge. Consult with Manufacturers: Seek assistance from battery manufacturers to verify your assumptions for the batteries you want to profile.<h2>Accuracy Considerations</h2>It's worth noting that precisely matching the discharged energy in the accelerated profile to the original profile may not be feasible, which could affect the profile's accuracy. The less acceleration used, the more accurate the results, but this method's level of accuracy is usually adequate for IoT implementations. It ensures a comprehensive real-world understanding of the battery's performance, contributing to successful IoT solutions.<h2>Profiling Different Battery Chemistries</h2>Here is an example from a customer case with LoRaWAN waste management IoT device. For this specific application, we profiled two distinct electro-chemistries encompassing six brands, including multiple samples from each brand. The profiling was carried out at room temperature, using an accelerated battery profile developed with input from battery manufacturers. Comparing samples from multiple manufacturers allowed us to evaluate how different form factors and chemical compositions influenced the results. Profiling the batteries in various environments could be the next logical step to observe the available capacity deviation from these controlled profiles, given that the profiling occurred at room temperature.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713351055997-otii-arc-battery-profiling.png" style="width: 100%px;" class="fr-fic fr-dib">Battery profiling set-up with Otii Arc ProBattery Profile AnalysisLM17500: Three samples of this battery were profiled, yielding an average capacity of approximately 3050 mAh before reaching the cut-off point, slightly higher than the data sheet capacity of 3000 mAh.CR123A: Two batteries were profiled, with an average capacity of about 1600 mAh, consistent with the data sheet for that specific temperature.Interestingly, alkaline batteries exhibited significant variations in available capacity based on the manufacturer, which is a crucial consideration if customers will be responsible for battery replacement.AAA: These batteries had capacities ranging from 1080 to 1150 mAh, with an average of around 1100 mAh. The data sheet indicated a capacity of 1200 mAh.AA: The AA batteries showed a wider range of capacities, from 2420 to 2730 mAh, with an average of approximately 2590 mAh. For example, the data sheet for Varta indicated a capacity of 2950 mAh.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713351084975-battery.png" style="width: 100%px;" class="fr-fic fr-dib">Batteries profiled in this studyOur profiling process identified performance nuances across different battery brands, chemistries, and form factors. These insights are crucial for making informed decisions specific to the device and use case, going beyond the data sheet capacities. Conducting profiling in diverse environmental conditions, particularly temperature, could further enhance our understanding and contribute to a more resilient IoT device design. You want to see more details from this study? Download the <a href="https://www.qoitech.com/whitepaper/powering-iot-devices/" target="_blank" id="isPasted">white paper</a>.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713351110704-picture6.png" style="width: 100%px;" class="fr-fic fr-dib">Battery profiles graphed for lithium manganese batteries from Saft and GP.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713351132266-picture7.png" style="width: 100%px;" class="fr-fic fr-dib">Battery profiles graphed for alkaline batteries (AA and AAA) from five different brands.<h2 id="isPasted">Replaying the Battery Profiles</h2>Accurately measured battery profiles provide a more realistic and tailored understanding of the battery's capacity for a specific application, surpassing the information provided in mere data sheets when estimating battery life. By directly applying the profiled battery discharge to the device instead of using a standard power supply, you can determine the actual usable capacity and further enhance the accuracy of the estimation. The actual usable capacity depends on the behavior of the device itself, and in some cases, we have encountered as low as 20% of the measured capacity. Tools such as <a href="https://www.qoitech.com/otii-ace/" target="_blank">Otii Ace Pro</a> with <a href="https://www.qoitech.com/battery-toolbox/" target="_blank">Battery Toolbox</a> can replicate these battery profiles, effectively serving as profiled batteries for the IoT device. By adjusting the used capacity in the battery emulator of these instrumentsand monitoring when the device either shuts down or reboots, you can uncover the true, realistic usable capacity. This ensures a more precise understanding of the battery's potential in the context of the specific application.<img border="0" width="605" src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713351148170-1713351148170.png" alt="Battery emulation with OtiiBattery Toolbox. MEasured and saved battery profile can be replayed to mimic the actual usable capacity and battery behaviour." class="fr-fic fr-dii">Battery emulation with Otii Battery Toolbox<h2>Conclusion</h2>We propose a technique for selecting the ideal battery: initial selection based on a defined use case and data sheet, power profiling of the IoT device and the battery for the use case, and a refined battery selection using device power behavior to assess the battery's real-world performance for the specific use case and device. This nuanced approach enables developers to make well-informed decisions that boost battery life.

Once the first prototype of your IoT device becomes available, it is essential to start power profiling and estimating battery life. This process involves gaining a comprehensive understanding of the device's configuration and power consumption characteristics within its specific deployment context.

How to pick the right battery for an IoT device: A waste management case study

Discover how Particle's M-Series transforms global asset tracking and fleet management for improved efficiency and security.

Steering IoT Innovation in Global Asset Expeditions and Fleet Journeys

While embedded systems tend to lack the processing horsepower of servers or even modern personal computers, the sheer number of devices is making them an increasingly valuable target for bad actors looking to run illegal botnets and cryptocurrency mining operations. One of the first major security-related wake-up calls for embedded system designers was the 2016 Nest thermostat botnet attacks. Given the consumer-facing nature of the particular Internet of Things (IoT) coupled with an increased sensitivity regarding privacy and security; the Nest botnet caused a huge amount of discussion. Those discussions tended to center on how companies should build security into their low-cost IoT products and how consumers can safely operate the devices in their homes and businesses.With the growing threat of cyberattacks, it is essential that developers keep security considerations in mind throughout the design process. By following some practical tips and recommendations, developers can guard against a wide range of attack scenarios. Read on for an outline of security measures developers can use in their embedded designs.<h2>Build Secure</h2>While there are numerous chip architectures, operating systems, and communications protocols; many IoT devices tend to be built around Arm®-based architectures, and if they run an OS it tends to be a Linux distribution. This commonality is good in many ways; lower costs and faster development times; it also comes with quite a few negatives. Attack vectors tend to become “one-size-fit-all”, especially for devices running a Linux-based OS. To mitigate the threats associated with widespread devices that share a common architecture, developers should implement the following “quick win” security design principles:<ul><li>Do NOT hardcode passwords into the firmware. Also, do not use a common default password for all devices. Require the user to create a custom username and password during device initialization.</li><li>Do NOT enable insecure protocols such as HTTP, FTP, or Telnet by default. Data going off the device via wired or wireless protocols must be strongly encrypted. Avoid “homebrewed” encryption solutions.</li><li>Ship the device with the most restrictive configuration possible and let the end-user make a proactive decision to reduce security-related settings.</li><li>All mechanisms used to access the devices should require authentication and authorization controls. Two-factor authentication (2FA) should be used if practical.</li><li>All user-facing inputs should be filtered to avoid injection-type attacks.</li><li>Implement a secure device management interface for the end-user that will allow them to manage their assets, update devices, monitor devices, and securely decommission the devices that have reached their end-of-life (EOL).</li><li>Over-The-Air (OTA) update mechanisms must be validated on-device. The update files must be sent encrypted en route to the device. Lastly, ensure there are anti-rollback features to prevent a device from being reverted to a previous, insecure firmware.</li><li>If third-party software libraries are used in the design of your device, they must be continually monitored to ensure that third-party updates are integrated and that they do not become deprecated. Abandoned software projects can become nasty vulnerabilities for your device. Change the third-party software default passwords before committing them to your project.</li><li>Limit what sensitive data should be stored on the device. Store such information in a secure enclave only.</li><li>Remember that with the IoT that embedded systems are only one part of a larger ecosystem. Ensure that security is built into the cloud, desktop, and mobile applications as well. Ecosystem security is only as strong as the weakest link.</li><li>Consider establishing a bug bounty program to encourage end-users and security researchers to submit flaws in a secure, responsible manner.</li></ul>Physical access to a device tends to be the game-over situation for devices. That doesn’t mean that there aren’t things that can be done to make it harder physically exploit these types of devices. Entire books have been written on making circuit boards and associated enclosures tamper-resistant but for a few “quick wins” consider the following physical design rules-of-thumb to harden your device:<ul><li>Pins for debugging ports such as JTAG and UARTs are very useful when developing and testing a device. They are also tempting targets for those seeking to reverse engineer a device with ill-intent. Obfuscating these pins and/or removing header pins for production units is recommended. Note that this will come at the expense of making it more difficult to troubleshoot once fielded. Designers must consider a balance between security and maintainability.</li><li>The use of adhesives, ultrasonic welding, and/or specialty security screws can make it more difficult to open a device.</li><li>Applying a non-conductive epoxy over sensitive components can obfuscate their identity and purpose.</li><li>You might be tempted to use older components that might have vulnerabilities. Be aware of the counterfeit components as well. If a deal seems too good to be true, it probably is. Balancing security and time-to-market considerations is not a decision to be taken lightly.</li><li>Multilayer boards can be used to route traces in a way that makes it harder to discern how the board functions.</li></ul>Some of the following recommendations might be a bit much for consumer-grade IoT devices. However, as we will elaborate on later, industrial control systems and defense systems would likely benefit from these more robust physical security measures:<ul><li>Build security features into the board itself, such as microswitches, mercury switches, or magnetic switches that can detect if a board is being unintentionally handled or opened. Nichrome wire or fiber optics can also be used. If the wire or fiber is negatively impacted by someone trying to tamper with a device, then there will be a detectable change in the current flow of the wire or the behavior of photons through the fiber.</li><li>Side Channel or glitching attacks, while perhaps not common, provide a unique advantage to adversaries in that they use the laws of physics against a device and thus are very difficult to prevent though can be detected. By attacking the timing or restricting the flow of electrons to a CPU, it’s possible to get a device to act in unintended ways that negate security features. Voltage and current sensors can be implemented onboard circuit boards to detect if glitching may be occurring, though there is potential for false positives.</li><li>Significant adversaries can employ x-ray machinery to peer into microchips and discover the topography of the transistors to determine function and more. Sensors that detect x-rays can be added to detect tampering, though can do nothing to prevent an adversary from extracting useful information.</li></ul>It should be noted that there is quite a dichotomy between the paradigms of security and openness. Security values obfuscation. Open hardware values understanding. Regardless, keep in mind the old adage that locks only keep honest people honest, and so too with security-at-large. For more information on how to build secure IoT devices, visit the Open Web Application Security Project (OWASP) IoT Project.<h2>Operate Secure</h2>Even if a manufacturer could implement all the best secure design principles in their product, they would be mostly for naught if the end-user does not operate their device in a secure manner.<ul><li>Change the default router name, router password, network name (SSID), and network encryption key. Be sure to use strong password principles and do not use the same password for both networks.</li><li>Segment your home network into two “virtual” networks so that IoT devices cannot be “seen” by the desktop computers, Network Attached Storage (NAS) devices, etc. To do this quickly and easily, use the guest network feature for your IoT network.</li><li>Most IoT devices rely on a smartphone app to control the device. Keep the app up to date and use 2FA for login if available.</li><li>Disable any features of your IoT devices that you do not intend to use.</li><li>Update the firmware of both your router and IoT devices on a regular basis.</li><li>When an IoT device reaches the end of life and no longer receives updates consider replacing it with a newer model.</li></ul><h2>Industrial Strength Security</h2>Consumer-facing IoT products may be plentiful, but their industrial counterparts, collectively&nbsp;<iframe id="65f2ad2682d39cfbc40c772a" width="250" class="specs-iframe" height="440" src="https://wevolver.com/iframe/specs/microchip-technology-evb-pci11400-pcie-switch-evaluation-board?iframe=true" frameborder="0" scrolling="no" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> referred to as Industrial Control Systems (ICS) manage numerous extremely important and potentially dangerous processes. Everything from energy production to factories uses embedded digital technology (referred to as Operational Technology or OT; in contrast to office-centric Information Technology or IT) to control the facilities and associated machinery responsible for performing the various processes. The ICS environment is sufficiently different from a strict IT environment that special considerations for hardening OT devices and ICS networks are warranted. The most fundamental principle is that ICS should not have a connection to the internet. While this seems like a no-brainer, it is surprising how often this fundamental rule is violated. For more information on how to secure ICS networks and devices, there are two security frameworks you should review: MITRE ATT&amp;CK for ICS and the MITRE ATT&amp;CK for Enterprise.The nature of where ICS systems are typically found (e.g., areas that are environmentally, chemically, or otherwise hazardous) means ICS has been designed to prioritize the availability of the systems over confidentiality. From a positive perspective, this typically means there are redundant systems, and those systems are designed to fail safely. However, ICS systems can be left in operations for several decades and may not always be kept up-to-date. In addition, many of the protocols are older and were built with efficiency, not security in mind. Bottom line, security in the ICS or IIoT space is uniquely challenging and best practices may be difficult to implement. However, embedded developers for such machinery should be cognizant of the need to modernize their design practices and incorporate security into future designs and not treat it as a bolt-on afterthought.

(Source: Nikolay N. Antonov - stock.adobe.com)

With the growing threat of cyberattacks, it is essential that developers keep security considerations in mind throughout the design process. By following some practical tips and recommendations, developers can guard against a wide range of attack scenarios.

Practical Tips for Embedded System Security

For thousands of years, humans have been building houses, roads, bridges, and markets that allowed us to live safer and more productive lives. But the tools of the trade have lacked the capability to let us do so as effectively as possible. &nbsp;Now, belatedly but perhaps inevitably, smart connectivity is helping to lay the foundations for improved productivity and worker safety in the all-important architecture, engineering, and construction (AEC) industry. To that end, technology companies are introducing a wide range of innovative products to power connected construction. &nbsp;<h2>The&nbsp;critical role of the&nbsp;building&nbsp;sector&nbsp;</h2>From humble beginnings, construction has culminated in giant cities where 55 percent of the world’s population resides, a figure, according to the UN, that’s set to rise to 68 percent by 2050.&nbsp; Today, the AEC sector is the largest in the global economy, responsible for employing around seven percent of the world’s working population, and about 13 percent of world’s GDP ($10 trillion) according to analyst McKinsey. And because around 50 percent of a construction project’s total cost relates to labor, &nbsp;skilled workers are the key to a project’s success.&nbsp;<h2>Tech&nbsp;provides a&nbsp;solution&nbsp;to productivity challenges&nbsp;</h2>Yet despite its importance, the construction industry has earned a reputation for being slow to innovate. As a result, the sector has often suffered from severe productivity challenges.&nbsp;Technology offers a solution for raising the game. The signs are promising; digital adoption is accelerating on the back of strong demand for more infrastructure, a shortage of skilled labor, and increased expectations for data transparency and integration. An estimated $50 billion was invested in AEC tech between 2020 and 2022—85 percent higher than the previous three years—according to the latest McKinsey research.&nbsp;Growth is being driven by increased uptake of the IoT, big data analytics, and AI, as well as a greater emphasis on sustainability. That’s allowing companies to take advantage of real time field reporting by connecting and tracking materials, equipment, and workers at the construction site.&nbsp;<h2>Enhanced productivity&nbsp;boosts the bottom line&nbsp;</h2>A construction project manager can now access a management platform with real-time project data and task alerts. This information enables fast decisions and speeds up workflows from the back office to the field, and onwards to the extended project team. And it removes the project silos that limit collaboration. These practices will also lead to improved margins.&nbsp;&nbsp;Connected construction offers several other advantages over manual operations. For one, it can improve communication and transparency on the worksite; for example, consider a worker reporting back to a supervisor via a smart device, or sensors and tags embedded in equipment providing key updates such as the location and status of a concrete mixer. Or connected vehicles like dump trucks, backhoe loaders, excavators, and mobile cranes, could enable location detection and monitoring of usage via embedded cellular and GNSS technology.&nbsp;&nbsp;<h2>Monitoring essential construction materials&nbsp;</h2>A critical requirement for the construction industry is monitoring essential construction materials like concrete for quality control. This requirement led German company ConcR to develop a&nbsp;<a href="https://www.nordicsemi.com/Nordic-news/2023/11/The-ConcR-Box-employs-Nordic-Semiconductors-nRF9160-SiP" target="_blank">material monitoring solution</a>&nbsp;that analyzes the characteristics of concrete as it cures, to check when it’s safe to proceed with the next stage of construction. The ConcR Sensors are attached to a rebar (a reinforcing steel bar used to strengthen concrete slabs) before the concrete is poured. The sensors provide key insights into the strength and durability of the cement or concrete by monitoring temperature, relative humidity, residual moisture, pH, and chloride levels.&nbsp;&nbsp;The Nordic&nbsp;<a href="https://www.nordicsemi.com/Products/nRF9160" target="_blank">nRF9160 SiP</a>-powered&nbsp;<a href="https://www.nordicsemi.com/Products/Wireless/Low-power-cellular-IoT" target="_blank">cellular IoT</a>&nbsp;solution provides data across the lifecycle of concrete and screed—a levelled layer of material applied to a floor or other surface—starting from construction until 25 years in operation. With this information, planners, engineers, contractors, and owner-operators gain data-informed insights about the condition of construction materials throughout their life.&nbsp;<h2>Putting worker safety&nbsp;first and foremost&nbsp;</h2>Beyond productivity gains, technology is essential to the safety of skilled construction workers on hazardous worksites.&nbsp;&nbsp;For example, by using sensors integrated into existing personal safety equipment such as hard hats, WakeCap Technologies’ solution connect workers across entire job sites.&nbsp;<a href="https://www.nordicsemi.com/Nordic-news/2021/01/Wirepas-Mesh-hard-hat-sensor-locates-wearer-across-large-sites-and-reports-activity-incidents" target="_blank">WakeCap</a>&nbsp;is a wearable product designed to overcome the challenge of “digitizing the construction sector’s field logistics and workforce”, according to the company. In operation, the hard hat-worn sensors track the wearer’s location and record knocks to the hard hat. In addition, WakeCap’s analytics compare the on-site work crew timing with staffing and project management plans, highlighting any unexpected problems early - helping avoid delays and extra costs.&nbsp;Thanks to wireless tech, construction equipment, vehicles, devices, personnel, and locations will communicate and work together using emerging connectivity platforms. Connected construction is here and the world is better for it.&nbsp;

Smart connectivity is helping to lay the foundations for improved productivity and worker safety in the all-important architecture, engineering, and construction (AEC) industry. To that end, technology companies are introducing a wide range of innovative products to power connected construction.  

Construction sector building on wireless tech foundation

Our new report, AI at the Edge: Technology Accelerated by Wi-Fi Services, dives deeply into the convergence of Wi-Fi services and Edge AI and explores how a technology model like WaaS can accelerate the adoption of AI at the edge.&nbsp;Below is an excerpt from the final chapter of the report. To read the full report, including in-depth case studies, download it now.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzExMTI0MjE4NTYyLVJlbGF5IDIucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The next years of this decade are going to be extremely exciting for AI, particularly Edge AI. And with Wi-Fi Services like Relay2 revolutionizing the access point market, the future is going to be very promising indeed. The surging demand for AI applications that offer real-time, low-latency, and privacy-conscious capabilities will usher in a wave of Edge AI implementations. These deployments are poised to become increasingly precise and efficient, capitalizing on advancements in communication networks, such as Wi-Fi 6, coupled with innovations in artificial intelligence (such as data-efficient AI) and the Internet of Things (IoT).&nbsp;This transformative landscape underscores the pivotal role played by researchers, industry leaders, and open-source communities in steering the trajectory of Edge AI development. The evolution of efficient Edge AI applications relies on the progress of various technologies designed to support edge devices and enhance the computational efficiency of AI functions, including Wi-Fi services.<h3 dir="ltr">Increased Adoption &amp; Integration with Edge AI</h3>As the demand for real-time processing and low-latency applications continues to surge, the integration of Edge AI with Wi-Fi services will follow suit. The agility and scalability of Wi-Fi seamlessly complement the requirements of edge computing and AI. For instance, the latest generation of Wi-Fi, Wi-Fi 6, promises much faster data transfer speeds that can reach up to five times the speeds of Wi-Fi 5. This is complemented by reduced power usage, increased capacity, and enhanced performance, providing higher connection reliability in environments with multiple connected devices. This is where Relay2 sets a standard with its Wi-Fi services, pushing for innovation that leverages advancements like Wi-Fi 6. With a dynamic system that ensures flexibility and security, Relay2 offers virtual access control and simultaneous HD video sessions that can support up to 128 sessions. This is further supported by high performance and management of heavy traffic, capitalizing on an average throughput DL that exceeds 2 Gbps and handles 1,024 clients concurrently.This is bound to grow and enhance the adoption and integration with Edge AI in order to enrich the user experience. By implementing AI at the edge for efficient data processing and leveraging Relay2’s proactive real-time analysis and natural language adaptability, businesses and organizations can elevate their user experience beyond merely improving connection reliability. This synergy allows for efficient processing closer to data sources, reducing latency and enhancing overall system responsiveness.With Wi-Fi 7 being the next generation of Wi-Fi, we could see an even more seamless and immersive user experience and an advanced Edge AI performance, building on its new features such as multi-link operation (MLO), 320 MHz superwide channels, and a 4k quadrature amplitude modulation (QAM). This convergence of state-of-the-art communications and information technologies, such as cloud-native Wi-Fi services, edge computing, IoT, and AI, is driving a massive leap in management efficiency and social productivity. The hybrid edge-cloud architectures and applications are likely to surge in the very near future, ensuring not only high speed and efficiency but also the ability to run multi-modal generative AI and language models at the edge . In the second half of this decade, we will arguably witness an unprecedented increase in the adoption of Edge AI across numerous applications, rendering the technology mainstream.<h3 dir="ltr">Computer Vision &amp; the Impact of Edge AI and Wi-Fi</h3>One area where the convergence of Edge AI and Wi-Fi services is poised to have a high impact is computer vision applications. With the capability to process data locally, Wi-Fi-empowered edge devices can execute intricate computer vision tasks with unparalleled speed and efficiency. This transformative shift not only minimizes reliance on centralized cloud servers but also allows positioning systems to optimize energy and costs, enabling applications such as facial recognition, object detection, and automated surveillance to operate seamlessly in real time.With localization techniques such as Wi-Fi-based positioning systems (WPS) and the advent of convolutional neural networks (CNNs), we can detect and extract spatial patterns from visual data in a remarkable manner. This has the potential to transform industries, including healthcare, retail, automotive, and traffic management. Combining the high-processing capabilities of cloud computing with the real-time features of edge computing and the connectivity enhancements brought about by Wi-Fi technologies can enable AI to lift computer vision to a different level. Consider industrial automation in large factories and warehouses, smart cities with optimized traffic control, retail centers and shopping complexes with enhanced services and store layouts based on monitored customer behavior, and even remote medical services and patient monitoring that enable accurate diagnosis in real time. All these developments can take place by leveraging computer vision that is powered by AI at the edge.While there are still challenges standing before computer vision today, such as high processing power and high-quality data, the potential that Edge AI brings, supported by Wi-Fi capabilities that ensure fast and seamless data transfer - even in remote areas where accessibility would otherwise be difficult - will propel computer vision and enable it to be a much more potent and ubiquitous technology.&nbsp;With on-device inference capabilities and functionalities that enable computer vision within edge and mobile devices, image and pattern recognition can improve significantly, taking advantage of edge computing’s reduced latency, enhanced security, and minimized costs. Integrating AI into the mix enables even low-cost cameras to act as smart cameras, with deep learning methods enabling better identification and interpretation of data patterns. This is further enhanced by leveraging data from multiple devices connected via Wi-Fi, which provides AI with more comprehensive data, reinforcing its precision and understanding.<h2 dir="ltr">Conclusion: Wi-Fi-Powered Edge AI - A Technology for the Future</h2>Wi-Fi and Edge AI represent a technological paradigm shift with far-reaching implications. The convergence of Wi-Fi with Edge AI is not just a technological evolution but a fundamental reshaping of the digital landscape. As this symbiotic relationship matures, it propels us towards a future where Edge AI is not just a technology but an integral and indispensable part of our interconnected world. The potential for transformative applications is immense, urging both developers and sellers to explore the uncharted territories of innovation and contribute to the unfolding narrative of Edge AI's promising future.This not only addresses the computational needs of Edge AI but also aligns with the growing trend of connected devices in smart buildings, industrial settings, healthcare, retail, and smart cities. The integration of Edge AI with Wi-Fi services brings forth a boost in performance, user experience, and security measures, ensuring that sensitive data is processed locally. This transformation leads to the development of robust, always-on systems capable of meeting the stringent privacy requirements of contemporary applications and the ever-growing need for better performance, flexibility, scalability, and reliability.Throughout this report, we explored the capabilities and impact of Edge AI, especially when coupled with Wi-Fi services. We highlighted the valuable insights provided by Relay2, the leading Wi-Fi technology provider for the Edge AI market. Relay2's Service Points technology is designed to deliver exceptional application performance while enhancing security, privacy, and scalability at the edge.&nbsp;The Relay2 ServiceEdge Platform harnesses the power of Wi-Fi Services with Edge Computing to create a digital world where application performance and user experience not only remain uncompromised but are also enhanced. We also dove into particular applications and reviewed some use cases of Wi-Fi services edge computing. And finally, we took a look at the future of these technologies and how they will impact our businesses and way of living in the coming years. All this could not have been done without the support of Relay2 in developing this report.&nbsp;Relay2 is committed to empowering its partners by providing comprehensive, innovative solutions to ensure mutual success leveraging Wi-Fi Services with Edge Computing and the Relay2 ServiceEdge Platform. Therefore, developers and sellers are encouraged to partner with Relay2 to unlock the full potential of technology and shape a seamless digital future. The convergence of Wi-Fi with Edge computing can reshape the digital technology and business landscape, and Relay2 is at the forefront of this transformation.<h2 dir="ltr">Acknowledgments</h2>This report is the result of collaborative efforts between Wevolver and Relay2. Initial input was graciously provided by Relay2’s Eric Chen and Daniel Kwong, sharing their insights, expertise, and case studies, creating a robust technical substructure for the report. Then, the author further expanded upon this information to develop a comprehensive and insightful text, supported by the content and design team at Wevolver. Thank you to all contributors and supporters, and special thanks to Relay2.Download the full report with custom images, case studies and more, below.&nbsp;<iframe width="540" height="305" src="https://c8056756.sibforms.com/serve/MUIFAIgRr7nh2u88k57MUBWp43e1lkOKIXq3Z9dxFiC8kXdgaxMxkxsZKsum_gd4u7Qq4_1n-e3n6hH4X_Nw12RLp7SUWPiUkUUgLHhq84lH8VfbGGhwuM1Hvkr60WpcS1SyyBLR-7ErbMPEfKrwqo_-_-CHDFwvTGW-swKGBpTKYdEO-rdhIbTFZyt7qA8fH9zg-zqRf6Li7cOQ" frameborder="0" scrolling="auto" allowfullscreen="" style="display: block;margin-left: auto;margin-right: auto;max-width: 100%;" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>

The coming years will see a surge in Edge AI advancements, driven by real-time, efficient, and privacy-focused AI applications, alongside innovations in Wi-Fi and IoT, marking a promising future shaped by researchers, industry leaders, and open-source contributions.

AI at the Edge Report. Chapter 4: Future of Edge AI with Wi-Fi Technology

Exploring the design, development, connectivity, security, and more aspects of IoT devices

Internet of Things: A Comprehensive Guide to its Engineering Principles and Applications

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Introduction

Edge AI Market Analysis and Trends

Healthcare and Medical Applications

Industrial IoT and Manufacturing

Smart Cities and Urban Infrastructure

Retail and Customer Experience

Energy Efficiency and Sustainability

Agriculture and Food Production 

Automotive and Transportation

Generative AI at the Edge

Edge AI Challenges and Real-World Mitigations

The Future of Edge AI

Exploring the Dynamic World of Edge AI Applications Across Industries


2024 State of Edge AI Report

Optimizing Operational Excellence: Synergies of Human-Machine Interfaces, Motion Control, IIoT, and Robust Power Supply in Industrial Automation

Understanding Industrial Automation: A Comprehensive Guide

Programmable Logic Controllers (PLCs) are the backbone of industrial automation, and PLC programming is the tool that brings these systems to life. In this article, we’ll take a look into the PLC basics, its programming process, programming language (with best practices), and tools to achieve this.

PLC Programming: A Comprehensive Guide to Mastering the Art of Automation

Born in the ‘80s, the CAN bus helps in carrying reliable electronic communication within your vehicles. This article delves into the basic principles, architecture, protocols, applications, and limitations of the CAN bus.

Understanding CAN Bus: A Comprehensive Guide

Network System Control Center Operator Working

Dive into our comprehensive guide on defense in depth layers, unravelling the complexities of multi-tiered security strategies for robust protection.

Unraveling Defense in Depth Layers: A Comprehensive Guide

An engineer using SCADA to monitor and control industrial processes via PLC.

PLCs bring precision control to critical processes, while SCADA empowers real-time data visualization and decision-making. In today's fast-paced industrial landscape, the ability to distinguish between PLC and SCADA is vital to ensure optimized production and efficient operations.


PLC and SCADA: Understanding the Differences in Industrial Automation Systems

IoT

2024 State of Edge AI Report

Introduction

1. Edge AI Market Analysis and Trends

2. Healthcare and Medical Applications

3. Industrial IoT and Manufacturing

4. Smart Cities and Urban Infrastructure

5. Retail and Customer Experience

6. Energy Efficiency and Sustainability

7. Agriculture and Food Production

8. Automotive and Transportation

9. Generative AI at the Edge

10. Edge AI Challenges and Real-World Mitigations

11. The Future of Edge AI

Internet of Things: A Comprehensive Guide to its Engineering Principles and Applications

Profiles