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The Guide to Understanding the Technologies Defining the Next Phase of Edge AI

The 2026 Edge AI Technology Report Just Launched

This white paper focuses on practical design choices that improve battery life and maintain stable operation across changing load conditions.

Efficient Power Management for Portable and IoT Devices

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

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

Half-duplex links take turns, while full-duplex links talk in both directions at once. This guide explains how each mode is built on real hardware, from Ethernet and RS-485 to Wi-Fi and 5G, what each costs you in throughput and latency, and how to choose between them.

Half Duplex vs Full Duplex: Key Differences Explained

Industrial Electrical Control Panel with Contactors, Circuit Components, and Colored Wiring, Labels

This article is a practical reference for establishing the SCCR rating of an industrial control panel, from the weakest-link method in UL 508A Supplement SB through the marking the inspector will look for.

SCCR Rating: How to Determine, Raise, and Mark It

A single point of failure is any part whose failure stops the whole system. Learn how engineers find SPOFs with FMEA and fault trees, quantify them with reliability math, and remove them with N+1, 2N, failover, quorum, and voting designs, and why common cause failures can still defeat redundancy.

Single Point of Failure: How to Find and Eliminate SPOFs

A practical engineering guide to spine-leaf data center fabrics: how every leaf connects to every spine, why the design replaced the three-tier network, how to size oversubscription and port counts, and which routing, cabling, and optics choices follow from it.

Spine and Leaf Architecture: Design, Sizing, and Cabling

What is star topology? See how a star network works, its advantages and disadvantages, extended and hub-and-spoke variants, and why Wi-Fi is a star.

Star Topology: How It Works, Pros, Cons, and Uses

UL 508A Industrial Control Panel Architecture

This guide covers what it applies to, how it differs from UL 508, how Supplement SB establishes the short-circuit current rating for a panel, and what it actually takes to label a panel under the UL 508A panel shop program.

UL 508A: The Standard for Industrial Control Panels

Radar finds objects by sending out radio waves and timing the echo that comes back. This guide explains how radar works from first principles, runs the radar equation with real numbers, and compares the frequency bands, radar types and applications you will meet in practice.

What Is Radar? How It Works, Frequency Bands, Types, and Uses

Network topology is the shape of a network: which nodes connect to which, and which paths data can take between them. This guide covers the seven types, how they compare, why the physical and logical views differ, and how to choose one for IoT and industrial systems.

Network Topology: 7 Types and How to Choose One

PLC and SCADA, HMI Equipment for Industry 4.0

In this article, we'll see how PLC and SCADA systems differ, how they work together, and which protocols, scan times and security controls matter in industrial automation.

PLC and SCADA: Key Differences and How They Work Together

Programmable Logic Controller (PLC) vs Distributed Control System (DCS)

This guide compares PLC vs DCS architecture, scan times, redundancy, scalability, programming, costs, and applications to help you choose the right industrial control system.

PLC vs DCS: Key Differences and How to Choose

This article is a practical PLC programming guide covering the five IEC 61131-3 languages, the scan cycle that governs how your code actually runs, ladder and structured text examples, and the mistakes that cost engineers the most commissioning time.

PLC Programming: Languages, Scan Cycles, and Working Ladder Logic

An STM32 development board (left) alongside an ESP32 development board (right)

STM32 vs ESP32 compared on architecture, Wi-Fi, power, peripherals, toolchains, safety, and longevity. Current 2026 specs from vendor datasheets, plus a decision table.

STM32 vs ESP32: Choosing the Right Microcontroller for Your Design

This article explains what is a PLC, what's inside it, how the scan cycle works, which programming languages the current IEC standard defines, and how to choose the right controller for your application.

What Is a PLC? A Complete Guide to Programmable Logic Controllers

Nearly every modern device—from smartphones and Wi-Fi® routers to aircraft navigation systems and deep‑space telescopes—relies on one technology: radio frequency (RF). Though invisible, RF energy supports daily activities such as communication and navigation. RF systems are built on components, circuits, and physical principles that work together to encode, move, and decode information.This blog explores how RF signals transmit information and how components and interconnects determine system performance.<h2>Turning Information into Electromagnetic Motion</h2>At its core, RF communication is the art of imprinting information into electromagnetic waves and transmitting them across space or along guided structures. These waves can span a staggering range—from a few kilohertz to hundreds of gigahertz—yet all follow the same basic principles.To send information, RF systems begin with a baseband signal: audio, data, or another low‑frequency source. This baseband signal is then modulated: Its characteristics are used to control the amplitude, frequency, or phase of a higher‑frequency RF carrier. Modulation lets signals ride on frequencies suited for long‑distance transmission or wide‑area coverage.For decades, communication engineers used superheterodyne architectures, shifting signals up through one or more intermediate frequency (IF) stages using mixers and local oscillators. This approach enabled precise filtering and high‑performance receivers. Today, however, high‑speed analog‑to‑digital converters (ADCs) and digital‑to‑analog converters (DACs) enable many systems to generate and sample RF signals directly, bypassing some analog stages entirely. This fully or near‑fully digital approach simplifies certain architectures while raising the bar on circuit quality, noise performance, and timing accuracy.Regardless of architecture, every RF link depends on signal fidelity. A receiver must reconstruct the original information even when noise, multipath reflections, interference, and component imperfections try to corrupt it. Therefore, RF communications rely heavily on filtering, amplification, shielding, and error‑correction techniques. Because of this reliance, the hardware behind the signal is highly important.<h2>Exploring the RF Signal Chain</h2>RF communications succeed or fail depending on the components that handle signals along their path. These parts, collectively called the RF signal chain, perform functions such as amplification, filtering, mixing, splitting, frequency generation, and conversion.<h3>Active Components</h3>Active devices require external power and often perform signal‑altering functions. Examples include amplifiers, oscillators, mixers, modulators, and frequency synthesizers. These components determine the strength, stability, and spectral cleanliness of an RF signal.<h3>Passive Components</h3>Filters, couplers, power dividers, power combiners, baluns, phase shifters, waveguides, and connectors manipulate signals using the energy passing through them. Though passive, these components are critical. A poorly designed filter or lossy transmission line can dismantle a communication system long before an amplifier ever sees the signal.<h3>Component Quality Considerations</h3>Every RF component introduces some combination of noise, loss, nonlinearity, or impedance mismatch. A low‑noise amplifier (LNA) at the front end of a receiver can significantly improve performance. However, any noise or interference introduced first will be amplified along with the desired signal. Conversely, interference entering later in the chain may have far less impact. This behavior demonstrates how design decisions are critical to overall output.<h2>Focusing on RF Interconnects</h2>Transmission lines, connectors, waveguides, and printed circuit board (PCB) traces carry signals between circuit blocks, and their behavior can significantly affect system performance.At high frequencies, an interconnect is not just a wire: It is a distributed electromagnetic structure with its own impedance, loss characteristics, dispersion, and susceptibility to noise. A connector mismatch can introduce reflections. A PCB trace with poor impedance control can radiate, couple into nearby components, or distort high‑frequency waveforms. Even routing geometry—trace length, distance to the ground, and whether it crosses a digital clock line—can change the signal.Interconnects also act as possible entry points for interference. Any unwanted energy at a mixer input can produce intermodulation distortion, generating new frequencies not present in the original signal. In dense RF environments with many nearby channels, this distortion can completely desensitize a receiver.Because of these factors, RF engineers must pay close attention when choosing connectors, cables, PCB materials, amplifiers, and oscillators.<h2>Aligning RF Communications and RF Hardware</h2>As wireless systems push into higher frequencies, wider bandwidths, and more densely packed spectra, both RF communications and RF hardware become more challenging. The need for cleaner signals, lower noise, and better isolation grows—especially as digital electronics and software‑defined radio architectures place even more pressure on the analog front end.RF communication techniques alone cannot overcome poor hardware. Likewise, every signal chain must conform to the modulation schemes, frequency allocations, and system constraints set by communication protocols. The two domains are inseparable; advancements in one domain inevitably demand improvements in the other.<h2>Conclusion</h2>RF communications power everything from everyday wireless connections to advanced sensing technologies. Behind every crisp signal lies an intricate network of circuits, components, and interconnects that determine how well information is transmitted. From basic communication to advanced sensing, RF systems combine physics, clever circuitry, and sophisticated digital processing to detect and interpret electromagnetic signals. Understanding how these elements work and interact helps demystify the invisible systems that keep the world connected.

Explore RF signal chains, components, and interconnects, and learn how amplification, filtering, impedance, and signal integrity shape RF system performance.

RF Signal Chain Components and Interconnect Fundamentals

As artificial intelligence (AI) advances, many industrial applications are shifting toward edge or hybrid architectures to reduce constant reliance on cloud infrastructure. Edge AI deploys AI models on local devices rather than relying exclusively on data centers or the cloud, which can improve data privacy and security when paired with appropriate device-level protections. Edge AI can also support lower latency, reduced network bandwidth, increased automation capabilities, and, depending on the architecture, lower overall energy use.This week’s New Tech Tuesdays explores the evolution of human-machine interfaces (HMIs) amidst the growing role of edge AI in industrial systems. It highlights the core capabilities of HMIs, implementation challenges, real-world applications in manufacturing and logistics, and new technologies that support low-latency processing at the edge.<h2>Understanding Human-Machine Interfaces</h2>Historically, automated machines were monitored through direct, manual observation, requiring workers to closely watch equipment for signs of defects. Today, HMIs allow users to oversee machine performance at a glance, displaying alerts and diagnostic information so issues can be identified immediately rather than through constant monitoring that still runs the risk of oversight.In industrial environments, HMIs are often used to monitor machines, track data, and anticipate problems. Centralized data monitoring helps users spend less time on manual checks.When HMIs incorporate edge computing and AI capabilities, they transition from passive display tools into local decision-making hubs. By processing data locally, edge-integrated HMIs can reduce latency, lower bandwidth requirements, improve data privacy, and support more reliable real-time decision-making. In quality inspection processes, for example, edge-enabled HMI platforms can process or display local AI inspection results, helping detect failures, improve operational efficiency, and enhance automation.<h2>Key Edge AI Implementation Challenges</h2>In industries such as manufacturing, edge AI can assist with vision systems, automated operations, and predictive maintenance. However, companies must have sufficient edge computing infrastructure in place. Engineers must monitor a wide range of operational metrics, including:<ul><li>Resource utilization</li><li>System delays and latency</li><li>Energy consumption</li><li>Data privacy protocols</li><li>Model accuracy</li></ul>The interconnectivity of components, such as Industrial Internet of Things (IIoT) devices, communication links, and end nodes (e.g., cameras), requires each part of the system to be reliable. A malfunctioning component can affect system performance, reduce model accuracy, or interrupt local decision-making unless the system is designed with redundancy or fallback behavior.In logistics use cases, environmental conditions present unique challenges. Sensors can detect alerts based on temperature, humidity, and shock data monitoring. Still, companies must design, test, and monitor tools based on their specific operational environment to ensure success.<h2>Edge AI in Action: Practical Use Cases</h2>The effects of edge AI, such as improved efficiency and low latency, are invaluable in many industries. Researchers have found that, on average, a large manufacturing plant loses around 253 million USD per year due to unplanned downtime. Predictive maintenance, including edge AI-enabled implementations where local processing is required, can help reduce some downtime-related losses.In one example, Hitachi Rail adopted an edge platform to process sensor and camera data in real time. The platform is designed to help identify tracks that need repair, monitor overhead power-line degradation, and assess the health of trains and signaling equipment. The railway found that edge AI helped reduce service delays by up to 20 percent, reduce train maintenance costs by up to 15 percent, and reduce fuel costs by up to 40 percent.In another example, a logistics operator deployed edge AI forklifts to support navigation and collision avoidance in a warehouse. As a result, safety incidents decreased and route efficiency improved. With effective testing and implementation, AI systems can support fleet management while improving accuracy and latency.<h2>The Newest Products for Your Newest Designs®</h2>The <a href="https://www.mouser.com/new/nxp-semiconductors/nxp-frdm-imx-95-dev-board/" target="_blank">NXP FRDM-i.MX 95 development board</a> is designed for AI acceleration and connectivity for edge tools. The board also provides software enablement and use-case demos. Additionally, the board supports HMI systems, vision processing, and industrial networking.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1790639078922-FRDM-IMX95-ISOMETRIC.jpg" class="fr-fic fr-dib" />Figure 1: NXP Semiconductors FRDM i.MX 95 development board is based on the powerful i.MX 95 applications processor in a 15mm × 15mm package. (Source: Mouser)<img alt="" src="https://mouser.bynder.com/asset/d6c407a0-7e78-40e9-a8ee-70dfb1955faf/Large/107656674.png" class="fr-fic fr-dib" />With its Arm® Cortex® -A55 multicore complex, Arm Cortex-M33, Arm Cortex-M7, and eIQ® Neutron neural processing unit (NPU), the FRDM-i.MX 95 board is designed for low-latency processing. In industrial contexts, the board supports automation, visual safety inspection, and real-time monitoring.<h2>Conclusion</h2>Companies looking to improve efficiency, detect failures earlier, reduce latency, lower bandwidth demand, and improve data privacy should evaluate edge AI where local processing aligns with their operational requirements. When these tools are implemented with edge computing infrastructure and evaluated for success, they can help companies reduce costs, gain efficiency, and stay competitive in the evolving industrial landscape.

Discover how edge AI and HMIs improve industrial efficiency with low-latency processing, predictive maintenance, and smarter automation.

Edge AI Is Reshaping Industrial Efficiency

As of 2025, approximately 600 million people—nearly half the population of Africa—still lack reliable access to electricity[1]. And, with more than 30 countries across Africa experiencing regular power shortages and interruptions in service[2], the continent continues to face significant barriers to achieving universal energy access. Improved data visibility allows utilities to better manage the grid and identify issues quickly and efficiently. “Utility providers across Africa are gradually moving towards the implementation of smart metering solutions,” explains Joseph Wamicha, CEO, GridsMesh (formerly Sustainable Energy Technologies) – a South African design and manufacturing company that has developed a smart metering solution for use in Africa. “Currently, most utilities are using cellular connectivity as a means of transmitting the recorded data, while others are opting for Power Line Communication [PLC G3].”Cellular-based meters enable long-range communication with base stations, which then transmit data to the Cloud. However, this method relies on SIM cards, which can be challenging to manage at the scale required for smart metering solutions. Additionally, widespread connectivity blackspots across Africa can disrupt data transmission.Power Line Communication (PLC) provides an alternative, using existing electrical wiring to send metering data to Data Concentrator Units (DCUs), which then forward it to the Cloud. But this approach also faces limitations, including poor signal quality and interference from ageing infrastructure. Frequent power outages, both scheduled and unscheduled, further impact communication reliability – especially since meters can take a considerable amount of time to reconnect once power is restored.“The foundation of smart metering is built upon dependable communication,” says Wamicha. “The broad range of wireless technologies available today – including LTE, NB-IoT, PLC G3, PLC+RF and Wi-SUN – provides utilities with many options. However, we have found that none of these are currently able to deliver the level of reliability required for effective smart metering across Africa.”<h3>Reliable connectivity through mesh networking</h3>To help meet these needs, GridsMesh has designed the ‘Pawane Energy App for Africa’,  a smart metering solution based on DECT NR+ (NR+), using a Wirepas RF mesh network. Operating independently of traditional telecom providers, this mesh technology offers significantly lower operational costs while delivering the high reliability required for large-scale deployments. The decentralized, self-healing design eliminates single points of failure, ensuring consistent performance even if individual nodes go offline.“Many areas, particularly in Africa, suffer from poor cellular network coverage,” says Wamicha. “With NR+, smart meters extend and strengthen wireless network coverage. In urban environments each smart meter can connect with over 50 neighboring meters, and a singular cellular subscription can be shared across 1,000 smart meters.”The solution is powered by a Network Interface Card (NIC) featuring Nordic’s <a href="https://www.nordicsemi.com/Products/nRF9151">nRF9151</a> low-power module, which has been purpose-built for DECT NR+ and cellular IoT applications. Supporting the Wirepas NR+ stack, it enables secure, scalable data transmission across the network. NR+ technology allows utilities to operate their own private 5G network – eliminating the need for base stations or SIM cards. In addition, the module features 1 MB of non-volatile memory and 256 KB of RAM, providing the capacity to support complex metering applications. References“Traditional cellular smart meters typically send data overnight, or a few times per day, because of the cost of data,” Wamicha continues. “This lacks ‘real time’ visibility. With NR+, data can be transmitted every minute without a significant increase in cellular subscription fees.” Designed for use by electricity distribution utility companies – including municipal, metropolitan, and private providers – as well as smart city projects and meter manufacturers, the solution eliminates the need for manual meter reading, significantly lowering operational costs. Cape Town-based technology consultancy Pentafortis is supporting GridsMesh with business and partnership development across key regions – identifying meter manufacturers for integration, building local partnerships, and driving sales and commercial initiatives.“This system has been created to help improve energy efficiency and enhance grid stability through dynamic load balancing and demand response optimization,” says Wamicha. “It also empowers consumers to better monitor and manage their energy usage.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1792479458823-1792479458823.png" class="fr-fic fr-dib" />The solution is powered by a Network Interface Card (NIC) featuring Nordic’s nRF9151 low-power module, which has been purpose-built for DECT NR+ and cellular IoT applications<h3 id="isPasted">Excellent transmission power</h3>With powerful transmission capabilities, the nRF9151 enables long-range communication, even in areas with weak signal conditions, with mesh networking for robust coverage and high receiver sensitivity for detecting distant signals. The nRF9151 surpasses its predecessor with support for Power Class 5 (20 dBm output power), alongside the existing Power Class 3 (23 dBm). “Nordic Semiconductor’s and Wirepas’ proven expertise and technology enables us to deliver robust, scalable, and self-healing network connectivity for smart meters,” says Wamicha. During development, the <a href="https://www.nordicsemi.com/Products/Development-software/nRF-Connect-SDK">nRF Connect SDK</a> significantly streamlined development with its pre-built libraries, security features, and optimized communication protocols.“This combined with Nordic’s strong technical support and reference designs, fast response times from application engineers, and reliable supply chain ensuring component availability, reduced our costs and accelerated time to market.”Adopting the right technology can play a pivotal role in advancing smart energy transformation across key regions. It enables energy providers to deliver reliable performance and seamless connectivity, while allowing consumers to monitor and better understand their energy usage.<h3>References</h3><ol><li>United Nations Sustainable Development Group: Decoding Africa’s Energy Journey: Three Key Numbers.</li><li>Africa Infrastructure Knowledge Program: Africa’s chronic power problems have escalated into a crisis affecting 30 countries. This tolls heavily on economic growth and productivity.</li></ol>Nordic Semiconductor’s and Wirepas’ proven expertise and technology enables us to deliver robust, scalable, and self-healing network connectivity for smart meters

GridsMesh's DECT NR+ smart metering solution provides reliable transmission over a Wirepas RF mesh network

Smart metering solution designed for use across Africa captures key utilities data

We got to play around with the F1 Starter Kit, built around the F1 Smart Module from SG Wireless, a module which encompasses 4 different radio technologies: cellular, LoRa, Bluetooth Low Energy and WiFi.We were given the challenge to build something, while using multiple technologies from within the module. The idea was quickly set: build a tracking application that combines Wi-Fi and cellular, with the option to add BLE beacons at a later stage.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAxOTY0NTE5LUlNR18zMTQ5IC0gMjIwMHB4LmpwZWciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:812px" class="fr-fic fr-dib" />Unboxing the F1 Starter KitHere's what we built, and what we experienced along the way.<iframe width="640" height="360" src="https://www.youtube.com/embed/H-LdJrk9eZ4?&amp;wmode=opaque&amp;rel=0" frameborder="0" class="fr-draggable"></iframe> <h3>Pycom’s heritage</h3>Remember the days of Pycom? Famous for its <a href="https://www.kickstarter.com/projects/pycom/lopy-the-lora-wifi-and-bluetooth-iot-development-p" target="_blank">Kickstarter campaigns</a> and its huge community of MicroPython developers. The successful Kickstarter led to the LoPy, the LoRa, Wi-Fi and Bluetooth IoT board.Fast-forward 6 years. By then, their latest module was developed comprising LoRa, Sigfox, WiFi, BLE and Cellular (LTE-M and NB-IoT), but reality kicked-in. <a href="https://www.seasongroup.com/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank">Season Group</a>, a Hong Kong-based electronics design and manufacturing company, came to the rescue and acquired Pycom's technology and its engineering team in late 2022. The assets and expertise were brought into Season Group’s IoT-focused subsidiary: <a href="https://sgwireless.com/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank">SG Wireless</a>. The module Pycom was working on at the time, based on Espressif’s ESP32-S3, eventually became the F1 Smart Module.Today, SG Wireless combines Pycom’s developer-focused heritage with Season Group’s expertise in hardware and firmware development, certification and manufacturing, helping companies take IoT products from initial prototype to production. <h3>The F1 Smart Module and the F1 Starter Kit</h3>F1 Smart Module supports four connectivity options: WiFi, Bluetooth, LoRa and Cellular (LTE-M and NB-IoT). The module is pre-certified, programmable in MicroPython, native C and soon with the Arduino IDE. It's built for rapid prototyping and easy scaling. The flagship module comes with all four radios, but there are also<a href="https://sgwireless.com/product/f1-c-cellular-module/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank"> a cellular</a>, BLE and WiFi version and<a href="https://sgwireless.com/product/f1-l-lora-module/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank"> a LoRa</a>, BLE and WiFi one.The F1 module is built around Espressif's ESP32-S3: a dual-core 32-bit Xtensa LX7 running at up to 240 MHz, with 384 KB ROM and 512 KB SRAM on chip, plus 8 MB PSRAM and 16 MB flash on the module. In deep sleep the MCU draws around 10 µA.Learn more at <a href="https://sgwireless.com/the-f1-smart-module/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank">SG Wireless’ website</a>.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAyMTYwMDE3LUYxIFNtYXJ0IE1vZHVsZS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" />The F1 Smart ModuleThe F1 Starter Kit is the Arduino-compatible devkit, with the F1 Smart Module on board. It comes with access to Ctrl, SG Wireless' device management platform for firmware updates as well as data storage and visualization.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAyMTg0MzM5LUlNR18zMzQyIC0gMTYwMHcuanBlZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" class="fr-fic fr-dib" />The F1 Starter Kit<h3>Wi-Fi tracking, but serverless</h3>The goal: track the location of the device without adding any extra sensors or peripherals. So-called WiFi sniffing is a great way of localizing assets as it – other than GPS – works indoors, and doesn’t require long and time-consuming satellite scans. To do so, the device scans for Wi-Fi access points and sends their unique addresses (BSSIDs), together with their signal strengths (Received Signal Strength Indicators or RSSIs) to a cloud platform. From there, an API call is made to a database that returns the location, including an accuracy radius.This had to work seamlessly, without me having to run a server myself. <h3>Architecture</h3><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAyMjc1NjIyLURpYWdyYW0tMi5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" /><ol><li>Scan. The F1 wakes up at preconfigurable time intervals, currently set to 15 minutes, and scans for nearby Wi-Fi access points. It keeps up to 6 strongest BSSIDs and drops randomized addresses (such as phone hotspots).  To avoid sending data while the device hasn't moved, it compares the BSSIDs with those of the last message sent. A new message is sent only when at least 50% of them have changed. If nothing changes, the device still sends an uplink at least once every 6 hours, so we can tell a stationary device from a dead one.  </li><li>Send. It connects over cellular using the <a href="https://soracom.io/" target="_blank">Soracom</a> SIM and sends the list of access point addresses and signal strengths to <a href="https://tago.io/" target="_blank">TagoIO</a> in a UDP message. Once an ACK is received, the module goes to sleep. </li><li>Resolve. In TagoIO, each received data message triggers a script, which sends the addresses and signal strengths to the <a href="https://developers.google.com/maps/documentation/geolocation/overview" target="_blank">Google Geolocation API</a>. </li><li>Store. Google returns the latitude, longitude and accuracy radius. The script writes these back to TagoIO, where they are stored. </li><li>Show. A TagoIO dashboard shows the device’s location on a map, including its accuracy.</li></ol><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAyMzIzMjUxLVNvcmFjb20ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width:100%px" class="fr-fic fr-dib" />Soracom ConsoleSoracom is used as the connectivity provider, enabling the F1 Smart Module to connect over LTE-M. The Soracom SIM comes with access to a centralised management platform and a console the monitor the connection. The same platform is designed to support connected devices as they move from prototyping into larger-scale deployments.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzkyNDAyMjg4NTMzLVRhZ29JTy5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width:100%px" class="fr-fic fr-dib" />TagoIO dasbhoardTagoIO is a full-stack IoT cloud platform that supports light-weight, low-bandwidth data transmissions to build end-to-end applications. Apart from data storage and visualization, it comes with the ability to run scripts to automate data processing.  <h3>Conclusion</h3>It works, better than I expected.  The existing tools let the device run by itself, without me having to run any servers with impressive accuracy. With just 3 access points, we already get a location within 20 to 50m. More access points bring that down to less than 20m. Impressive!The messages are small. Each access point adds about 22 bytes, so a full message is roughly 150 to 250 bytes, depending on how many access points we include. With some minor adaptations, the payload can be further reduced to make it work with LoRaWAN, which allows up to 222 bytes per message.Only send when it moves. Currently, the application scans at a configurable time interval. When the device is stationary for a while, it keeps scanning for WiFi networks and comparing them to the previous set, which drains the battery. A better way is to add an accelerometer that triggers the Wi-Fi scan. As long as no movement is detected, the device stays in deep sleep, consuming as little energy as possible.The other radios allow for further application development. We only used Wi-Fi and cellular, the same module could:<ul><li>Use Bluetooth beacons for more precise location, indoors</li><li>Use LoRaWAN as a fallback when there is no cellular coverage (or vice versa)</li><li>Fall back to the cell tower location when there aren't enough Wi-Fi networks around, using a database like <a href="https://www.opencellid.org/" target="_blank">OpenCelliD</a></li></ul><hr />The F1 Starter Kit is a great platform for prototyping, giving plenty of directions to pursue due to the multi-radio setup. The kit is widely available and the documentation gets you started quickly. The Pycom community has been dormant for the past few years, but with the F1 Starter Kit, along with SG Wireless’ efforts to activate <a href="https://forum.sgwireless.com/?utm_source=blog1&amp;utm_medium=referral&amp;utm_campaign=stars2610" target="_blank">the forum</a>, we may see part of the community revive.The bigger promise of the F1 Starter Kit is a seamless transition from prototyping to production where the same module can be used in both stages. SG Wireless’ expertise in IoT, combined with Season Group’s capabilities in hardware development, certification and manufacturing, should lead to a seamless process to get ideas to market. But, let’s dig into the process to optimize the WiFi-based tracker and get it ready for production. A new article is coming up that describes the next steps of moving a prototype to production. Stay tuned! Laurens Slats

The F1 Starter Kit, built around the F1 Smart Module which supports cellular, LoRa, BLE and WiFi

Tracking without GPS: hands-on with the F1 Starter Kit

IoT solutions company, Sentinum, has released a cellular IoT-powered asset tracking and condition monitoring solution built on Nordic Semiconductor’s <a href="https://www.nordicsemi.com/Products/nRF9151">nRF9151</a> low power SiP module and <a href="https://www.nordicsemi.com/Products/nRF7000">nRF7000</a> Wi-Fi 6 Companion IC. The ‘Juno Cellular Tracker’ is designed to provide continuous insights on the location, handling events, and operational status of mobile or distributed assets. Juno integrates a three-axis MEMS accelerometer to provide three key sensing functions—motion, tilt, and detection of opening or tampering—that support activity-based tracking, misuse detection, and automated event logging without the need for additional components. ‘TH’ model versions of the Juno solution also include a high-accuracy temperature and humidity sensor to deliver accurate readings of surrounding conditions for environmental monitoring. The sensors are supervised by the integrated Nordic nRF9151 module’s 64 MHz Arm® Cortex®-M33 dedicated application processor, including 1 MB Flash and 256 KB RAM.<blockquote>By adding Wi-Fi scanning through the nRF7000, we can offer faster, more consistent, and more power-efficient location updates, which directly translates into longer battery life and more reliable tracking data for our usersManuel Hart, Co-Founder and General Director of Sentinum GmbH</blockquote><h3 id="isPasted">nRF9151 cell-based locationing</h3>To complement its condition sensors, Juno employs hybrid positioning technologies optimized for indoor and outdoor environments. SSID-based Wi-Fi scanning is used to capture nearby MAC (media access control) addresses and resolve position with 2–50 m accuracy, ideal for indoor or dense urban areas. GNSS provides precise outdoor positioning and is used selectively to minimize energy consumption. When Wi-Fi and GNSS are unavailable or when ultra-low-power operation is required, cell-based locationing is used to estimate position. Using the nRF9151’s multimode LTE-M/NB-IoT modem and GNSS, the Juno device can operate on cellular networks for reliable, wide-area data transmission without requiring local gateways or additional infrastructure. The Juno Tracker’s intelligent firmware automatically selects the most efficient locationing method, switching between Wi-Fi or network-based indoor positioning and GNSS outdoors. Nordic’s <a rel="noopener noreferrer" href="https://nrfcloud.com/#/" target="_blank">nRF Cloud powered by Memfault</a> plays a central role in the Juno system architecture by providing a secure, scalable, and standardized backend for device management and geolocation processing. By leveraging Nordic’s infrastructure, Sentinum can offload complex operations from the device, reduce power consumption, and streamline fleet management for its customers.For positioning, Juno devices transmit GNSS and Wi-Fi scan data directly to nRF Cloud, where the final location is resolved using Nordic’s global databases. This cloud-based approach eliminates the need for power-intensive processing on the device and ensures high accuracy even in challenging environments. <blockquote>“When designing the Juno Tracker, our priority was to build a compact, low-power device capable of running advanced tracking and sensor logic autonomously over long periods without maintenance,” explains Manuel Hart, Co-Founder and General Director of Sentinum GmbH. </blockquote><blockquote>“The Nordic nRF9151 was the only cellular SiP module that could meet all these requirements in one platform. Its powerful processor and generous memory give us enough headroom to run our hybrid positioning algorithms, event detection logic, and firmware over-the-air update [FOTA] functionality directly on the device – without needing an external MCU.”</blockquote><h3>Power-efficient location updates with Wi-Fi scanning</h3>In addition to the nRF9151, the Juno Tracker employs the Nordic nRF7000 Wi-Fi Companion IC. With a primary focus on active and passive scanning of Wi-Fi access points, the nRF7000 offers a reliable and power-efficient solution in location-based applications. It is designed specifically for SSID-based Wi-Fi locationing across both 2.4 GHz and 5 GHz bands, which gives the Juno Tracker the ability to locate itself even inside buildings or dense urban areas.<blockquote>“We chose to integreate the nRF7000 Wi-Fi Companion IC because it solved one of the main limitations of GNSS-only tracking - maintaining accurate positioning in indoor or signal-obstructed environments without draining the battery,” says Hart. “By adding Wi-Fi scanning through the nRF7000, we can offer faster, more consistent, and more power-efficient location updates, which directly translates into longer battery life and more reliable tracking data for our users.”</blockquote>With optimized settings - such as one daily uplink and one daily GNSS fix—the tracker can operate for multiple years, even under challenging conditions like weak cellular coverage or limited satellite visibility. In higher-activity modes, users can still expect long-lasting performance without the need for recharging, according to the company.<blockquote>“A key reason we selected the nRF9151 SiP was its exceptionally low-power architecture. The integrated MCU and modem allow us to precisely control every subsystem’s sleep and wake cycles, ensuring the device remains idle most of the time and only activates when events or scheduled reports require it,” says Hart.</blockquote>The Juno Tracker is fully integrated into Sentinum’s sensor-to-cloud ecosystem, which includes the company’s dedicated tracking platform, ‘Orbix’. Data is processed and visualized through the ‘Sentinum Dashboard’ (built on the enhanced ‘ThingsBoard’ platform) or can be integrated directly into any third-party platform or customer-preferred software via standard APIs. This ensures reliable, scalable asset visibility within existing operational systems.“Overall, we chose Nordic Semiconductor not only for its low-power cellular technology but also for the strength of its development ecosystem,” says Hart. “The nRF91 Series gave us the energy efficiency we needed for multi-year battery life, and the <a href="https://www.nordicsemi.com/Products/Development-software/nRF-Connect-SDK">nRF Connect SDK</a> (Sapphire) made firmware integration and FOTA updates straightforward.”<a href="https://sentinum.de/" class="button-main-action">ABOUT SENTINUM</a>

Sentinum;s Juno Cellular Tracker uses Nordic nRF9151 low power SiP module, nRF7000 Wi-Fi 6 Companion IC and nRF Cloud powered by Memfault to provide reliable connectivity, location and sensor data reporting

Nordic-powered cellular IoT and Wi-Fi asset tracker provides location, handling and status insights indoors or outdoors

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