Energy Efficiency in IoT Devices

Edge capability and conditional transmission ... How edge computing on LPWAN devices extends the battery life by factor of 4 As industrial IoT systems continue to scale across critical infrastructure—pipelines, reservoirs, remote assets, and urban utilities—one question persists across all engineering teams: "How do we make the device smarter without draining the battery faster or make the firmware more complex?" The answer is not in more power—it’s in more intelligence at the edge. > What Is #EdgeCapability in #LPWAN Devices? Edge capability refers to the ability of the device to process and analyze data locally, before deciding whether to transmit it over the network. This is a critical advancement in the design of battery-powered LPWAN devices—whether #LoRaWAN, #NB-IoT, or #LTE-M. Instead of blindly transmitting data at fixed intervals, smart edge devices evaluate conditions such as: - Threshold violations (e.g., pressure above X bar) - Anomalous patterns (e.g., sudden temperature spike) - Predictive failure signals (via trend detection) Only when action is needed, do they transmit. > Why Conditional Transmission Changes the Game Let’s take a real-world example from our deployments at Ellenex: - Scenario A: Traditional Mode Transmit every 15 minutes (fixed schedule) 96 transmissions/day Average battery life: < 1 year - Scenario B: Edge Mode with Conditional Transmission Sample every 5 minutes Transmit only when threshold conditions are met or at max once per day 1–5 transmissions/day depending on conditions Average battery life: 3.5–4 years By eliminating unnecessary network sessions, power-hungry radio activations, and overhead from MAC layer interactions, energy usage drops dramatically. > Implications for Industrial Use Cases Water Utilities can detect leaks without flooding the network with data. Smart Agriculture devices react only to critical soil moisture levels, not morning dew. Asset Monitoring for pressure, level, vibration, or flow becomes cost-effective in remote areas. And most importantly: maintenance intervals are extended dramatically. Battery replacements become rare events, not monthly line items. > What This Means for Product Designers When we design LPWAN devices at Ellenex, edge intelligence is not optional—it’s a core requirement. Every mA-hour counts. We, at Ellenex Industrial IoT, design products with: - Smart wakeup logic - Configurable edge thresholds - Modular firmware to enable OTA updates of local logic Because the edge is not just about faster insights—it’s about operational viability. Final Thought Nowadays, data is only valuable when it's actionable—and battery life is only long when data knows when not to leave the device. Edge capability + conditional transmission provides longer life, smarter systems, and scalable deployments. If you're still pushing data every 15 minutes—it is time to re-think 🤔 . #monitoring #IoT #ellenex #EdgeComputing #LPWAN #batterylife

🔋 The Unsung Hero of IoT Devices: Power Management Done Right When discussing IoT devices, most people focus on connectivity (Wi-Fi, BLE, LoRa), sensors, and firmware features. But the factor that truly determines whether your product succeeds in the field is power management. Without it, even the most advanced design can fail. ⚡ Why Power Management Matters A typical ESP32-CAM or Wi-Fi-based IoT board can draw 120–240mA during active transmission. Without optimization, a standard Li-ion battery may last just a few hours. With sleep modes, efficient power regulation, and smart duty cycling, you can extend runtime to days, weeks, or even months depending on the use case. 👉 In IoT, battery life = product usability. 🔑 Practical Power Optimization Strategies ✅ Use Deep Sleep Aggressively Most MCUs (ESP32, STM32, RP2040) can drop to tens of µA in deep sleep. Wake up, capture data, send it, then return to sleep. ✅ Choose the Right Regulator Select low-dropout regulators (LDOs) with low quiescent current. For battery applications, a switching regulator (buck converter) often provides higher efficiency than an LDO. ✅ Design PCB for Power Efficiency Place decoupling capacitors close to high-current devices like radios and cameras. Separate analog and digital grounds to minimize noise and wasted power. ✅ Select Communication Wisely Wi-Fi: High power, short bursts — best for images or larger data packets. BLE / Zigbee / LoRa: Much lower average power — ideal for periodic sensor data. 📊 Real-World Lesson In one IoT monitoring project: Initial design (Wi-Fi + no sleep): battery drained in under a day. Optimized design (deep sleep + burst Wi-Fi + efficient regulator): runtime extended to several weeks on the same cell. That’s the difference between a prototype and a deployable solution. 🎯 Key Takeaway Power management is not an afterthought — it’s the foundation of IoT design. Great firmware and sensors don’t matter if your device shuts down after a few hours. 👉 For every IoT or embedded project, treat power as a first-class design parameter — plan for it, measure it, and optimize it. 💬 What’s the biggest challenge you’ve faced in low-power IoT design? Let’s share real-world lessons 👇 #IoT #EmbeddedSystems #PowerManagement #ESP32 #PCBDesign #LowPowerIoT #FirmwareDevelopment #ElectronicsEngineering #HardwareDesign #EmbeddedEngineering #IoTDevelopment Disclaimer:This image was generated using AI for illustrative and educational purposes only. While it represents engineering concepts at a high level, certain technical details, dimensions, or component behaviors may not be fully accurate. Always consult official datasheets, manufacturer documentation, and domain experts before making design, hardware, or firmware decisions. This content is intended to raise awareness and share general insights, not to replace professional engineering guidance.

There's a temptation in IoT to push out more data just because you can - the assumption being more updates equal better insights. But the real win isn’t in collecting data; it’s knowing when to send it. This challenge isn’t just a software problem or a hardware problem - it’s both. You might ship a device with excellent low-power hardware, yet the software doesn’t fully leverage its capabilities. Or the hardware might be built assuming constant connectivity because the team hasn't set clear thresholds for when data actually matters. The result? Unnecessary data transfers, wasted battery, and system underperformance.  The best-performing IoT products get it right from the start. Hardware and software teams align early on: ↳ What data truly matters? ↳ What can be processed at the edge? ↳ And when should the data be transmitted? By shifting from real-time updates to time-based or event-driven updates, devices can power down connectivity during idle periods - we’ve seen power savings of 50% in some cases.  Looking ahead, AI-driven scheduling will be a major focus in 2025. We’re going to use software to smartly predict the best moments to transmit - balancing power constraints with real-world   conditions - while not overcomplicating algorithms that operate at the edge.  Ultimately, it’s not about constant connection - it’s about constant awareness. Deliver the right data at the right time, and your devices will last longer, perform better, and yield smarter insights. Like, Comment or Follow for more IoT insights.

At IoT Stars 2025 in Nuremberg, we unveiled the RAK11160 and shared the story behind why we built it. The idea came from a clear pattern we kept seeing: More than 60% of our RAK3172 users were also integrating ESP32 into their designs, just to add MQTT, OTA updates, or BLE provisioning. That meant extra components, extra cost, and more power consumption. That’s exactly what inspired our team at RAKwireless  to build the RAK11160. This isn’t just another LoRaWAN module. It's our first dual-core wireless MCU, blending: - STM32WLE5 for LoRa + ultra-low-power sensing - ESP32-C2 for on-demand WiFi and BLE These two cores don’t run in parallel. Instead, the STM32 manages system logic and keeps power consumption low by fully shutting down the ESP32, only waking it for BLE provisioning, WiFi-based OTA, or MQTT uplinks. This setup is great for #smartfarming , since the devices can stay asleep for days, send soil or crop data with LoRaWAN, and only use Bluetooth when a technician is close by. Read the blog: http://bit.ly/3ZARjkv Check out RAK11160: https://bit.ly/3TscMbF #IoT #RAK11160 #LoRaWAN #LowPowerDesign #STM32 #ESP32

In IoT and battery-operated embedded systems, power efficiency is often just as critical as processing capability. The ESP32-C6, a RISC-V-based MCU from Espressif, is equipped with multiple sleep modes that enable designers to finely control power usage depending on the system’s activity levels. This guide delves into the effective use of Modem Sleep, Light Sleep, and Deep Sleep modes on the ESP32-C6, providing practical code examples and configuration tips that utilize the ESP-IDF framework and FreeRTOS.

𝟵 𝗧𝗶𝗽𝘀 𝗮𝗻𝗱 𝗧𝗿𝗶𝗰𝗸𝘀 𝘁𝗼 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗕𝗹𝘂𝗲𝘁𝗼𝗼𝘁𝗵 𝗟𝗼𝘄 𝗘𝗻𝗲𝗿𝗴𝘆 𝗣𝗼𝘄𝗲𝗿 𝗖𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 🔋 In BLE-enabled devices, power efficiency isn’t just a feature—it’s a competitive edge. Whether you’re developing IoT gadgets, wearables, or smart home devices, reducing power consumption is key to ensuring longer device life and a better user experience. Here are 9 proven tips to help you optimize BLE power consumption effectively: 1. 🔄 Use Connection Intervals Wisely • Increase the connection interval to reduce the frequency of communication. This keeps the radio off longer, saving power. • 𝘛𝘪𝘱: Use intervals between 100-1000 ms for most low-data applications. 2. 💤 Leverage Sleep and Deep Sleep Modes • Ensure your MCU and radio enter sleep modes when not actively transmitting. • 𝘛𝘪𝘱: Configure sleep states carefully to avoid unexpected wake-ups. 3. 📡 Adjust TX Power and Range • Lower transmission power when close-range communication is sufficient. • 𝘛𝘪𝘱: Use the LE Power Control feature if your BLE stack supports it. 4. 📶 Minimize Advertising Frequency • Reduce advertising intervals when in a low-power state or when the device isn’t actively used. • 𝘛𝘪𝘱: Try a 1000-2000 ms interval for non-critical advertising. 5. 📋 Manage Notifications and Indications • Limit the frequency of notifications and only push data when necessary. • 𝘛𝘪𝘱: Batch data updates instead of sending them one at a time. 6. 🔧 Use Non-Connectable, Non-Scannable Advertising • When no active connection is needed, switch to non-connectable, non-scannable advertising. This prevents responding to scan requests, allowing the device to sleep immediately after sending advertising packets. • 𝘛𝘪𝘱: Use this mode for periodic broadcasts like beacons or telemetry data. 7. ⏳ Enable Peripheral Latency • Use Peripheral Latency to allow the peripheral device to skip connection events when no data needs to be transferred. This keeps the radio off longer between transmissions. • 𝘛𝘪𝘱: Choose a latency value based on your application’s tolerance for delayed responses. 8. 🔄 Use Data Caching and Local Processing • Perform local processing and data caching on the device to reduce how often data must be transmitted. This minimizes the need for frequent radio activations. • 𝘛𝘪𝘱: Process data locally when possible, and send aggregated data only when necessary. 9. 📶 Optimize Data Throughput • Use the most efficient BLE data modes, like LE 2M PHY, for fast data transfer when possible. • 𝘛𝘪𝘱: Send larger data packets to reduce protocol overhead and radio airtime. 𝗞𝗲𝗲𝗽 𝗶𝗻 𝗠𝗶𝗻𝗱: Small adjustments can translate into months or even years of extra battery life. Power optimization helps reduce maintenance costs and enhances customer satisfaction—a win-win scenario. 👉 What’s your go-to BLE power-saving trick? Share it in the comments! CC: Bluetooth SIG #BluetoothLowEnergy #BLE #IoT #PowerOptimization #EmbeddedSystems

This sensor harnesses energy from a wire’s magnetic field... Researchers from MIT have created an easy-to-install energy harvester that converts the magnetic field around ordinary wires into stored energy to power self-sustaining IoT devices. A lot of IoT equipment monitors run on batteries, which aren’t favored in industrial settings due to fire hazard or simply because of the cost of replacing worn-out batteries. Replacement is especially difficult in remote locations. The energy harvester is clamped around a wire carrying current, and uses electromagnetic induction to harvest energy from the magnetic field produced by the current. The energy is then stored in capacitors in the IoT device. But scavenging energy isn’t enough, too often, IoT devices are built optimistically. Designers assume that IoT devices will always be taking in enough energy to operate over the life of the sensor and that they will never face situations where they completely run out of energy and need restarting. Researchers found that the solution lies in using an energy-management system to carefully balance the needs of the energy harvester with the needs of the sensor. The system executes algorithms that determine when energy harvesting or data collection should be prioritized. The specific decisions that an energy-management system’s algorithms make will depend on the application. A sensor that monitors the health of a motor by measuring its vibrations, for example, may need to prioritize data collection at specific times, such as during motor spindown cycles – even if doing so may deplete the IoT device’s stored energy. And if an IoT device runs out of power completely, the energy-management system should have a way to perform a “cold start” once enough power has been harvested. The researchers’ prototype is able to do this because no power is needed to harvest magnetic energy; the device can continue to build up energy even after its capacitors are empty. Cold-start capability is especially important for building reliable IoT systems, because it ensures that running out of power won’t mean the device is “dead forever.” The researchers found that cold-start capabilities and algorithms that properly prioritize energy harvesting and data collection were key to resilient IoT systems. They developed additional design guidelines, and hope that other developers will be able to use their work as a blueprint and adapt it to their own specific applications. The guidelines can apply to any #iot device, regardless of the energy-harvesting method or sensor type. One of the biggest challenges in the self-powered IoT space is the gap between the energy supplied by typical energy harvesters and the energy needed to power a device. An energy-management system is a critical piece in making self-powered IoT systems possible… Daily #electronics from Asia insights – connect with me, Case, and never miss a post by ringing my 🔔. #technology #innovation

How to improve Energy Efficiency in Embedded Systems? 💤 Use Low-Power Modes: Leverage sleep or deep-sleep modes for idle times. 🔌 Minimize Peripheral Usage: Turn off unused peripherals to save energy. ⏱️ Optimize Clock Speeds: Dynamically scale clocks to balance power and performance. ⚡ Reduce Polling Loops: Use interrupts instead of continuous polling. 📉 Optimize Sensor Sampling: Lower sampling rates without compromising accuracy. 💡 Use Hardware Acceleration: Offload tasks to specialized hardware for efficiency. 📊 Measure Power Usage: Regularly profile power consumption to find optimization opportunities. Small tweaks can lead to big savings! Let's create systems that are both powerful and energy-efficient. 🌟 #EmbeddedSystems #LowPowerDesign #EnergyEfficiency #FirmwareDevelopment #Microcontrollers #IoT #GreenTech #HardwareOptimization #EmbeddedSoftware #DeepSleep #Interrupts #PowerManagement #SustainableTech #SystemDesign #TechInnovation