In the realm of IoT, where technology meets innovation, the IoT market is a dynamic landscape with solutions tailored to various verticals. This article delves into a specific IoT segment, often called Massive IoT or large-scale IoT. This realm encompasses sensors that churn out data in copious amounts, data that holds the key to insights and informed decision-making, and data that can drive transformative shifts in behaviour.
Unlocking the Power of Smart Sensors
In the ever-evolving world of IoT, one trend stands out prominently: smart sensors. When iot-analytics.com peered into the crystal ball for 2023, it revealed a compelling top trend and smart sensors took centre stage. Why, you ask? Because the benefits are substantial. Transmitting data wirelessly incurs direct and indirect costs that can be significant. These costs range from monetary expenses to escalating subscription fees and expanding storage costs in the cloud or servers. Moreover, they include the unwelcome guest of increased power consumption, a surefire recipe for shorter battery life and more frequent battery replacements.
The Quest for Efficient Energy Consumption
Energy consumption becomes paramount in sensor applications, especially when using a battery-operated device. In many cases, it’s imperative to ensure a device can run for over a decade on battery. The energy consumption factor is the holy grail of large-scale IoT, as it’s the ultimate selling point— “our device will last ten years on one battery.” This is a bar every solution must meet in the IoT realm. The question arises: how do we get there? The answer lies in techniques like data filtering, intelligent local data storage, and non-real-time data uploads, aligning perfectly with current market trends.
A Three-Step Model for Efficiency
The AKKR8 sensor, an IoT marvel we’ve designed, incorporates a relatively simple yet essential logic at the sensor level. It’s grounded in the understanding that addressing connectivity costs is most efficient when done close to the network’s edges. Our sensor employs data filtering as a core function. After each measurement, it makes a critical decision—discard, store, or upload the measurement value to the server.
Consider this example:
Imagine installing a temperature monitoring sensor in a conference room. Not everyone benefits from five-minute updates on a room’s temperature hovering around 20°C. Instead, the sensor wakes up, takes a reading, and checks if it falls within the typical range, say 20-21 degrees. If it does, the sensor stores the value. If the next reading is, for instance, 20.6°C, it saves that value and discards the previous one. Only select values are uploaded less frequently if readings consistently fall within the accepted range. However, if the sensor detects an outlier, such as 18 degrees, it immediately sends an alert. In such cases, we adopt a more frequent logging strategy stored locally and transmitted more frequently. We optimize power consumption, local storage, and cloud resources through these straightforward software functions.
Optimizing Battery Life through Network Signal Strength
We all know that a sensor’s battery life is inversely proportional to its coverage, whether LoRaWAN or 4G/5G. In areas with poorer connectivity, the battery life can shrink by a factor of four. To address this, our sensor periodically assesses the signal strength of its connection and reports it. Our central data analysis platform then calculates the optimal frequency for metric transmissions based on the current signal strength.
Customizing Sensor Functionality
But how do we adjust and fine-tune sensor settings remotely? We achieve this by employing MQTT data protocols utilizing MQTT callbacks. These callbacks enable the sensor to re-read updates and implement configuration changes while maintaining a connection. This can include reading measurements from additional sensors, controlling the frequency of updates, and much more. It’s a flexible approach that doesn’t necessitate remote software updates. Standard protocols like MQTT offer this flexibility, and we anticipate even more power-efficient protocols and advanced adaptations. In time, sensors will learn from their environment, adapting to the installation scenario. Imagine a sensor on an electric motor monitoring temperature and vibration. The future sensor will observe and learn, setting alarm limits based on abnormal deviations.
In closing, while more innovative future sensors may be around the corner, our current focus is saving battery power, reducing costs, and preserving the environment. In the grand landscape of IoT, it’s not just about connecting devices; it’s about doing so efficiently, sustainably, and intelligently.