European Research and Innovation
Research and Innovation
Machine Learning in IoT for Autonomous, Adaptive Sensing by Frank Alexander Kraemer, Nattachart Tamkittikhun and Anders Eivind Braten (NTNU) Inevitably, there will be a huge number of sensor devices within the internet of things (IoT) - but how can we possibly manage to optimise each and every one of them? Our answer is to treat them as autonomous units, much like robots. To this end we have been experimenting with different approaches to find out how constrained devices can benefit from machine learning, so that they can operate optimally. Sensor devices are often situated in heterogeneous environments that change over time, for instance by changing location or variations in the weather. This is critical for their operation: Many sensor devices use energy harvesting, like solar energy, to sustain their operations, and their energy budget is critical to achieving their goals. This requires a high degree of optimisation. One of the characteristics of the internet of things (IoT), however, is its expected scale in terms of the number of devices. Therefore, the task of optimising IoT sensors or individually oversee their operation, cannot be performed manually. This leaves us with two options: Either over-dimensioning the system, for instance by investing in larger solar panels or batteries, or reducing the duty cycle of sensor devices to save energy, which effectively means to sense less frequently and send less data. In either case, systems do not operate optimally. This was also our experience within a smart city sensing project [1], where we used a static sensing approach. Sensing the emission data every six minutes worked adequately during the summer, but the solar panels could not provide enough energy during the dark winter in the Nordic areas, which eventually caused the sensor devices to shut down. The experiences within this smart city project motivated our approach of autonomous and adaptive sensing in the ART project: Instead of looking at sensor devices as simple and constrained sources of data, we see them as autonomous agents, much like robots. Throughout their operation, they have to constantly plan ahead and make decisions based on the changing environment and what they have observed so far. Possible mechanisms for this include different machine learning techniques, applied in combination with each other. To verify such an approach, we established a lab for autonomous sensors, which consists of an array of sensor nodes called Waspmotes, an off-the-shelf sensing system from Libelium driven by an 8-bit microcontroller. They communicate via LoRaWAN to a backend. Since the sensor nodes as well as the network are fairly constrained, the question is how machine learning can be applied in such a scenario. One solution is a centralised approach, in which machine learning is applied as part of the device management. Instead of just collecting data and monitoring key performance parameters such as sending frequency and battery level, the backend also learns from the received metadata and calculates optimised sensing strategies.
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ERCIM NEWS 110 July 2017