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Towards realizing an intelligent networked society, enabling low-costlow-energy connectivity for things, also known as Internet of Things (IoT), isof crucial importance. While the existing wireless access networks requirecentralized signaling for managing network resources, this approach is of lessinterest for future generations of wireless networks due to the energyconsumption in such signaling and the expected increase in the number of IoTdevices. Then, in this work we investigate leveraging machine learning fordistributed control of IoT communications. Towards this end, first weinvestigate low-complex learning schemes which are applicable toresource-constrained IoT communications. Then, we propose a lightweightlearning scheme which enables the IoT devices to adapt their communicationparameters to the environment. Further, we investigate analytical expressionspresenting performance of a centralized control scheme for adaptingcommunication parameters of IoT devices, and compare the results with theresults from the proposed distributed learning approach. The simulation resultsconfirm that the reliability and energy efficiency of IoT communications couldbe significantly improved by leveraging the proposed learning approach.

Reliability and Battery Lifetime Improvement for IoT Networks: Challenges and AI-powered solutions