Title: Using neural networks to reduce sensor cluster interferences and power consumption in smart cities

Authors: Per Lynggaard

Addresses: Technical University of Denmark, Lautrupvang 15, 2750 Ballerup, Denmark

Abstract: In the future smart cities, billions of communicating Internet of Things (IoT) devices are expected which communicate wirelessly in the limited spectrum offered by 5G and long-range technologies. This means that a huge amount of interferences must be overcome by new agile technologies without wasting power resources in the IoT nodes. In this paper, these challenges are addressed by a neural-network-based machine learning system that is based on frequency-domain features extracted from the communication channel. This machine learning system predicts the needed transmit power to overcome the interferences by a predefined margin. Extensive system simulations have been performed on a real-world dataset that shows power savings in the range of 35-83% and a packet receive-ratio of at least 95%. Similarly, it has been found that the system converts after approximately 50 supervised samples, which supports efficient tracking of parameter variations in the communication channel.

Keywords: smart buildings; IoT networks; interferences; neural-networks; transmit-power regulation; decentralised control schemes.

DOI: 10.1504/IJSNET.2020.104460

International Journal of Sensor Networks, 2020 Vol.32 No.1, pp.25 - 33

Received: 25 Jul 2019
Accepted: 01 Aug 2019

Published online: 13 Jan 2020 *

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