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.
International Journal of Sensor Networks, 2020 Vol.32 No.1, pp.25 - 33
Received: 25 Jul 2019
Accepted: 01 Aug 2019
Published online: 12 Jan 2020 *