Density and Transmission Power in Intelligent Wireless Sensor Networks

This paper covers the problem of interference generated by sensor nodes in Wireless Sensor Networks (WSNs). The interference affects the link quality of wireless communications, thus the Quality of Service (QoS) of Internet of Things (IoT) applications. The interference is the effect of the transmission of a cluster of nodes, at a certain power which is not always efficiently set, or calibrated. In addition, using unnecessary high power values impacts the waste of the node energy. Therefore, we address the interference problem by means of Transmission Power Control (TPC), for spatial reuse across the networks, which allows simultaneous point-to-point communications. Given the dynamics and unpredictability of the wireless channel, theoretical and empirical solutions are too slow, inefficient and memoryless for the problem we are facing. Our proposed protocol, QL-TPC, integrates reinforcement learning with game theory, within the IEEE 802.15.4 standard, at the MAC layer, to learn the combination of power levels per node, through indirect cooperation. The goal is to define the minimum transmission power, related to the density of the network, while respecting the QoS requirements and saving energy. QL-TPC is implemented in Atmel Zigbit, real world sensor devices, and is tested in a Faraday cage. We show the results, focusing on the aspect of reliability, energy efficiency, convergence and scalability. The nodes that use our protocol are estimated to have longer lifetime in order of months, while keeping same performance, than the homogeneous case.