Model assisted compressive data gathering in dense IoT monitoring of water distribution networks

Abstract

Smart city applications are becoming among the fastest growing segments of government. One of their highly promising applications is the continuous dense monitoring of critical infrastructure such as water, gas, and electrical networks. Technology advancements have made it possible to create low cost battery-operated internet of things (IoT) devices to monitor the operating parameters of these networks. However, the challenge remains to increase the life-time of these sensors and reduce its communication burden on the network. In this paper, we propose a compressive data gathering and node activation approach that utilizes information from existing operation models of these networks and combines it with the temporal and spatial correlation of the measured data in a compressive sensing (CS) framework. The proposed approach can effectively reduce the required number of measurements and achieves more power efficient node activation strategy. We give an example of the effectiveness of our approach using a hydraulic model for water distribution networks. We evaluate the results of the proposed compressive data gathering approach using the Hanoi network water data set and a pilot area in Hurghada, Egypt.