Energy-Efficient Compressive Sensing Based Data Gathering and Scheduling in Wireless Sensor Networks

In a low-cost wireless sensor network data collection is the fundamental source of energy dissipation. In such a scenario, compressive data gathering has emerged as a powerful tool to minimise the energy consumption. Compressive data gathering reduces energy dissipation by minimising the amount of transmitted data. In this work, compressive sensing based data collection and link scheduling have been jointly studied in a disconnected network by considering a physical interference model. The network being disconnected, mobile collectors have been employed in the network for data collection. In compressive sensing, only a subset of the sensors are activated which sends the compressed data to the mobile collectors which then recover the data for all the sensors. The objective of this work is to reduce both the end-to-end latency and the number of transmissions for data collection. As the joint problem is NP-Hard, heuristic approaches have been proposed for both tree construction and link scheduling. Simulation results have been performed to show the effectiveness of the proposed algorithm when compared with some existing algorithms.