Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing

Abstract

Data gathering is one of the most important operations in many wireless sensor networks (WSNs) applications. In order to implement data gathering, a tree structure rooted at the sink is usually defined. In most wireless sensor networks, nodes are powered by batteries with limited energy. Prolonging network lifetime is a critical issue for WSNs. As a technique for signal processing, compressed sensing (CS) is being increasingly applied to wireless sensor networks for saving energy. Compressive sensing can reduce the number of data transmissions and balance the traffic load throughout networks. In this paper, we investigate data gathering in wireless sensor networks using CS and aim at constructing a maximum-lifetime data-gathering tree. The lifetime of the network is defined as the number of data-gathering rounds until the first node depletes its energy. Based on the hybrid-CS data-gathering model, we first construct an arbitrary data-gathering tree and then use the random switching decision and optimal parent node selecting strategy to adjust the load of the bottleneck node and prolong the network lifetime. Simulation results show that the proposed algorithm outperforms several existing approaches in terms of network lifetime.

Distributed semi‐adaptive compressive sensing data collection in wireless sensor networks

“…The value of the parameters of energy consumption modes is specified in Table . To simplify the experiments, the simulation can neglect the energy consumptions of the sampling and data processing for all the simulated methods . Transmissions of all the data packets and the control packets are considered in the simulations.…”

Optimising energy in distributed phenomena detection for mobile WSN

“…proposed two strategies to reduce the energy consumption of the network. An approach for reducing network energy consumption by reducing the amount of transmitted data through compression is proposed by Chen et al [13]. Anagnostopoulos et al [14] deploy mobile sensor nodes close to the phenomena to attain high-quality monitoring.…”