A Data-Gathering Scheme with Joint Routing and Compressive Sensing Based on Modified Diffusion Wavelets in Wireless Sensor Networks

Abstract

Compressive sensing (CS)-based data gathering is a promising method to reduce energy consumption in wireless sensor networks (WSNs). Traditional CS-based data-gathering approaches require a large number of sensor nodes to participate in each CS measurement task, resulting in high energy consumption, and do not guarantee load balance. In this paper, we propose a sparser analysis that depends on modified diffusion wavelets, which exploit sensor readings’ spatial correlation in WSNs. In particular, a novel data-gathering scheme with joint routing and CS is presented. A modified ant colony algorithm is adopted, where next hop node selection takes a node’s residual energy and path length into consideration simultaneously. Moreover, in order to speed up the coverage rate and avoid the local optimal of the algorithm, an improved pheromone impact factor is put forward. More importantly, theoretical proof is given that the equivalent sensing matrix generated can satisfy the restricted isometric property (RIP). The simulation results demonstrate that the modified diffusion wavelets’ sparsity affects the sensor signal and has better reconstruction performance than DFT. Furthermore, our data gathering with joint routing and CS can dramatically reduce the energy consumption of WSNs, balance the load, and prolong the network lifetime in comparison to state-of-the-art CS-based methods.

A Charging-Aware Multi-Mode Routing Protocol for Data Collection in Wireless Rechargeable Sensor Networks

“…A worse chaining sequence may cause the sensors to exhaust their energy faster than the sensors in the cluster-based methods. Another variation of the chain-based methods is the tree-type methods [27,28,29,30]. The tree-type methods use the minimum spanning trees (MSTP) [17] to create the shortest link between the sensors and their neighbors or try to propagate data to sink with a shortest distance path.…”

Energy Management in Wireless Sensor Networks for Internet of Things Applications

“…ireless sensor networks (WSNs) consist of number of sensor nodes which are distributed over the environment to monitor various physical characteristics of the real world, especially in internet of things (IoT) applications such as health care, education, transportation, or food industry. [1,2] The life of WSN depends on energy of sensor node. [3,4] Some proposal saves energy of nodes itself by energy power management of battery; [5] other proposals save energy by proposing efficient routing algorithm in WSN to maximize lifetime of WSN.…”