An Efficient Compressive Sensing Data Gathering Using Modified Ant Colony and Diffusion Wavelets in WSN

Compressive sensing (CS) depend on data gathering is a promising method to reduce energy consumption in wireless sensor networks (WSNs). The existing 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. The 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 combine routing and CS is present. A modified ant colony algorithm-based diffusion wavelets (ACBDW), where next hop node selection takes a node's residual energy and path length into consideration simultaneously. Moreover, in order to quickness up the coverage rate and avoid the local optimal of the algorithm, an improved pheromone impact factor is put forward. The diffusion wavelets based on sensor nodes' degree and different nodes' distance considering the above factors are proposed. To further reduce the transport costs in WSNs, a sparse measurement matrix is utilized and modified ant colony routing are jointly applied to mitigate energy consumption and balance the network load, especially lowering the transmission costs for those nodes nearest the sink node. The experimental result show data-gathering approaches, this proposed algorithm not only minimizes the energy consumption of the network, but prolongs the network lifetime.