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.
Highlights
Wireless Sensor Networks (WSNs) generally consist of a large number of sensor nodes and a sink node deployed in the detected environment to monitor various physical characteristics of the real world, such as temperature, voltage, wind direction, and so on
Quer et al introduce a framework for data gathering considering Compressive sensing (CS) and principal component analysis (PCA) simultaneously
In [14] a sparest random scheduling for compressive data gathering in wireless sensor networks (WSNs) is provided to satisfy restricted isometric property (RIP) property; a sparse basis is designed based on the measurement matrix and sensor node readings
Summary
Wireless Sensor Networks (WSNs) generally consist of a large number of sensor nodes and a sink node deployed in the detected environment to monitor various physical characteristics of the real world, such as temperature, voltage, wind direction, and so on. (See Section 2 for more details.) For instance, [12] proposes a data-gathering scheme that diminishes the bottleneck of the sink in WSNs. In [13], the spatial correlation of sensor node readings is leveraged to improve the performance of WSNs. Wu et al. Quer et al introduce a framework for data gathering considering CS and principal component analysis (PCA) simultaneously In this scheme, the spatial characteristics of sensor node readings are utilized to enhance the recovery accuracy [15]. In [16], multi-chain CS-based data gathering is presented by considering the routing protocol from the sensor nodes to the sink of WSNs. A special routing algorithm corresponding to the topology structure is given in [17].
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