Optimized Local Superposition in Wireless Sensor Networks With T-Average-Mutual-Coherence

Abstract

Abstract-Compressed sensing (CS) is a new technology for recovering sparse data from undersampled measurements. It shows great potential to reduce energy for sensor networks. First, a basic global superposition model is proposed to obtain the measurements of sensor data, where a sampling matrix is modeled as the channel impulse response (CIR) matrix while the sparsifying matrix is expressed as the distributed wavelet transform (DWT). However, both the sampling and sparsifying matrixes depend on the location of sensors, so this model is highly coherent. This violates the assumption of CS and easily produces high data recovery error. In this paper, in order to reduce the coherence, we propose to control the transmit power of some nodes with the help of t-average-mutual-coherence, and recovery quality are greatly improved. Finally, to make the approach more realistic and energy-efficient, the CIR superposition is restricted in local clusters. Two key parameters, the radius of power control region and the radius of local clusters, are optimized based on the coherence and resource consideration in sensor networks. Simulation results demonstrate that the proposed scheme provides a high recovery quality for networked data and verify that t-average-mutual-coherence is a good criterion for optimizing the performance of CS in our scenario.

Improving Rare Events Detection in WSN through Cluster-Based Power Control Mechanism

“…This paper proposes new power control schemes whose accurate comparison to the existing work [17] is given in Table 2. Two new approaches are here proposed and compared, which are, respectively, based on sensors spatial repartition and sensors number.…”

“…Reference [17] also addressed the problem of cooperative power control in WSN. It proposed a method for proper matrix A design to achieve a sufficiently low coherence and guarantee a good recovery performance.…”

“…We also consider a measure of coherence, given by (17), to characterize the proposed mechanisms capacity in approaching the RIP conditions.…”

“…More precisely, it increases the transmit power for the farthest targets to compensate their fading and reduces the power for the nearest targets to CH. Note that in [17] the nearest nodes are assigned reduced transmit power and the farthest do not undergo any PC and keep their initial transmit power.…”

Effective Data Acquisition Protocol for Multi-Hop Heterogeneous Wireless Sensor Networks Using Compressive Sensing

“…Based on CS theory, Jia et al [28] considers a sparse event detection scenario where the channel impulse response (CIR) matrix is used as a natural sampling matrix. In [29], a basic global superposition model to obtain the measurements of sensor data is proposed, where a sampling matrix is modeled as the channel impulse response (CIR) matrix while the sparsifying matrix is expressed as the distributed wavelet transform. In [30], compressive distributed sensing using random walk CDS(RW) algorithm is proposed that uses rateless coding.…”