Abstract
This paper proposes an approach toward solving an issue pertaining to measuring compressible data in large-scale energy-harvesting wireless sensor networks with channel fading. We consider a scenario in which N sensors observe hidden phenomenon values, transmit their observations using amplify-and-forward protocol over fading channels to a fusion center (FC), and the FC needs to choose a number of sensors to collect data and recover them according to the desired approximation error using the compressive sensing. In order to reduce the communication cost, sparse random matrices are exploited in the pre-processing procedure. We first investigate the sparse representation for sensors with regard to recovery accuracy. Then, we present the construction of sparse random projection matrices based on the fact that the energy consumption can vary across the energy harvesting sensor nodes. The key ingredient is the sparsity level of the random projection, which can greatly reduce the communication costs. The corresponding number of measurements is chosen according to the desired approximation error. Analysis and simulation results validate the potential of the proposed approach.
Highlights
A wireless sensor network (WSN) is an intelligent system with data collection, data fusion and independent transmission, which involves in many applications such as military surveillance, embedded systems, computer networks and communications
In order to reduce the energy consumption while forwarding observations to fusion center (FC), we consider an innovative data gathering and reconstruction process based on three key subproblems: (i) compressive sensing (CS) based data acquisition; (ii) transmission of sparse random projection under fading for adapting random energy availability in energy harvesting (EH) systems; and (iii) CS based data reconstruction
The main purpose of this paper is to study sparse representation and sparse random projection for EH WSNs under fading channels
Summary
A wireless sensor network (WSN) is an intelligent system with data collection, data fusion and independent transmission, which involves in many applications such as military surveillance, embedded systems, computer networks and communications. It consists of several sensors and each node is generally small in size and has a battery of limited capacity and energy. The lifetimes of WSNs are extremely limited by the total energy available in the batteries. In EH-WSN, each sensor node provides two functionalities: sensing, transmitting data to the fusion center (FC), and harvesting energy from ambient energy sources. In order to reduce the energy consumption while forwarding observations to FC, we consider an innovative data gathering and reconstruction process based on three key subproblems: (i) compressive sensing (CS) based data acquisition; (ii) transmission of sparse random projection under fading for adapting random energy availability in EH systems; and (iii) CS based data reconstruction
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