Affiliation:
1. School of Information, Yunnan University of Finance and Economics, Kunming 650221, China
2. School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
Abstract
The energy efficiency for data collection is one of the most important research topics in wireless sensor networks (WSNs). As a popular data collection scheme, the compressive sensing- (CS-) based data collection schemes own many advantages from the perspectives of energy efficiency and load balance. Compared to the dense sensing matrices, applications of the sparse random matrices are able to further improve the performance of CS-based data collection schemes. In this paper, we proposed a compressive data collection scheme based on random walks, which exploits the compressibility of data vectors in the network. Each measurement was collected along a random walk that is modeled as a Markov chain. The Minimum Expected Cost Data Collection (MECDC) scheme was proposed to iteratively find the optimal transition probability of the Markov chain such that the expected cost of a random walk could be minimized. In the MECDC scheme, a nonuniform sparse random matrix, which is equivalent to the optimal transition probability matrix, was adopted to accurately recover the original data vector by using the nonuniform sparse random projection (NSRP) estimator. Simulation results showed that the proposed scheme was able to reduce the energy consumption and balance the network load.
Funder
2020 Undergraduate Training Program for Innovation and Entrepreneurship of Yunnan Province
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems