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.
Highlights
This paper considers the energy efficiency issue of compressive data collection in wireless sensor networks (WSNs)
This paper extends the reference [23] mainly in five aspects: (1) the starting node of random walks is variational; (2) nodes’ residual energy is considered for the balance of network load; (3) the Minimum Expected Cost Data Collection (MECDC) scheme along with its distributed realization is proposed; (4) the process of collecting measurements is accelerated by partitioning the network into layers; (5) computation of optimal transition probability matrix is simplified
The main contributions of this paper are summarized as follows: (i) We propose a random walk-based compressive data collection scheme which exploits the compressibility of the original data vector
Summary
This paper considers the energy efficiency issue of compressive data collection in wireless sensor networks (WSNs). A WSN is consisted of low-cost, low-power, and energyconstrained sensors which acquires and transmits information to the sink through wireless links [1,2,3,4,5]. In the area of Internet of Things (IoT), a WSN is regarded as a key technology for the data sensing and collection [6, 7]. One of the most important factors that affects the performance of WSNs is the energy limitation of sensors [8, 9]. We intend to design an energy-efficient data collection scheme by applying the compressive sensing (CS) technology and random walks
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