Cost-Aware Stochastic Compressive Data Gathering for Wireless Sensor Networks

Abstract

Data gathering is a crucial function of wireless sensor networks (WSNs). In resource-limited WSNs, it is critical to improve cost-efficiency and prolong network lifetime. Sensor networks utilizing deterministic routing paths are particularly vulnerable to attacks. Additionally, repeated use of the same path will introduce load unbalance. In this paper, we propose a cost-aware stochastic compressive data gathering for WSNs. In contrast to traditional compressive sensing (CS) based algorithms that implicitly assume uniform transmission cost, our proposed scheme will consider the cost diversity into the CS data gathering framework. The Markov chain based model will be used to characterize the stochastic data gathering process. An optimization problem is formulated to minimize the total expected cost subjected to the constraints on global degree of randomness and the recovery error. Our proposed algorithm requires less total expected cost to achieve certain level of recovery accuracy. It also prolongs network lifetime due to its load balancing features. Extensive simulations on both synthetic and real data show that the proposed algorithm significantly outperforms benchmark algorithms.

Edge Computing in Internet of Things: A Novel Sensing-Data Reconstruction Algorithm Under Intelligent-Migratoin Stragegy

“…In the procedure of WSNs data collection, the main nodes energy consumption is the energy consumption of wireless communication, which can be greatly cut down by reducing the amount of data transmissions [15]- [18]. Therefore, by compressing the data and reducing the volume of data communication in data transmission, the network energy consumption can be lower, and the network service life can be extended in an effective manner [19]- [22].…”

A Random Walk-Based Energy-Aware Compressive Data Collection for Wireless Sensor Networks

“…Without the sacrifice of recovery fidelity, sparse random matrices have been proven to give better energy efficiency than the dense random matrices [17,18]. Under the CDG framework of WSNs, the sparse random matrix can be either uniform [17][18][19][20] or nonuniform [21][22][23][24]. In the uniform sparse random matrix, each entry is equal to zero with an identical probability.…”

“…Recently, nonuniform sparse random matrices were proved to give similar performance as the uniform sparse random matrices [21][22][23][24]. Liu et al [21] proposed a novel compressive data collection scheme which compresses data under an opportunistic routing.…”

“…In the proposed scheme, the total number of hops is reduced by a ring topology-based random walk, and the recovery accuracy is improved by the dual compensation-based compressive sensing measurements. Huang and Soong [23] proposed a cost-aware stochastic compressive data collection scheme, where the cost diversity and the stochastic data collection process are considered by using the Markov chain model. The proposed scheme is aimed at minimizing the expected cost of a random walk subjected to constraints on the global degree of randomness and recovery error.…”

“…Eventually, by using the NSRP-based estimator [21], the original data vector can be accurately recovered. For the compressive data collection problem, the reference [23] adopted a similar idea as this paper. However, in their scheme, each random walk starts at a fixed node, which results in the rapid energy expenditure of the fixed node.…”