Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143046
Title: Cost-aware stochastic compressive data gathering for wireless sensor networks
Authors: Huang, Jiajia
Soong, Boon-Hee
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Huang, J., & Soong, B.-H. (2019). Cost-aware stochastic compressive data gathering for wireless sensor networks. IEEE Transactions on Vehicular Technology, 68(2), 1525-1533. doi:10.1109/TVT.2018.2887091
Journal: IEEE Transactions on Vehicular Technology
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.
URI: https://hdl.handle.net/10356/143046
ISSN: 0018-9545
DOI: 10.1109/TVT.2018.2887091
Schools: School of Electrical and Electronic Engineering 
Organisations: Institute for Infocomm Research, A∗STAR
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TVT.2018.2887091
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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