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Sparsest Random Sampling for Cluster-Based Compressive Data Gathering in Wireless Sensor Networks
Zhejiang Univ, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China..
Shanghai Huawei Technol Corp Campus, Shanghai 200040, Peoples R China..
Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Hubei, Peoples R China.;Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210023, Jiangsu, Peoples R China..
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-5407-0835
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2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 36383-36394Article in journal (Refereed) Published
Abstract [en]

Compressive data gathering (CDG) has been recognized as a promising technique to collect sensory data in wireless sensor networks (WSNs) with reduced energy cost and better traffic load balancing. Besides, clustering is often integrated into CDG to further facilitate the network performance. However, existing cluster-based CDG methods generally require a large number of sensor nodes to participate in each compressive sensing (CS) measurement gathering and rarely consider possible node failures due to power depletion or malicious attacks, leading to insufficient energy efficiency and poor system robustness. In this paper, we propose a sparsest random sampling scheme for cluster-based CDG (SRS-CCDG) in WSNs to achieve energy efficient and robust data collection. Specifically, sensor nodes are organized into clusters. In each round of data gathering, a random subset of sensor nodes sense the monitored field and transmit their measurements to the corresponding cluster heads (CHs). Then, each CH transmits the data gathered within its cluster to the sink. In SRS-CCDG, each sensor reading is regarded as one CS measurement, and both intra-cluster and inter-cluster data transmissions can be realized by two methods, i.e., relaying or direct transmission. Furthermore, we propose analytical models that study the relationship between the size of clusters and the energy cost when using different intra-cluster and inter-cluster transmission schemes, aimed at finding the optimal size of clusters and transmission schemes that could lead to minimum energy cost. Then, we present a centralized clustering algorithm based on the theoretical analysis. Finally, we investigate the robustness of signal recovery performance of SRS-CCDG when node failures happen. Extensive simulations demonstrate that SRS-CCDG can significantly reduce the energy cost and improve the system robustness to node failures.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 6, p. 36383-36394
Keywords [en]
Compressive data gathering, cluster, node failures, wireless sensor networks
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-233306DOI: 10.1109/ACCESS.2018.2846815ISI: 000439022000099Scopus ID: 2-s2.0-85048563828OAI: oai:DiVA.org:kth-233306DiVA, id: diva2:1239290
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QC 20180816

Available from: 2018-08-16 Created: 2018-08-16 Last updated: 2018-08-16Bibliographically approved

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