Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing
Data gathering is one of the most important operations in many wireless sensor networks (WSNs) applications. In order to implement data gathering, a tree structure rooted at the sink is usually defined. In most wireless sensor networks, nodes are powered by batteries with limited energy. Prolonging...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-05-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2016/2313064 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850106551845519360 |
|---|---|
| author | Zhengyu Chen Geng Yang Lei Chen Jian Xu |
| author_facet | Zhengyu Chen Geng Yang Lei Chen Jian Xu |
| author_sort | Zhengyu Chen |
| collection | DOAJ |
| description | Data gathering is one of the most important operations in many wireless sensor networks (WSNs) applications. In order to implement data gathering, a tree structure rooted at the sink is usually defined. In most wireless sensor networks, nodes are powered by batteries with limited energy. Prolonging network lifetime is a critical issue for WSNs. As a technique for signal processing, compressed sensing (CS) is being increasingly applied to wireless sensor networks for saving energy. Compressive sensing can reduce the number of data transmissions and balance the traffic load throughout networks. In this paper, we investigate data gathering in wireless sensor networks using CS and aim at constructing a maximum-lifetime data-gathering tree. The lifetime of the network is defined as the number of data-gathering rounds until the first node depletes its energy. Based on the hybrid-CS data-gathering model, we first construct an arbitrary data-gathering tree and then use the random switching decision and optimal parent node selecting strategy to adjust the load of the bottleneck node and prolong the network lifetime. Simulation results show that the proposed algorithm outperforms several existing approaches in terms of network lifetime. |
| format | Article |
| id | doaj-art-2e2a19b5b6a34ef8b4f9681cf2fecb32 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-2e2a19b5b6a34ef8b4f9681cf2fecb322025-08-20T02:38:48ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-05-011210.1155/2016/2313064Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed SensingZhengyu Chen0Geng Yang1Lei Chen2Jian Xu3 Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, Jiangsu 210003, China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, Jiangsu 210003, China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, Jiangsu 210003, China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, Jiangsu 210003, ChinaData gathering is one of the most important operations in many wireless sensor networks (WSNs) applications. In order to implement data gathering, a tree structure rooted at the sink is usually defined. In most wireless sensor networks, nodes are powered by batteries with limited energy. Prolonging network lifetime is a critical issue for WSNs. As a technique for signal processing, compressed sensing (CS) is being increasingly applied to wireless sensor networks for saving energy. Compressive sensing can reduce the number of data transmissions and balance the traffic load throughout networks. In this paper, we investigate data gathering in wireless sensor networks using CS and aim at constructing a maximum-lifetime data-gathering tree. The lifetime of the network is defined as the number of data-gathering rounds until the first node depletes its energy. Based on the hybrid-CS data-gathering model, we first construct an arbitrary data-gathering tree and then use the random switching decision and optimal parent node selecting strategy to adjust the load of the bottleneck node and prolong the network lifetime. Simulation results show that the proposed algorithm outperforms several existing approaches in terms of network lifetime.https://doi.org/10.1155/2016/2313064 |
| spellingShingle | Zhengyu Chen Geng Yang Lei Chen Jian Xu Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing International Journal of Distributed Sensor Networks |
| title | Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing |
| title_full | Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing |
| title_fullStr | Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing |
| title_full_unstemmed | Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing |
| title_short | Constructing Maximum-Lifetime Data-Gathering Tree in WSNs Based on Compressed Sensing |
| title_sort | constructing maximum lifetime data gathering tree in wsns based on compressed sensing |
| url | https://doi.org/10.1155/2016/2313064 |
| work_keys_str_mv | AT zhengyuchen constructingmaximumlifetimedatagatheringtreeinwsnsbasedoncompressedsensing AT gengyang constructingmaximumlifetimedatagatheringtreeinwsnsbasedoncompressedsensing AT leichen constructingmaximumlifetimedatagatheringtreeinwsnsbasedoncompressedsensing AT jianxu constructingmaximumlifetimedatagatheringtreeinwsnsbasedoncompressedsensing |