无线传感器网络分簇和节点休眠综合性策略研究

计算机与现代化

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无线传感器网络分簇和节点休眠综合性策略研究

中南大学软件学院，湖南长沙410075

收稿日期:2014-09-18 出版日期:2015-04-27 发布日期:2015-04-29
作者简介: 陈龙(1990-),男,湖南湘潭人，中南大学软件学院硕士研究生,研究方向：无线传感器网络; 刘莉平(1971-),女,博士研究生,副教授，研究方向：无线传感器网络；赵明(1975-),男,博士研究生,副教授，研究方向：无线传感器网络。
基金资助:
国家自然科学基金资助项目(61073186); 湖南省自然科学基金资助项目(13JJ3007)

Comprehensive Strategy Research for Network Clustering and Node Sleeping

School of Software, Central South University, Changsha 410075, China

Received:2014-09-18 Online:2015-04-27 Published:2015-04-29

摘要/Abstract

摘要：

能量有限性是制约传感器网络寿命的瓶颈。在传感器网络中通常采用相似数据收集策略或者节点休眠策略来减少能耗延长网络寿命。但是以往的研究往往局限在其中一种机制：在基于相似

数据收集的分簇策略中，基本上所有的传感器节点都是处于活动状态，而在节点休眠机制中，往往也没有使用相似数据收集的思想进行网络的分簇。本文提出一种综合传感器网络分簇和节点休眠机制的

协议，将传感器网络的生命划分成若干个时间周期，在每一个时间周期中，确定每个节点的θ相似节点集并选出代表节点（Rnode）来发送感知的数据，并且休眠部分冗余的节点。仿真实验结果表明，在

保证网络覆盖的情况下，该协议能让传感器网络有更长的网络寿命。

关键词: , 分簇；能量消耗；网络寿命；节点休眠；代表节点

Abstract:

To conserve energy, we can use method for approximate data collection. Sensor nodes with the same readings can be clustered such that only the representative

nodes report their readings. Also, we can use sensor activity scheduling mechanism that consists on turning off redundant sensors while maintaining coverage by the remaining

active nodes to prolong the life time of WSN. However, the two techniques seem independent and few works consider both of them. In this paper, we propose an efficient protocol

synthesizing network clustering and sensor activity scheduling. The proposed protocol determine θ-similar_sets of node and only the Rnode need to report their readings. Also,

the protocol divides the network lifetime into activity rounds. In each round a number of active nodes (Rnode and members) is selected to ensure coverage while others are sleep.

Compared with some existing protocols, simulation results show that our protocol can have a longer lifetime with a high network coverage.

Key words: clustering, energy consumption, network lifetime, node sleeping, representative node

陈龙,刘莉平,赵明,陈志刚. 无线传感器网络分簇和节点休眠综合性策略研究[J]. 计算机与现代化.

CHEN Long, LIU Li-ping, ZHAO Ming, CHEN Zhi-gang. Comprehensive Strategy Research for Network Clustering and Node Sleeping[J]. Computer and Modernization.

［1］ Akyildiz I F, Su Weilian, Sankarasubramaniam F, et al. A survey on sensor networks［J］. IEEE Communications Magazine, 2002,40(8):102-114. 
［2］ Yick J, Mukherjee B, Ghosal D. Wireless sensor network survey［J］. Computer Networks, 2008,52(12):2292-2330. 
［3］ Krishnamachari B, Estrin D, Wicker S. The impact of data aggregation in wireless sensor networks［C］// Proceedings of the 22nd International Conference on Distributed

Computing Systems Workshops (ICDCSW). 2002：575-578.
［4］ Wang Bang, Fu Cheng, Lim Hock Beng. Layered diffusion-based coverage control in wireless sensor networks［C］// 2007 the 32nd IEEE Conference on Local Computer Networks.

2007:504-511.
［5］ Hung Chih-Chieh, Peng Wen-Chih, Lee Wang-Chien. Energy-aware set-covering approaches for approximate data collection in wireless sensor networks［J］. IEEE Transactions

on Knowledge and Data Engineering, 2009,24(11):1993-2007.
［6］ Kotidis Y. Snapshot queries: Towards data-centric sensor networks［C］// Proceedings of the 21st International Conference on Data Engineering(ICDE). 2005:131-142.
［7］ Yang Hong-Yen, Lin Chia-Hung, Tsai Ming-Jer. Distributed algorithm for efficient construction and maintenance of connected k-hop dominating sets in mobile Ad Hoc networks

［J］. IEEE Transactions on Mobile Computing, 2008,7(4):444-457.
［8］ Mo Lufeng, He Yuan, Liu Yunhao, et al. Canopy closure estimates with GreenOrbs: Sustainable sensing in the forest［C］// The 7th International Conference on Embedded

Networked Sensor Systems. 2009:99-112.
［9］ Liu Chong, Wu Kui, Pei Jian. An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation［J］. IEEE Transactions

on Parallel and Distributed System, 2007,18（7）：1010-1023.
［10］Huang Scott, Wan Peng-jun, Vu Chinh, et al. Nearly constant approximation for data aggregation scheduling in wireless sensor networks［C］// Proceedings of the 26th IEEE

International Conference on Computer Communications. 2007:366-372.
［11］Amol Deshpande, Carlos Guestrin, Samuel R Madden,et al. Model-driven data acquisition in sensor networks［C］// Proceedings of the Thirtieth International Conference on

Very Large Data Bases. 2004,30:588-599.
［12］Iosif Lazaridis, Sharad Mehrotra. Capturing sensor-generated time series with quality guarantees［C］// Proceedings of the 19th International Conference on Data

Engineering(ICDE’03). 2003：429-440.
［13］Pazand B, Datta A. An energy-efficient node scheduling scheme for wireless sensor networks based on minimum dominating sets［J］. International Journal of Network

Management, 2009,19(2):75-99.
［14］Douglas D H, Peucker T K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature［J］. The International Journal

for Geographic Information and Geovisualization, 1973,10(2):112-122.
［15］ Chalermek Intanagonwiwat, Deborah Estrin, Ramesh Govindan, et al. Impact of network density on data aggregation in wireless sensor networks［C］// Proceedings of the

22nd International Conference on Distributed Computing Systems. 2002:457-458.

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无线传感器网络分簇和节点休眠综合性策略研究

Comprehensive Strategy Research for Network Clustering and Node Sleeping

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