Region segmentation model for wireless sensor networks considering optimal energy conservation constraints

被引:4
作者
Chen, Xi [1 ]
Wu, Tao [2 ]
机构
[1] Southwest Minzu Univ, Sch Comp Sci & Technol, Chengdu 610041, Sichuan, Peoples R China
[2] Chengdu Univ Informat Technol, Sch Comp Sci, Chengdu 610225, Sichuan, Peoples R China
来源
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2019年 / 22卷 / Suppl 3期
关键词
Energy constraint; Wireless sensor network; Region segmentation; Energy cost; Coverage;
D O I
10.1007/s10586-018-1788-9
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In order to improve the life cycle of wireless sensor networks as well as reducing the energy cost, the structural optimization and energy conservation for region segmentation are designed. A region segmentation model for wireless sensor networks based on optimal energy conservation constraints is proposed. The initial network topology for node distribution of wireless sensor networks is constructed. The equivalent network-wide energy balance topology is used for optimal calculation of the coverage area of the sensor network and the shortest path optimization method is used for energy conservation design for sensor network nodes. According to the energy attribute of sensor nodes, the coverage area of wireless sensor networks is segmented optimally to improve the coverage of wireless sensor networks and reduce the energy cost of a single node in the network, to realize the optimal networking of wireless sensor networks. The simulation results show that for the region segmentation model of wireless sensor networks constructed by this method, the quality reliability of transmitting data by network nodes is higher, the regional coverage is stronger and the energy cost is lower, compared with previous works, which effectively prolong the life cycle of wireless sensor networks.
引用
收藏
页码:S7507 / S7514
页数:8
相关论文
共 20 条
[1]  
Abo-Zahhad M, 2015, IEEE I C ELECT CIRC, P17, DOI 10.1109/ICECS.2015.7440238
[2]  
Akhlaq M., 2015, SENS APPL S SAS 2012, P1
[3]   Collective Prediction exploiting Spatio Temporal correlation (CoPeST) for energy efficient wireless sensor networks [J].
Arunraja, Muruganantham ;
Malathi, Veluchamy .
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2015, 9 (07) :2488-2511
[4]  
Baidya S.S., 2015, INT C COMM INF COMP, P1
[5]  
Basavaraju TG, 2015, INT J NETW COMMUN, V5, P31
[6]   Exact approaches for lifetime maximization in connectivity constrained wireless multi-role sensor networks [J].
Castano, Fabian ;
Bourreau, Eric ;
Velasco, Nubia ;
Rossi, Andre ;
Sevaux, Marc .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2015, 241 (01) :28-38
[7]  
Chefi A, 2014, IEEE I C ELECT CIRC, P678, DOI 10.1109/ICECS.2014.7050076
[8]   Energy conservation in wireless sensor networks: a rule-based approach [J].
Chong, Suan Khai ;
Gaber, Mohamed Medhat ;
Krishnaswamy, Shonali ;
Loke, Seng Wai .
KNOWLEDGE AND INFORMATION SYSTEMS, 2011, 28 (03) :579-614
[9]  
Das B, 2015, INTERNATIONAL CONFERENCE ON 2015 APPLICATIONS AND INNOVATIONS IN MOBILE COMPUTING (AIMOC), P132, DOI 10.1109/AIMOC.2015.7083841
[10]   WILDSENSING: Design and Deployment of a Sustainable Sensor Network for Wildlife Monitoring [J].
Dyo, Vladimir ;
Ellwood, Stephen A. ;
MacDonald, David W. ;
Markham, Andrew ;
Trigoni, Niki ;
Wohlers, Ricklef ;
Mascolo, Cecilia ;
Pasztor, Bence ;
Scellato, Salvatore ;
Yousef, Kharsim .
ACM TRANSACTIONS ON SENSOR NETWORKS, 2012, 8 (04)