DocumentCode :
3059347
Title :
Reduced energy dissipation using Beacon Based Data Collection algorithm for mobile sink in wireless sensor networks
Author :
Thanigaivelu, K. ; Murugan, K.
Author_Institution :
Ramanujan Comput. Centre, Anna Univ. Chennai, Chennai, India
fYear :
2009
fDate :
13-15 Dec. 2009
Firstpage :
112
Lastpage :
115
Abstract :
The growth of wireless sensor network applications including rescue missions, intrusion detection and smart buildings has encouraged the use of mobile devices such as mobile phones in a large scale, event-based, pervasive sensing environments. In a normal scenario static sinks collect data from the entire network but this approach results in high energy dissipation and higher traffic load in the sink´s vicinity. This would drain their energy faster leading to disconnected network. Since all sensor nodes are energy constrained, it becomes all more important to reduce the draining of energy of those nodes closer to the sink, simply because they are in the path of data propagation towards the sink. In this paper, we address this problem, by employing beacon based data collection (BBDC) algorithm for data collection from nodes using mobile sink. In this paper, we evaluate and compare network performance metrics including energy dissipation of the nodes using mobile sink and compare the same after implementing BBDC algorithm.
Keywords :
mobile handsets; mobile radio; wireless sensor networks; beacon-based data collection algorithm; data propagation; energy dissipation reduction; event-based environment; intrusion detection; mobile phones; mobile sink; pervasive sensing environment; rescue missions; smart buildings; wireless sensor networks; Batteries; Biomedical monitoring; Energy dissipation; Large-scale systems; Measurement; Mobile computing; Relays; Telecommunication traffic; Weather forecasting; Wireless sensor networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computing, 2009. ICAC 2009. First International Conference on
Conference_Location :
Chennai
Print_ISBN :
978-1-4244-4786-2
Electronic_ISBN :
978-1-4244-4787-9
Type :
conf
DOI :
10.1109/ICADVC.2009.5378257
Filename :
5378257
Link To Document :
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