Paper Info
Title
Energy efficient approximate self-adaptive data collection in wireless sensor networks.
Abstract
To extend the lifetime of wireless sensor networks, reducing and balancing energy consumptions are main concerns in data collection due to the power constrains of the sensor nodes. Unfortunately, the existing data collection schemesmainly focus on energy saving but overlook balancing the energy consumption of the sensor nodes. In addition, most of them assume that each sensor has a global knowledge about the network topology. However, in many real applications, such a global knowledge is not desired due to the dynamic features of the wireless sensor network. In this paper, we propose an approximate self-adaptive data collection technique (ASA), to approximately collect data in a distributed wireless sensor network. ASA investigates the spatial correlations between sensors to provide an energyefficient and balanced route to the sink, while each sensor does not know any global knowledge on the network.We also show that ASA is robust to failures. Our experimental results demonstrate that ASA can provide significant communication (and hence energy) savings and equal energy consumption of the sensor nodes.
Year
DOI
Venue
2016
10.1007/s11704-016-4525-7
Frontiers of Computer Science
Keywords
Field
DocType
wireless sensor networks,data collection,energy efficient,self-adaptive
Computer science,Computer network,Artificial intelligence,Distributed computing,Sensor node,Key distribution in wireless sensor networks,Data collection,Efficient energy use,Network topology,Mobile wireless sensor network,Wireless sensor network,Energy consumption,Machine learning
Journal
Volume
Issue
ISSN
10
5
2095-2228
Citations 
PageRank 
References 
1
0.36
24
Authors
6
Name
Order
Citations
PageRank
Bin Wang11788246.68
Xiaochun Yang244052.12
Guoren Wang31366159.46
Ge YU41313175.88
Wanyu Zang519321.20
Meng Yu652466.52