Energy efficient approximate self-adaptive data collection in wireless sensor networks
Bin WANG1,Xiaochun YANG1(),Guoren WANG1,Ge YU1,Wanyu ZANG2,Meng YU3
1. Northeastern University, School of Computer Science and Engineering, Liaoning 110819, China 2. Texas A&M University at San Antonio, Department of Accounting, Computing and Finance, San Antonio TX 78363, USA 3. The University of Texas at San Antonio, Computer Science Department, San Antonio TX 78249, USA
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.
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