Improving energy efficiency in a wireless sensor network by combining cooperative MIMO with data aggregation


In wireless sensor networks where nodes are powered by batteries, it is critical to prolong the network lifetime by minimizing the energy consumption of each node. In this paper, the cooperative multiple-input-multiple-output (MIMO) and data-aggregation techniques are jointly adopted to reduce the energy consumption per bit in wireless sensor networks by reducing the amount of data for transmission and better using network resources through cooperative communication. For this purpose, we derive a new energy model that considers the correlation between data generated by nodes and the distance between them for a cluster-based sensor network by employing the combined techniques. Using this model, the effect of the cluster size on the average energy consumption per node can be analyzed. It is shown that the energy efficiency of the network can significantly be enhanced in cooperative MIMO systems with data aggregation, compared with either cooperative MIMO systems without data aggregation or data-aggregation systems without cooperative MIMO, if sensor nodes are properly clusterized. Both centralized and distributed data-aggregation schemes for the cooperating nodes to exchange and compress their data are also proposed and appraised, which lead to diverse impacts of data correlation on the energy performance of the integrated cooperative MIMO and data-aggregation systems.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > Adaptive communications networks research group
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Uncontrolled Keywords: centralized data-aggregation scheme,cluster-based sensor network,cooperative MIMO,data aggregation,data compression,distributed data-aggregation scheme,energy consumption reduction,energy efficiency,wireless sensor network,Electrical and Electronic Engineering,Aerospace Engineering,Automotive Engineering,Computer Networks and Communications,Applied Mathematics
Publication ISSN: 1939-9359
Last Modified: 27 Jun 2024 07:53
Date Deposited: 26 Jul 2016 12:15
Full Text Link: http://ieeexplo ... rnumber=5540320
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2010-10
Published Online Date: 2010-08-05
Accepted Date: 2010-07-20
Submitted Date: 2009-10-18
Authors: Gao, Qiang
Zuo, Yi
Zhang, Jun
Peng, Xiao-Hong (ORCID Profile 0000-0002-0608-233X)



Version: Accepted Version

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