Energy-Efficient Compressive Data Gathering Utilizing Virtual Multi-Input Multi-Output
Abstract: Data gathering is an
attractive operation for obtaining information in wireless sensor networks (WSNs).
But one of important challenges is to minimize energy consumption of networks.
In this paper, an integration of distributed compressive sensing (CS) and
virtual multi-input multi-output (vMIMO) in WSNs is proposed to significantly
decrease the data gathering cost. The scheme first constructs a distributed data
compression model based on low density parity check-like (LDPC-like) codes.
Then a cluster-based dynamic virtual MIMO transmission protocol is proposed.
The number of clusters, number of cooperative nodes and the constellation size
are determined by a new established optimization model under the restrictions
of compression model. Finally, simulation results show that the scheme can
reduce the data gathering cost and prolong the sensor network’s lifetime in a
reliable guarantee of sensory data recovery quality.
Author: Fang Jiang, Yanjun Hu
Journal Code: jptkomputergg170139