Energy-efficient Compressive Data Gathering Utilizing Virtual Multi-input Multi-output

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

Energy-efficient clustering/routing for cooperative MIMO operation in sensor networks

Abstract — Employing multi-input multi-output (MIMO) links can improve energy efficiency in wireless sensor networks (WSNs). Although a sensor node is likely to be equipped with only one antenna, it is possible to group several sensors to form a virtual MIMO link. Such grouping can be formed by means of clustering. In this paper, we propose a distributed MIMO-adaptive energyefficient clustering/routing scheme, coined cooperative MIMO (CMIMO), which aims at reducing energy consumption in multihop WSNs. In CMIMO, each cluster has two cluster heads (CHs), which are responsible for routing traffic between clusters (i.e., inter-cluster communications). CMIMO has the ability to adapt the transmission mode and transmission power on a per-packet basis. The transmission mode can be one of four transmit/receive configurations: 1 × 1 (SISO), 2 × 1 (MISO), 1 × 2 (SIMO), and 2 × 2 (MIMO). We study the performance of CMIMO via simulations. Results indicate that our proposed scheme achieves a significant reduction in energy consumption, compared to nonadaptive clustered WSNs. I