Abstract
Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we propose an energy efficient distributed compressive sensing solution for sensor networks. The proposed solution utilizes sparsity distribution of signals to group sensor nodes into several coalitions and then implements localized compressive sensing inside coalitions. This solution improves data-gathering performance in terms of both data accuracy and energy consumption. The approach curbs both data-transmission costs and number of measurements. Coalition-based data gathering cuts transmission costs, and the number of measurements is reduced by scheduling sensor nodes and adjusting their sampling frequency. Our simulation showed that our approach enhances network performance by minimizing energy cost and improving data accuracy.
Highlights
Energy efficiency is a continuing concern within wireless sensor networks
In order to achieve an energy-efficient and quality-aware compressive sensing method, we introduce a distributed compressive sensing approach with spatial correlation among sensor nodes to group them into coalitions
The proposed coalition-formation method is represented by a block diagonal measurement matrix in which each diagonal entity corresponds to one of the coalitions
Summary
Energy efficiency is a continuing concern within wireless sensor networks. Every sensor network is operational as long as they have enough energy resources. Recent compressive sensing solutions proposed in wireless sensor networks have proven advantages in minimizing the number of measurements, but they are still not competitive with the existing data compression techniques [3]. We propose a new distributed compressive sensing based data gathering approach which utilizes spatial-temporal correlation to improve the network performance in terms of energy consumption and data reconstruction accuracy. The block diagonal measurement matrix is structured in a way that balances the computation and communication load over the coalitions This spatial-temporal correlation based compressive sensing is used inside each coalition to compress sensor node readings and transfer it to the base station. This recovery procedure executes inside each coalition to achieve a common profile among sensor nodes Utilizing this recovery algorithm helps to achieve better accuracy in data reconstruction while the number of required measurements is reduced.
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