Compressed sensing based in-network compression methods which minimize data redundancy are critical to cognitive video sensor networks. However, most existing methods require a large number of sensors for each measurement, resulting in significant performance degradation in energy efficiency and quality-of-service satisfaction. In this paper, a cluster-based distributed compressed sensing scheme working together with a quality-of-service aware routing framework is proposed to deliver visual information in cognitive video sensor networks efficiently. First, the correlation among adjacent video sensors determines the member nodes that participate in a cluster. On this basis, a sequential compressed sensing approach is applied to determine whether enough measurements are obtained to limit the reconstruction error between decoded signals and original signals under a specified reconstruction threshold. The goal is to maximize the removal of unnecessary traffic without sacrificing video quality. Lastly, the compressed data is transmitted via a distributed spectrum-aware quality-of-service routing scheme, with an objective of minimizing energy consumption subject to delay and reliability constraints. Simulation results demonstrate that the proposed approach can achieve energy-efficient data delivery and reconstruction accuracy of visual information compared with existing quality-of-service routing schemes.
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