Cluster-based Distributed Compressed Sensing for QoS Routing in Cognitive Video Sensor Networks. (February 2019) - Li, Lingli
Abstract: Compressed sensing based in-network compression methods to minimize the data redundancy are critical to cognitive video sensor networks (CVSNs). 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 (QoS) satisfaction. CDCS, a cluster-based distributed compressed sensing approach for QoS routing is proposed to efficiently deliver visual information in CVSNs. To begin with, the correlation among adjacent video sensors is utilized to determine the suitable set of video sensors that participate in a cluster. On this basis, a sequential compressed sensing approach is applied to determine whether enough measurement have been obtained to limit the reconstruction error between decoded signals and original signals under a specified reconstruction threshold, thereby maximizing the removal of redundant traffic without sacrificing video quality. Lastly, the compressed data is transmitted by a distributed QoS routing scheme, with an objective to minimize energy consumption subject to delay and reliability constraints. Simulation results demonstrate that compared with exiting QoS routing schemes, CDCS can achieve energy-efficient data delivery and reconstruction accuracy of visual information.