## 1 Introduction  
Wireless video sensor networks (WVSNs) [1] refer to networks of interconnected video sensor nodes equipped with audio and video information collection modules. Such networks can process, transmit and fuse multimedia data originated from heterogeneous sources. Quality-of-Service (QoS) sensitive video applications require more spectrum and transmission resources [2; 3; 4; 5; 6; 7]. Cognitive radio (CR) [8] as a promising technology has been presented to improve spectrum utilization. Some CR standards have been developed in TV White Spaces (TVWS) band [9]. Secondary users (SUs) can opportunistically access the TVWS while preventing harmful interference to primary user (PU) transmissions. CR can also be applied to WVSNs for better communication performance, which evolves into cognitive video sensor networks (CVSNs) [10; 11] aimed at delivering video content with a predetermined level of QoS. However, in addition to resource constraints such as limited energy and processing capacity, the transmission of visual information requires high-bandwidth, high-fidelity and more processing energy. These challenges and constraints, along with the complex network environment, make video transmission and processing over CVSNs a challenging issue. Typical applications in CVSNs require spatially dense sensor deployment to achieve satisfactory coverage [12]. As a consequence, multiple sensors often record information about one event in each field-of-view (FoV). Due to the high density in the network topology, spatially proximal sensor observations are highly correlated, leading to much redundant visual data. Collaborative in-network multimedia processing [13] is promising for alleviating redundant data, with which each sensor can filter uninterested information or integrate observations with other correlated nodes. Unlike spatial correlation of user experience [14] which affects the performance of cooperative spectrum sensing, here we focus on the spatial correlation of visual information that exists among the observations of distributed video sensors with overlapped FoVs. By performing correlation-aware in-network video processing, the communication cost/bandwidth usage of the entire downstream path decreases at the expense of relatively small energy consumption induced by the local transmission and compression. This method facilitates a routing protocol to provide QoS guarantees. Compressed sensing (CS) [15] as an extension of information theory can be introduced that deals with visual information for CVSNs. Such a technique is an alternative to the traditional Shannon-Nyquist sampling for sparse/compressible signal acquisition, aiming to extract as much information as possible from as little data as possible. Specifically, sampling devices collect not the original signal, but the data after orthogonal transformation. Each observation contains original signals to some extent and each part of an original signal diffuse in each observation. Distributed compressed sensing (DCS) [16; 17; 18] extends CS to exploit both intra- and inter-signal correlation for video compression, thereby reducing the amount of data for signal reconstruction. According to the theory of DCS, a signal can be reconstructed by a small number of linear observations as long as the signal can be represented sparsely on some bases. Many existing analyses characterize how many measurements we need for the signal with a given sparsity level. As the sparsity level is often not known a priori, obtaining the signal sparsity in practical applications is very difficult. The focus of this work is on the energy-efficient transmission of visual information in the presence of QoS constraints in CVSNs. We propose a cluster-based distributed compressed sensing scheme (called CDCS) working together with a distributed QoS routing to facilitate delivery of video data. The main contributions are as follows:  
* A correlation metric for adjacent video sensors with overlapped FoVs is utilized to determine which video sensor can participate in a cluster.