In wireless multimedia sensor networks (WMSNs), visual correlation exists among multiple nearby cameras, thus leading to considerable redundancy in the collected images. This paper proposes a differential coding-based scheduling framework for efficiently gathering visually correlated images. This framework consists of two components including MinMax Degree Hub Location (MDHL) and Maximum Lifetime Scheduling (MLS). The MDHL problem aims to find the optimal locations for the multimedia processing hubs, which operate on different channels for concurrently collecting images from adjacent cameras, such that the number of channels required for frequency reuse is minimized. After associating camera sensors with proper hubs, the MLS problem targets at designing a schedule for the cameras such that the network lifetime of the cameras is maximized by letting highly correlated cameras perform differential coding on the fly. It is proven in this paper that the MDHL problem is NP-complete, and the MLS problem is NP-hard. Consequently, approximation algorithms are proposed to provide bounded performance. Since the designed algorithms only take the camera settings as inputs, they are independent of specific multimedia applications. Experiments and simulations show that the proposed differential coding-based scheduling can effectively enhance the network throughput and the energy efficiency of camera sensors.

The spatial correlation of visual information retrieved from distributed camera sensors leads to considerable data redundancy in wireless video sensor networks, resulting in significant performance degradation in energy efficiency and quality-of-service (QoS) satisfaction. In this paper, a correlation-aware QoS routing algorithm (CAQR) is proposed to efficiently deliver visual information under QoS constraints by exploiting the correlation of visual information observed by different camera sensors. First, a correlation-aware inter-node differential coding scheme is designed to reduce the amount of traffic in the network. Then, a correlation-aware load balancing scheme is proposed to prevent network congestion by splitting the correlated flows that cannot be reduced to different paths. Finally, the correlation-aware schemes are integrated into an optimization QoS routing framework with an objective to minimize energy consumption subject to delay and reliability constraints. Simulation results demonstrate that the proposed routing algorithm achieves efficient delivery of visual information under QoS constraints in wireless video sensor networks.

In wireless multimedia sensor networks (WMSNs), visual correlation exist among multiple nearby cameras, thus leading to considerable redundancy in the collected images. This paper addresses the problem of timely and efficiently gathering visually correlated images from camera sensors. Towards this, three fundamental problems are considered, namely, MinMax Degree Hub Location (MDHL), Minimum Sum-entropy Camera Assignment (MSCA), and Maximum Lifetime Scheduling (MLS). The MDHL problem aims to find the optimal locations to place the multimedia processing hubs, which operate on different channels for concurrently collecting images from adjacent cameras, such that the number of channels required for frequency reuse is minimized. With the locations of the hubs determined by the MDHL problem, the objective of the MSCA problem is to assign each camera to a hub in such a way that the global compression gain is maximized by jointly encoding the visually correlated images gathered by each hub. At last, given a hub and its associated cameras, the MLS problem targets at designing a schedule for the cameras such that the network lifetime of the cameras is maximized by letting highly correlated cameras perform differential coding on the fly. It is proven in this paper that the MDHL problem is NP-complete, and the others are NP-hard. Consequently, approximation and heuristic algorithms are proposed. Since the designed algorithms only take the camera settings as inputs, they are independent of specific multimedia applications. Experiments and simulations show that the proposed image gathering schemes effectively enhance network throughput and image compression performance.

Data redundancy caused by correlation has motivated the application of collaborative multimedia in-network processing for data filtering and compression in wireless multimedia sensor networks (WMSNs). This paper proposes an information theoretic image compression framework with an objective to maximize the overall compression of the visual information gathered in a WMSN. The novelty of this framework relies on its independence of specific image types and coding algorithms, thereby providing a generic mechanism for image compression under different coding solutions. The proposed framework consists of two components. First, an entropy-based divergence measure (EDM) scheme is proposed to predict the compression efficiency of performing joint coding on the images collected by spatially correlated cameras. The EDM only takes camera settings as inputs without requiring statistics of real images. Utilizing the predicted results from EDM, a distributed multi-cluster coding protocol (DMCP) is then proposed to construct a compression-oriented coding hierarchy. The DMCP aims to partition the entire network into a set of coding clusters such that the global coding gain is maximized. Moreover, in order to enhance decoding reliability at data sink, the DMCP also guarantees that each sensor camera is covered by at least two different coding clusters. Experiments on H.264 standards show that the proposed EDM can effectively predict the joint coding efficiency from multiple sources. Further simulations demonstrate that the proposed compression framework can reduce 10%–23% total coding rate compared with the individual coding scheme, i.e., each camera sensor compresses its own image independently.

The spatial correlation among the images retrieved from distributed video sensors leads to considerable data redundancy, thus resulting in significant performance degradation in energy efficiency and QoS satisfaction. In this paper, a correlation-aware QoS routing algorithm (CAQR) is proposed to efficiently deliver visual information under QoS constraints by exploiting the correlation among video sensors. Firstly, a correlation-aware differential coding scheme is designed to reduce the amount of traffic generated by correlated video sensors. Then, a correlation-aware load balancing scheme is proposed to prevent network congestion by splitting the correlated flows that cannot be reduced to different paths. Finally, these correlation-aware schemes are integrated into an optimization QoS routing framework with an objective to minimize energy consumption subject to QoS constraints. Simulation results show that the proposed algorithm achieves efficient delivery of visual information under QoS constraints in wireless video sensor networks.

Data redundancy caused by correlation has motivated the application of collaborative multimedia in-network processing for data filtering and compression in wireless multimedia sensor networks (WMSNs). This paper proposes an information theoretic data compression framework with an objective to maximize the overall compression of the visual information gathered in a WMSN. To achieve this, an entropy-based divergence measure (EDM) scheme is proposed to predict the compression efficiency of performing joint coding on the images collected by spatially correlated cameras. The novelty of EDM relies on its independence of the specific image types and coding algorithms, thereby providing a generic mechanism for prior evaluation of compression under different coding solutions. Utilizing the predicted results from EDM, a distributed multi-cluster coding protocol (DMCP) is proposed to construct a compression-oriented coding hierarchy. The DMCP aims to partition the entire network into a set of coding clusters such that the global coding gain is maximized. Moreover, in order to enhance decoding reliability at data sink, the DMCP also guarantees that each sensor camera is covered by at least two different coding clusters. Experiments on H.264 standards show that the proposed EDM can effectively predict the joint coding efficiency from multiple sources. Further simulations demonstrate that the proposed compression framework can reduce 10% - 23% total coding rate compared with the individual coding scheme, i.e., each camera sensor compresses its own image independently.

Wireless multimedia sensor networks (WMSNs) are interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists correlation among the visual information observed by cameras with overlapped field of views. In this paper, the correlation characteristics of visual information are used to address two issues: 1) how to measure the amount of visual information provided by multiple cameras in the network, and 2) how to select a group of cameras to report their information to the sink under distortion constraints. An entropy-based analytical framework is developed to measure the amount of visual information provided by multiple correlated cameras first. Based on this framework, a correlation-based camera selection scheme is introduced. Simulation results show that, given a distortion bound at the sink, the correlation-based selection scheme requires fewer cameras to report to the sink than the random selection scheme.

Wireless multimedia sensor networks (WMSNs) are interconnected devices that allow retrieving video and audio streams, still images, and scalar data from the environment. In a densely deployed WMSN, there exists correlation among the visual information observed by cameras with overlapped field of views. This paper proposes a novel spatial correlation model for visual information in WMSNs. By studying the sensing model and deployments of cameras, a spatial correlation function is derived to describe the correlation characteristics of visual information observed by cameras with overlapped field of views. The joint effect of multiple correlated cameras is also studied. An entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. Furthermore, according to the proposed correlation function and entropy-based framework, a correlation-based camera selection algorithm is designed. Experimental results show that the proposed spatial correlation function can model the correlation characteristics of visual information in WMSNs through low computation and communication costs. Further simulations show that, given a distortion bound at the sink, the correlation-based camera selection algorithm requires fewer cameras to report to the sink than the random selection algorithm.