In this paper, the Relative Pose based Redundancy Removal (RPRR) scheme is presented, which has been designed for mobile RGB-D sensor networks operating under bandwidth-constrained operational scenarios. The scheme considers a multiview scenario in which pairs of sensors observe the same scene from different viewpoints, and detect the redundant visual and depth information to prevent their transmission leading to a significant improvement in wireless channel usage efficiency and power savings. We envisage applications in which the environment is static, and rapid 3D mapping of an enclosed area of interest is required, such as disaster recovery and support operations after earthquakes or industrial accidents. Experimental results show that wireless channel utilization is improved by 250 and battery consumption is halved when the RPRR scheme is used instead of sending the sensor images independently.
Distributed source coding (DSC) algorithms @cite_10 @cite_34 @cite_66 @cite_36 @cite_17 are other promising approaches that can be used to reduce the redundant data in multiview VSN scenarios. Each DSC encoder operates independently, but at the same time, relies on joint decoding operations at the sink (remote monitoring station). The advantage of these approaches is that the camera sensors do not need to directly communicate the captured visual information with others in the network. Furthermore, these algorithms shift the computational complexity from the sensor nodes to the remote monitoring station, which fits the needs of VSNs well. However, the side information must be predicted as accurately as possible and the correlation structure should be able to be identified at the decoder side (remote monitoring station), without an accurate knowledge of the network topology and the poses of the sensors. These are the main disadvantages that prevent DSC algorithms from being widely implemented. A detailed discussion on multi-view image compression and transmission schemes in VSNs is presented in @cite_38 .
@cite_38 @cite_36 @cite_34 @cite_10 @cite_66 @cite_17
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Many applications in visual sensor networks (VSNs) demand the low-cost wireless transmission of video data. In this context, distributed video coding (DVC) has proven its potential to achieve state-of-the-art compression performance while maintaining low computational complexity of the encoder. Despite their proven capabilities, current DVC solutions overlook hardware constraints, and this renders them unsuitable for practical implementations. This paper introduces a DVC architecture that offers highly efficient wireless communication in real-world VSNs. The design takes into account the severe computational and memory constraints imposed by practical implementations on low-resolution visual sensors. We study performance-complexity trade-offs for feedback-channel removal, propose learning-based techniques for rate allocation, and investigate various simplifications of side information generation yielding real-time decoding. The proposed system is evaluated against H.264 AVC intra, Motion-JPEG, and our previously designed DVC prototype for low-resolution visual sensors. Extensive experimental results on various data show significant improvements in multiple configurations. The proposed encoder achieves real-time performance on a 1k-pixel visual sensor mote. Real-time decoding is performed on a Raspberry Pi single-board computer or a low-end notebook PC. To the best of our knowledge, the proposed codec is the first practical DVC deployment on low-resolution VSNs. Visual sensor networks (VSNs) are becoming increasingly popular in a number of application domains. A distinguishing characteristic of VSNs is to self-configure to minimize the need for operator control and to improve scalability. One of the areas of self-configuration is camera coverage control that is, how should cameras adjust their field-of-views to cover maximum targetsq This is an NP-hard problem. We show that the existing heuristics have a number of weaknesses that influence both coverage and overhead. Therefore, we first propose a computationally efficient centralized heuristic that provides near-optimal coverage for small-scale networks. However, it requires significant communication and computation overhead, making it unsuitable for large-scale networks. Thus, we develop a distributed algorithm that outperforms the existing distributed algorithm with lower communication overhead, at the cost of coverage accuracy. We show that the proposed heuristics guarantee to cover at least half of the targets covered by the optimal solution. Finally, to gain benefits of both centralized and distributed algorithms, we propose a hierarchical algorithm where cameras are decomposed into neighborhoods that coordinate their coverage using an elected local coordinator. We observe that the hierarchical algorithm provides scalable near-optimal coverage with networking cost significantly less than that of centralized and distributed solutions. In this article, a multiview image compression framework, which involves the use of Block-based Compressive Sensing (BCS) and Joint Multiphase Decoding (JMD), is proposed for a Visual Sensor Network (VSN). In the proposed framework, one of the sensor nodes is configured to serve as the reference node, the others as nonreference nodes. The images are encoded independently using the BCS to produce two observed measurements that are transmitted to the host workstation. In this case, the nonreference nodes always encoded the images (INR) at a lower subrate when compared with the images from the reference nodes (IR). The idea is to improve the reconstruction of INR using IR. After the two observed measurements are received by the host workstation, they are first decoded independently, then image registration is applied to align IR onto the same plane of INR. The aligned IR is then fused with INR, using wavelets to produce the projected image IP. Subsequently, the difference between the measurements of the IP and INR is calculated. The difference is then decoded and added to IP to produce the final reconstructed INR. The simulation results show that the proposed framework is able to improve the quality of INR on average by 2dB to 3dB at lower subrates when compared with other Compressive Sensing (CS)--based multiview image compression frameworks. In recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf previous milestones in the information revolution. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies. It relies on many components working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies in sensor networks is the distributed source coding (DSC), which refers to the compression of the multiple correlated sensor outputs that does not communicate with each other. DSC allows a many-to-one video coding paradigm that effectively swaps encoder-decoder complexity with respect to conventional video coding, thereby representing a fundamental concept shift in video processing. This article has presented an intensive discussion on two DSC techniques, namely Slepian-Wolf coding and Wyner-Ziv coding. The Slepian and Wolf coding have theoretically shown that separate encoding is as efficient as joint coding for lossless compression in channel coding.