Centralized Algorithms for Redundant Coverage Maximization in Wireless Visual Sensor Networks

Wireless visual sensor networks provide valuable information for many monitoring and control applications. Sometimes, a set of targets needs to be monitored by deployed visual sensors. For those networks, however, some active visual sources may fail, potentially degrading the application monitoring quality when targets become uncovered. Moreover, some applications may need different perspectives of the same target. As visual sensors will be used to monitor a set of targets, a high level of monitoring redundancy may be required and an effective way to achieve it is assuring that targets are being concurrently viewed by more than one visual sensor. We propose a centralized greedy algorithm to enhance redundancy in wireless visual sensor networks when visual sensors with adjustable orientations are deployed. Additionally, as some targets may be more critical for the application, we propose a priority-based configuration of the sensors' poses in order to achieve an optimized configuration for the visual sensors.

Many applications use virtual view to see objects or sceneries in certain directions where there is no angle of visual sensor viewing the directions. Generating indirect virtual view images also save visual sensor node in a network. In addition visual sensors have abilities to give more information related to virtual view images. In fact, generating virtual view images using Wireless Visual Sensor Network is a challenging task, due to the limitation of energy, computation, and bandwidth. Therefore this paper proposes using combination of camera selection method with parameters optimization of JPEG 2000 image compression and image transmission strategy applying mutual information criteria. The camera selection method based on the smallest value of disparity was adopted to reduce transmission of number of images. In each node of visual sensor, the optimization of JPEG 2000 image compression using parameters selection such as DWT level and bit rate was applied to reduce the size of the images. However image quality in PSNR has to be preserved. By implementing the sensor selection, the image compression, and the mutual information strategic, the study results that the total energy required to send one compressed image can be reduced to 10.27% of energy consumption of uncompressed image. Thus the image will be faster delivered.

Wireless visual sensor networks (WVSNs) comprise a large number of battery-powered visual sensors with limited processing and transmission capacity in the field of interest. Hence, energy conservation is an important issue in resource-constrained WVSNs. In-network data fusion, clustering networks has been proved to be an effective scheme in reducing energy consumption. However, in most clustering algorithms, clustering may introduce bottlenecks to WVSNs which bring extra delay in the processes of data collection and forwarding. Such delay can be reduced by the design of routing schemes. In this paper, we propose an approach of energy-delay trade-off (EDT) based on two-sided matching theory. First, we determine a balance clustering structure which can balance energy consumption and delay in random-densely-deployed WVSNs. Then, the problem formulation of EDT problem in the balance clustering structure is provided. Moreover, the transformation strategy based on two-sided matching (TS-TSM) is proposed. After the TS-TSM, the EDT problem is transformed into a stable two-sided matching (STSM) problem with single objective function and the computational complexity is reduced. Furthermore, most of floating-point operations in original EDT problem are converted into integer operations which are easy to parallel compute. Finally, an improved deferred-acceptance algorithm (IDAA) is provided to generate a stable matching solution as the EDT scheme. Evaluation results demonstrate that the EDT approach can reduce delay in data aggregation processes and keep the total energy consumption at low levels. Further simulations show that the EDT approach is a fast approach, and it is not inferior to the existing algorithms.

Enhancing the availability of wireless visual sensor networks: Selecting redundant nodes in networks with occlusion

Traditional wireless sensor networks (WSNs) transmit the scalar data (e.g., temperature and irradiation) to the sink node. A new wireless visual sensor network (WVSN) that can transmit images data is a more promising solution than the WSN on sensing, detecting, and monitoring the environment to enhance awareness of the cyber, physical, and social contexts of our daily activities. However, the size of image data is much bigger than the scalar data that makes image transmission a challenging issue in battery-limited WVSN. In this paper, we study the energy efficient image aggregation scheme in WVSN. Image aggregation is a possible way to eliminate the redundant portions of the image captured by different data source nodes. Hence, transmission power could be reduced via the image aggregation scheme. However, image aggregation requires image processing that incurs node processing power. Besides the additional energy consumption from node processing, there is another MAC-aware retransmission energy loss from image aggregation. In this paper, we first propose the mathematical model to capture these three factors (image transmission, image processing, and MAC retransmission) in WVSN. Numerical results based on the mathematical model and real WVSN sensor node (i.e., Meerkats node) are performed to optimize the energy consumption tradeoff between image transmission, image processing, and MAC retransmission.

Wireless visual sensor networks (WVSNs) are composed of a large number of visual sensor nodes covering a specific geographical region. This paper addresses the target detection problem within WVSNs where visual sensor nodes are left unattended for long-term deployment. As battery energy is a critical issue it is always challenging to maximize the network’s lifetime. In order to reduce energy consumption, nodes undergo cycles of active-sleep periods that save their battery energy by switching sensor nodes ON and OFF, according to predefined duty cycles. Moreover, adaptive compressive sensing is expected to dynamically reduce the size of transmitted data through the wireless channel, saving communication bandwidth and consequently saving energy. This paper derives for the first time an analytical framework for selecting node’s duty cycles and dynamically choosing the appropriate compression rates for the captured images and videos based on their sparsity nature. This reduces energy waste by reaching the maximum compression rate for each dataset without compromising the probability of detection. Experiments were conducted on different standard datasets resembling different scenes; indoor and outdoor, for single and multiple targets detection. Moreover, datasets were chosen with different sparsity levels to investigate the effect of sparsity on the compression rates. Results showed that by selecting duty cycles and dynamically choosing the appropriate compression rates, the desired performance of detection can be achieved with adaptive CS and at the same time saving energy, where the proposed framework results in an 70% on average energy saving as compared to transmitting the captured image without CS.

Nowadays, Wireless visual sensor Network (WVSN) is gaining significant importance and has several distinctive applications. WVSNs provides better results when it come to object detection since it consists of several tiny camera sensors which can capture the object's image and helps in identifying the object more precisely. There are some limitations of WVSN like battery constraints, limited storage memory so to overcome these limitations the proposed method is using block based compressive sensing technique (BCS) which applies compression only to the required blocks of image rather than applying it on whole image frame. The proposed method also incorporates a change detecting algorithm which will work only if the Mean square error (MSE) comparator crosses its predefined threshold value. The main aim of the proposed method is to achieve faster reconstruction of image with acceptable image quality so, to attain the required performance contourlet transform and projection driven compressive sensing recovery technique has been used. Also to increase the speed of reconstruction wiener filter is incorporated into iterative projected landweber compressive sensing recovery technique. The results have shown that the proposed block based compressive sensing with smooth projected landweber performs better than other methods as compared to quality of the reconstructed image.

Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.

Communication links in low-power wireless visual sensor networks (WVSNs) are subject to short-term and long-term noise variations. These variations can cause a WVSN to exhibit prolonged or periodic transitional or bursty transmission performance. In this paper, we present our work on how to generate noise traces that simulate real-world transitional and bursty network behavior in TOSSIM. We develop a toolset called BurstyGen for TOSSIM which can facilitate WVSN protocol designers and application developers to better understand WVSN performance under these conditions. BurstyGen allows users to model both short time-scale and long time-scale variations in WVSN noise environments for the simulation and testing of WVSN system algorithms and protocols.

Wireless Sensor Networking is a sensor information gathering technology that has a wide range of applications in numerous fields. However due to the fact that sensor nodes have limited battery and are deployed in remote and harsh environments, energy efficient and real time transmission of the information are still open challenges. Since in a wireless sensor network data transmission is the most power consuming task, so far most useful techniques for the purpose of energy efficient and real time transmissions are based on data compression, the majority of them are based on wavelet transform. This paper give performance analysis of different wavelets for energy efficient and real time image data transmission in application like environment monitoring using wireless visual sensor networks. The performance evaluation shows that Haar wavelet is better in terms of energy efficiency and transmission time.

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VSN is different from traditional WSN that collect and transmit images/video instead of scalar data to the sink. Images provide richer information than scalar data that enable VSN to be a promising technology on security monitoring and surveillance. Image transmission in VSN is expansive due to its large image size and significant energy consumption. One possible way to reduce the image size is to aggregate the received images on the sensor nodes before transmission. By eliminating the redundant image, image aggregation in VSN could transmit the overlapped region of the images only once to reduce the total energy consumption. However, image aggregation requires image processing at the aggregator node that incurs additional node processing energy. In this paper, we study the energy consumption tradeoff between node image processing and image aggregation. We derive the image aggregation energy consumption model that considers the image processing and image transmission. From the simulation results, image aggregation scheme does help lowering the energy consumption. As compared to the non-aggregation scheme, image aggregation scheme consumes only 46% of the energy consumption. In addition, we also find that the threshold for the number of aggregated nodes. Above this threshold, increasing the number aggregated nodes does not help on reducing the energy consumption. This could help determining the number of aggregated nodes under different degree of aggregation to achieve minimum energy consumption.

Multi-Camera Activation Scheme for Target Tracking with Dynamic Active Camera Group and Virtual Grid-Based Target Recovery

Aiming at the uneven random coverage distribution of wireless sensor networks sensor nodes, the sparrow search algorithm (SSA) is used to optimize the node coverage of wireless sensor networks. To improve the global search capability of SSA, the algorithm is improved and applied to the wireless sensor networks of bridge monitoring. For enhancing the coverage of the wireless sensor networks, this article uses two improved methods. One is to use the good point set theory to make the initial population evenly distributed; another is to introduce a weight factor to speed up its convergence. The experiments have proved the reliability and rationality of the algorithm. The improved algorithm is superior to other meta-heuristic algorithms and provides a new idea for optimizing bridge monitoring wireless sensor networks coverage.

A coverage optimization method is suggested to improved node coverage in wireless sensor networks (WSNs), decrease node redundancy, and lengthen network lifetime, this method is based on the adaptive grey wolf arithmetic optimization algorithm The exploitation and exploration are first balanced using a cosine acceleration function. Then, to boost the method's performance, the grey wolf optimizer and arithmetic optimization algorithm are combined. In comparison to the conventional coverage optimization algorithm, our simulation results could improve arithmetic optimization algorithm has higher convergence efficiency and coverage rate, decreases network loss, and significantly lengthens the network life cycle.