Enhancing Redundancy in Wireless Visual Sensor Networks for Target Coverage

Visual sensors can be used to retrieve still images or video streams in a large set of monitoring applications, providing valuable information of the monitored environment. In fact, camera-enabled sensors are directional and the viewed area depends on their orientations. As the positions and orientations of the embedded cameras may not be optimal after deployment, the area viewed by sensors may be adjusted using some optimization algorithm. Although the problem of coverage optimization has been addressed by some research works, coverage maximization arises as a challenging problem where the number of camera views over a set of targets has to be maximized for higher availability and increased number of viewed perspectives of targets. This paper proposes centralized algorithms to compute orientations of rotatable cameras to achieve maximized coverage in wireless visual sensor networks, bringing significant results to this research area.

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.

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

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.

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.

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.

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.

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.

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.

Wireless visual sensor networks are potentially applicable for several emerging applications. Since the resource-limited restriction for a visual sensor node (VSN), efficient resource allocation for video compression is challenging. In this paper, a power-rate-distortion (PRD) model-based low-complexity multiview video codec is proposed. Our PRD model is used to characterize the relationship between the available resources and the RD performance of our video codec. More specifically, an RD function in terms of the percentages for different coding modes of blocks and the target bit rate under the available resource constraints is derived for optimal coding mode decision. Analytic and simulation results are provided to verify the resource scalability and accuracy of our PRD model in facilitating our low-complexity multiview video codec to achieve good performance.

Greenhouses are proliferating across Canada. Greenhouse crop production requires considerable attention. The only way to maintain the production growth is by controlling the greenhouse atmosphere and monitoring the plants so that they remain healthy in the greenhouse. In this paper, we utilize a Wireless Visual Sensor Network (WVSN) with machine learning and image processing to observe any deficiency, pest, or disease presenting on the leaves of the plants. We distribute camera sensors throughout the greenhouse. Each camera sensor node captures an image from inside the greenhouse and use machine learning and image processing techniques to detect the presence of fungus. When a fungus is detected, the camera sensor node sends a message to the sensor node via the wireless sensor network to measure the humidity and then send a message to the actuator to re-set accordingly. This paper demonstrates how Hough forest machine learning and image processing can be successful in detecting fungus present on crop plant leaves from the images taken from camera sensors in the greenhouse. Cross-validation was applied to measure the performance of the system. The results are highly promising. There was a 94% success rate in detecting the fungus.

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim is to design low power WVSN surveillance application using adaptive Compressive Sensing (CS) which is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In this paper, an adaptive block CS technique is proposed and implemented to represent the high volume of captured images in a way for energy efficient wireless transmission and minimum storage. Furthermore, to achieve energy-efficient target detection and tracking with high detection reliability and robust tracking, to maximize the lifetime of sensor nodes as they can be left for months without any human interactions. Adaptive CS is expected to dynamically achieve higher compression rates depending on the sparsity nature of different datasets, while only compressing relative blocks in the image that contain the target to be tracked instead of compressing the whole image. Hence, saving power and increasing compression rates. Least mean square adaptive filter is used to predicts target's next location to investigate the effect of CS on the tracking performance. The tracking is achieved in both indoor and outdoor environments for single/multi targets. Results have shown that with adaptive block CS up to 20 % measurements of data are required to be transmitted while preserving the required performance for target detection and tracking.

In this work we consider an event-driven wireless visual sensor network (WVSN) comprised of untethered camera nodes and scalar sensors deployed in a hostile environment. In the event-driven paradigm, each camera node transmits a surveillance frame to the cluster-head only if an event of interest was captured in the frame, for energy and bandwidth conservation. We thus examine a simple image processing algorithm at the camera nodes based on difference frames and the chi-squared detector. We show that the test statistic of the chi-squared detector is equivalent to that of a robust (non-parametric) detector and that this simple algorithm performs well on indoor surveillance sequences and some, but not all, outdoor sequences. In outdoor sequences containing significant changes in background and lighting, this simple detector may produce a high probability of error and benefits from the inclusion of scalar sensor decisions. The scalar sensor decisions are, however, prone to attack and may exhibit errors that are arbitrarily frequent, pervasive throughout the network and difficult to predict. To achieve attack prediction and mitigation given an attacker whose actions are not known a priori, we employ game-theoretic analysis. We show that the scalar sensor error can be controlled through cluster-head checking and appropriate selection of cluster size n. Given this attack mitigation, we employ real-life sequences to determine the total probability of error when individual and combined decisions are utilized and we discuss the ensuing ramifications and performance issues.

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