Perimeter coverage scheduling in wireless sensor networks using sensors with a single continuous cover range

In target monitoring problem, it is generally assumed that the whole target object can be monitored by a single sensor if the target falls within its sensing range. Unfortunately, this assumption becomes invalid when the target object is very large that a sensor can only monitor part of it. In this paper, we study the perimeter coverage problem where the perimeter of a big object needs to be monitored, but each sensor can only cover a single continuous portion of the perimeter. We describe how to schedule the sensors so as to maximize the network lifetime in this problem. We formally prove that the perimeter coverage scheduling problem is NP-hard in general. However, polynomial time solution exists in some special cases. We further identify the sufficient conditions for a scheduling algorithm to be a 2-approximation solution to the general problem, and propose a simple distributed 2-approximation solution with a small message overhead. Copyright © 2010 K.-S. Hung and K.-S. Lui.published_or_final_versio

Belt-Barrier Construction Algorithm for WVSNs

[[abstract]]Previous research of barrier coverage did not consider breadth of coverage in Wireless Visual Sensor Networks (WVSNs). In this paper, we consider breadth to increase the Quality of Monitor (QoM) of WVSNs. The proposed algorithm is called Distributed β-Breadth Belt-Barrier construction algorithm (D-TriB). D-TriB constructs a belt-barrier with β breadth to offer β level of QoM, we call β-QoM. D-TriB can not only reduce the number of camera sensors required to construct a barrier but also ensure that any barrier with β-QoM in the network can be identified. Finally, the successful rate of the proposed algorithm is evaluated through simulations.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120401~20120404[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Shanghai, Chin

On perimeter coverage in wireless sensor networks with minimum cost

One of the major applications of sensor networks is tracking and surveillance. Very often, a single sensor is sufficient to monitor a single target. However, when the object is very large, several sensors have to work together to monitor the object continuously. In this paper, we study how to identify a set of sensors that can cover the perimeter of a large target with the minimum cost. We develop a novel distributed algorithm that requires fewer messages than existing mechanisms. Our algorithm can be extended to solve the problem when the sensor range is adjustable. We provide a formal proof of correctness and convergence time analysis of our algorithm. We further demonstrate the performance through extensive simulations. © 2011 Inderscience Enterprises Ltd.postprin

The Coverage Problem in Video-Based Wireless Sensor Networks: A Survey

Wireless sensor networks typically consist of a great number of tiny low-cost electronic devices with limited sensing and computing capabilities which cooperatively communicate to collect some kind of information from an area of interest. When wireless nodes of such networks are equipped with a low-power camera, visual data can be retrieved, facilitating a new set of novel applications. The nature of video-based wireless sensor networks demands new algorithms and solutions, since traditional wireless sensor networks approaches are not feasible or even efficient for that specialized communication scenario. The coverage problem is a crucial issue of wireless sensor networks, requiring specific solutions when video-based sensors are employed. In this paper, it is surveyed the state of the art of this particular issue, regarding strategies, algorithms and general computational solutions. Open research areas are also discussed, envisaging promising investigation considering coverage in video-based wireless sensor networks

Correlation-based communication in wireless multimedia sensor networks

Wireless multimedia sensor networks (WMSNs) are networks of 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 observations of camera sensors with overlapped coverage areas, which introduces substantial data redundancy in the network. In this dissertation, efficient communication schemes are designed for WMSNs by leveraging the correlation of visual information observed by camera sensors. First, a spatial correlation model is developed to estimate the correlation of visual information and the joint entropy of multiple correlated camera sensors. The compression performance of correlated visual information is then studied. An entropy-based divergence measure is proposed to predict the compression efficiency of performing joint coding on the images from correlated cameras. Based on the predicted compression efficiency, a clustered coding technique is proposed that maximizes the overall compression gain of the visual information gathered in WMSNs. The correlation of visual information is then utilized to design a network scheduling scheme to maximize the lifetime of WMSNs. Furthermore, as many WMSN applications require QoS support, a correlation-aware QoS routing algorithm is introduced that can efficiently deliver visual information under QoS constraints. Evaluation results show that, by utilizing the correlation of visual information in the communication process, the energy efficiency and networking performance of WMSNs could be improved significantly.PhDCommittee Chair: Akyildiz, Ian; Committee Member: Ammar, Mostafa; Committee Member: Ji, Chuanyi; Committee Member: Li, Ye; Committee Member: Romberg, Justi

Feature-based Image Comparison and Its Application in Wireless Visual Sensor Networks

This dissertation studies the feature-based image comparison method and its application in Wireless Visual Sensor Networks. Wireless Visual Sensor Networks (WVSNs), formed by a large number of low-cost, small-size visual sensor nodes, represent a new trend in surveillance and monitoring practices. Although each single sensor has very limited capability in sensing, processing and transmission, by working together they can achieve various high level tasks. Sensor collaboration is essential to WVSNs and normally performed among sensors having similar measurements, which are called neighbor sensors. The directional sensing characteristics of imagers and the presence of visual occlusion present unique challenges to neighborhood formation, as geographically-close neighbors might not monitor similar scenes. Besides, the energy resource on the WVSNs is also very tight, with wireless communication and complicated computation consuming most energy in WVSNs. Therefore the feature-based image comparison method has been proposed, which directly compares the captured image from each visual sensor in an economical way in terms of both the computational cost and the transmission overhead. The feature-based image comparison method compares different images and aims to find similar image pairs using a set of local features from each image. The image feature is a numerical representation of the raw image and can be more compact in terms of the data volume than the raw image. The feature-based image comparison contains three steps: feature detection, descriptor calculation and feature comparison. For the step of feature detection, the dissertation proposes two computationally efficient corner detectors. The first detector is based on the Discrete Wavelet Transform that provides multi-scale corner point detection and the scale selection is achieved efficiently through a Gaussian convolution approach. The second detector is based on a linear unmixing model, which treats a corner point as the intersection of two or three “line” bases in a 3 by 3 region. The line bases are extracted through a constrained Nonnegative Matrix Factorization (NMF) approach and the corner detection is accomplished through counting the number of contributing bases in the linear mixture. For the step of descriptor calculation, the dissertation proposes an effective dimensionality reduction algorithm for the high dimensional Scale Invariant Feature Transform (SIFT) descriptors. A set of 40 SIFT descriptor bases are extracted through constrained NMF from a large training set and all SIFT descriptors are then projected onto the space spanned by these bases, achieving dimensionality reduction. The efficiency of the proposed corner detectors have been proven through theoretical analysis. In addition, the effectiveness of the proposed corner detectors and the dimensionality reduction approach has been validated through extensive comparison with several state-of-the-art feature detector/descriptor combinations

Deployment, Coverage And Network Optimization In Wireless Video Sensor Networks For 3D Indoor Monitoring

As a result of extensive research over the past decade or so, wireless sensor networks (wsns) have evolved into a well established technology for industry, environmental and medical applications. However, traditional wsns employ such sensors as thermal or photo light resistors that are often modeled with simple omni-directional sensing ranges, which focus only on scalar data within the sensing environment. In contrast, the sensing range of a wireless video sensor is directional and capable of providing more detailed video information about the sensing field. Additionally, with the introduction of modern features in non-fixed focus cameras such as the pan, tilt and zoom (ptz), the sensing range of a video sensor can be further regarded as a fan-shape in 2d and pyramid-shape in 3d. Such uniqueness attributed to wireless video sensors and the challenges associated with deployment restrictions of indoor monitoring make the traditional sensor coverage, deployment and networked solutions in 2d sensing model environments for wsns ineffective and inapplicable in solving the wireless video sensor network (wvsn) issues for 3d indoor space, thus calling for novel solutions. In this dissertation, we propose optimization techniques and develop solutions that will address the coverage, deployment and network issues associated within wireless video sensor networks for a 3d indoor environment. We first model the general problem in a continuous 3d space to minimize the total number of required video sensors to monitor a given 3d indoor region. We then convert it into a discrete version problem by incorporating 3d grids, which can achieve arbitrary approximation precision by adjusting the grid granularity. Due in part to the uniqueness of the visual sensor directional sensing range, we propose to exploit the directional feature to determine the optimal angular-coverage of each deployed visual sensor. Thus, we propose to deploy the visual sensors from divergent directional angles and further extend k-coverage to ``k-angular-coverage\u27\u27, while ensuring connectivity within the network. We then propose a series of mechanisms to handle obstacles in the 3d environment. We develop efficient greedy heuristic solutions that integrate all these aforementioned considerations one by one and can yield high quality results. Based on this, we also propose enhanced depth first search (dfs) algorithms that can not only further improve the solution quality, but also return optimal results if given enough time. Our extensive simulations demonstrate the superiority of both our greedy heuristic and enhanced dfs solutions. Finally, this dissertation discusses some future research directions such as in-network traffic routing and scheduling issues