Controlling the Coverage of Wireless Sensors Network Using Coverage in Block Algorithm

This research investigate the modeling of Blocks, Present in the sensing field and its impact in the computation of coverage path in wireless sensor networks (WSNs). The solutions of these problems are proposed using techniques from Approximation algorithm. In order to accomplish the designated task successfully, sensors need to actuate, compute and disseminate the acquired information amongst them. Intuitively, coverage denotes the quality of sensing of a sensor node. While a sensor senses. It needs to communicate with its neighboring sensor nodes in order to disseminate the acquired data. That is where connectivity comes in to place. In fact, coverage and connectivity together measure the quality of service (QoS) of a sensor network. Coverage and connectivity in wireless sensor networks are not unrelated problems. Therefore, the goal of an optimal sensor deployment strategy is to have a globally connected network, while optimizing coverage at the same time. By optimizing coverage, the deployment strategy would guarantee that optimum area in the sensing field is covered by sensor, as required by the underlying application, whereas by ensuring that the network is connected, it is ensured that the sensed information is transmitted to other nodes and possibly to a centralized base station (called sink) which makes valuable decision for the application. Many recent and ongoing research in sensor networks focus on optimizing coverage and connectivity by optimizing node placement strategy, minimizing number of nodes to guarantee required degree of coverage, maximizing network lifetime by minimizing energy usage, computing the most and least sensed path in the given region and so on. To solve these optimizing problems related to coverage, exiting research uses mostly probabilistic technique based on random graph theory, randomized algorithm, computational geometry, and so on. Of particular interest to us is the problem of computing the coverage in block (CIB), where give

Development of a GIS-based method for sensor network deployment and coverage optimization

Au cours des dernières années, les réseaux de capteurs ont été de plus en plus utilisés dans différents contextes d’application allant de la surveillance de l’environnement au suivi des objets en mouvement, au développement des villes intelligentes et aux systèmes de transport intelligent, etc. Un réseau de capteurs est généralement constitué de nombreux dispositifs sans fil déployés dans une région d'intérêt. Une question fondamentale dans un réseau de capteurs est l'optimisation de sa couverture spatiale. La complexité de l'environnement de détection avec la présence de divers obstacles empêche la couverture optimale de plusieurs zones. Par conséquent, la position du capteur affecte la façon dont une région est couverte ainsi que le coût de construction du réseau. Pour un déploiement efficace d'un réseau de capteurs, plusieurs algorithmes d'optimisation ont été développés et appliqués au cours des dernières années. La plupart de ces algorithmes reposent souvent sur des modèles de capteurs et de réseaux simplifiés. En outre, ils ne considèrent pas certaines informations spatiales de l'environnement comme les modèles numériques de terrain, les infrastructures construites humaines et la présence de divers obstacles dans le processus d'optimisation. L'objectif global de cette thèse est d'améliorer les processus de déploiement des capteurs en intégrant des informations et des connaissances géospatiales dans les algorithmes d'optimisation. Pour ce faire, trois objectifs spécifiques sont définis. Tout d'abord, un cadre conceptuel est développé pour l'intégration de l'information contextuelle dans les processus de déploiement des réseaux de capteurs. Ensuite, sur la base du cadre proposé, un algorithme d'optimisation sensible au contexte local est développé. L'approche élargie est un algorithme local générique pour le déploiement du capteur qui a la capacité de prendre en considération de l'information spatiale, temporelle et thématique dans différents contextes d'applications. Ensuite, l'analyse de l'évaluation de la précision et de la propagation d'erreurs est effectuée afin de déterminer l'impact de l'exactitude des informations contextuelles sur la méthode d'optimisation du réseau de capteurs proposée. Dans cette thèse, l'information contextuelle a été intégrée aux méthodes d'optimisation locales pour le déploiement de réseaux de capteurs. L'algorithme développé est basé sur le diagramme de Voronoï pour la modélisation et la représentation de la structure géométrique des réseaux de capteurs. Dans l'approche proposée, les capteurs change leur emplacement en fonction des informations contextuelles locales (l'environnement physique, les informations de réseau et les caractéristiques des capteurs) visant à améliorer la couverture du réseau. La méthode proposée est implémentée dans MATLAB et est testée avec plusieurs jeux de données obtenus à partir des bases de données spatiales de la ville de Québec. Les résultats obtenus à partir de différentes études de cas montrent l'efficacité de notre approche.In recent years, sensor networks have been increasingly used for different applications ranging from environmental monitoring, tracking of moving objects, development of smart cities and smart transportation system, etc. A sensor network usually consists of numerous wireless devices deployed in a region of interest. A fundamental issue in a sensor network is the optimization of its spatial coverage. The complexity of the sensing environment with the presence of diverse obstacles results in several uncovered areas. Consequently, sensor placement affects how well a region is covered by sensors as well as the cost for constructing the network. For efficient deployment of a sensor network, several optimization algorithms are developed and applied in recent years. Most of these algorithms often rely on oversimplified sensor and network models. In addition, they do not consider spatial environmental information such as terrain models, human built infrastructures, and the presence of diverse obstacles in the optimization process. The global objective of this thesis is to improve sensor deployment processes by integrating geospatial information and knowledge in optimization algorithms. To achieve this objective three specific objectives are defined. First, a conceptual framework is developed for the integration of contextual information in sensor network deployment processes. Then, a local context-aware optimization algorithm is developed based on the proposed framework. The extended approach is a generic local algorithm for sensor deployment, which accepts spatial, temporal, and thematic contextual information in different situations. Next, an accuracy assessment and error propagation analysis is conducted to determine the impact of the accuracy of contextual information on the proposed sensor network optimization method. In this thesis, the contextual information has been integrated in to the local optimization methods for sensor network deployment. The extended algorithm is developed based on point Voronoi diagram in order to represent geometrical structure of sensor networks. In the proposed approach sensors change their location based on local contextual information (physical environment, network information and sensor characteristics) aiming to enhance the network coverage. The proposed method is implemented in MATLAB and tested with several data sets obtained from Quebec City spatial database. Obtained results from different case studies show the effectiveness of our approach

An Energy-Efficient Distributed Algorithm for k-Coverage Problem in Wireless Sensor Networks

Wireless sensor networks (WSNs) have recently achieved a great deal of attention due to its numerous attractive applications in many different fields. Sensors and WSNs possesses a number of special characteristics that make them very promising in many applications, but also put on them lots of constraints that make issues in sensor network particularly difficult. These issues may include topology control, routing, coverage, security, and data management. In this thesis, we focus our attention on the coverage problem. Firstly, we define the Sensor Energy-efficient Scheduling for k-coverage (SESK) problem. We then solve it by proposing a novel, completely localized and distributed scheduling approach, naming Distributed Energy-efficient Scheduling for k-coverage (DESK) such that the energy consumption among all the sensors is balanced, and the network lifetime is maximized while still satisfying the k-coverage requirement. Finally, in related work section we conduct an extensive survey of the existing work in literature that focuses on with the coverage problem

Multi-Level Multi-Objective Programming and Optimization for Integrated Air Defense System Disruption

The U.S. military\u27s ability to project military force is being challenged. This research develops and demonstrates the application of three respective sensor location, relocation, and network intrusion models to provide the mathematical basis for the strategic engagement of emerging technologically advanced, highly-mobile, Integrated Air Defense Systems. First, we propose a bilevel mathematical programming model for locating a heterogeneous set of sensors to maximize the minimum exposure of an intruder\u27s penetration path through a defended region. Next, we formulate a multi-objective, bilevel optimization model to relocate surviving sensors to maximize an intruder\u27s minimal expected exposure to traverse a defended border region, minimize the maximum sensor relocation time, and minimize the total number of sensors requiring relocation. Lastly, we present a trilevel, attacker-defender-attacker formulation for the heterogeneous sensor network intrusion problem to optimally incapacitate a subset of the defender\u27s sensors and degrade a subset of the defender\u27s network to ultimately determine the attacker\u27s optimal penetration path through a defended network

Intrusion Detection Mechanism for Empowered Intruders Using IDEI

In the past, intrusion detection has been extensively investigated as a means of ensuring the security of wireless sensor networks. Anti-recon technology has made it possible for an attacker to get knowledge about the detecting nodes and plot a route around them in order to evade detection. An "empowered intruder" is one who poses new threats to current intrusion detection technologies. Furthermore, the intended impact of detection may not be obtained in certain subareas owing to gaps in coverage caused by the initial deployment of detection nodes at random. A vehicle collaboration sensing network model is proposed to solve these difficulties, in which mobile sensing cars and static sensor nodes work together to identify intrusions by empowered intruders. An algorithm for mobile sensing vehicles, called Intrusion Detection Mechanism for Empowered Intruders(IDEI), and a sleep-scheduling technique for static nodes form the basis of our proposal. Sophisticated intruders will be tracked by mobile sensors, which will fill in the gaps in coverage, while static nodes follow a sleep schedule and will be woken when the intruder is discovered close. Our solution is compared to current techniques like Kinetic Theory Based Mobile Sensor Network (KMsn)and Mean Time to Attacks (MTTA) in terms of intrusion detection performance, energy usage, and sensor node movement distance. IDEI's parameter sensitivity is also examined via comprehensive simulations. It is clear from the theoretical analysis and simulation findings that our idea is more efficient and available

Achieving Minimum Coverage Breach under Bandwidth Constraints in Wireless Sensor Networks

This paper addresses the coverage breach problem in wireless sensor networks with limited bandwidths. In wireless sensor networks, sensor nodes are powered by batteries. To make efficient use of battery energy is critical to sensor network lifetimes. When targets are redundantly covered by multiple sensors, especially in stochastically deployed sensor networks, it is possible to save battery energy by organizing sensors into mutually exclusive subsets and alternatively activating only one subset at any time. Active nodes are responsible for sensing, computing and communicating. While the coverage of each subset is an important metric for sensor organization, the size of each subset also plays an important role in sensor network performance because when active sensors periodically send data to base stations, contention for channel access must be considered. The number of available channels imposes a limit on the cardinality of each subset. Coverage breach happens when a subset of sensors cannot completely cover all the targets. To make efficient use of both energy and bandwidth with a minimum coverage breach is the goal of sensor network design. This paper presents the minimum breach problem using a mathematical model, studies the computational complexity of the problem, and provides two approximate heuristics. Effects of increasing the number of channels and increasing the number of sensors on sensor network coverage are studied through numerical simulations. Overall, the simulation results reveal that when the number of sensors increases, network lifetimes can be improved without loss of network coverage if there is no bandwidth constraint; with bandwidth constraints, network lifetimes may be improved further at the cost of coverage breach

A comprehensive survey of wireless body area networks on PHY, MAC, and network layers solutions

Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted