3,589 research outputs found

    Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

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    The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

    Spatio-temporal coverage optimization of sensor networks

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    Les rĂ©seaux de capteurs sont formĂ©s d’un ensemble de dispositifs capables de prendre individuellement des mesures d’un environnement particulier et d’échanger de l’information afin d’obtenir une reprĂ©sentation de haut niveau sur les activitĂ©s en cours dans la zone d’intĂ©rĂȘt. Une telle dĂ©tection distribuĂ©e, avec de nombreux appareils situĂ©s Ă  proximitĂ© des phĂ©nomĂšnes d’intĂ©rĂȘt, est pertinente dans des domaines tels que la surveillance, l’agriculture, l’observation environnementale, la surveillance industrielle, etc. Nous proposons dans cette thĂšse plusieurs approches pour effectuer l’optimisation des opĂ©rations spatio-temporelles de ces dispositifs, en dĂ©terminant oĂč les placer dans l’environnement et comment les contrĂŽler au fil du temps afin de dĂ©tecter les cibles mobiles d’intĂ©rĂȘt. La premiĂšre nouveautĂ© consiste en un modĂšle de dĂ©tection rĂ©aliste reprĂ©sentant la couverture d’un rĂ©seau de capteurs dans son environnement. Nous proposons pour cela un modĂšle 3D probabiliste de la capacitĂ© de dĂ©tection d’un capteur sur ses abords. Ce modĂšle inĂšgre Ă©galement de l’information sur l’environnement grĂące Ă  l’évaluation de la visibilitĂ© selon le champ de vision. À partir de ce modĂšle de dĂ©tection, l’optimisation spatiale est effectuĂ©e par la recherche du meilleur emplacement et l’orientation de chaque capteur du rĂ©seau. Pour ce faire, nous proposons un nouvel algorithme basĂ© sur la descente du gradient qui a Ă©tĂ© favorablement comparĂ©e avec d’autres mĂ©thodes gĂ©nĂ©riques d’optimisation «boites noires» sous l’aspect de la couverture du terrain, tout en Ă©tant plus efficace en terme de calculs. Une fois que les capteurs placĂ©s dans l’environnement, l’optimisation temporelle consiste Ă  bien couvrir un groupe de cibles mobiles dans l’environnement. D’abord, on effectue la prĂ©diction de la position future des cibles mobiles dĂ©tectĂ©es par les capteurs. La prĂ©diction se fait soit Ă  l’aide de l’historique des autres cibles qui ont traversĂ© le mĂȘme environnement (prĂ©diction Ă  long terme), ou seulement en utilisant les dĂ©placements prĂ©cĂ©dents de la mĂȘme cible (prĂ©diction Ă  court terme). Nous proposons de nouveaux algorithmes dans chaque catĂ©gorie qui performent mieux ou produits des rĂ©sultats comparables par rapport aux mĂ©thodes existantes. Une fois que les futurs emplacements de cibles sont prĂ©dits, les paramĂštres des capteurs sont optimisĂ©s afin que les cibles soient correctement couvertes pendant un certain temps, selon les prĂ©dictions. À cet effet, nous proposons une mĂ©thode heuristique pour faire un contrĂŽle de capteurs, qui se base sur les prĂ©visions probabilistes de trajectoire des cibles et Ă©galement sur la couverture probabiliste des capteurs des cibles. Et pour terminer, les mĂ©thodes d’optimisation spatiales et temporelles proposĂ©es ont Ă©tĂ© intĂ©grĂ©es et appliquĂ©es avec succĂšs, ce qui dĂ©montre une approche complĂšte et efficace pour l’optimisation spatio-temporelle des rĂ©seaux de capteurs.Sensor networks consist in a set of devices able to individually capture information on a given environment and to exchange information in order to obtain a higher level representation on the activities going on in the area of interest. Such a distributed sensing with many devices close to the phenomena of interest is of great interest in domains such as surveillance, agriculture, environmental monitoring, industrial monitoring, etc. We are proposing in this thesis several approaches to achieve spatiotemporal optimization of the operations of these devices, by determining where to place them in the environment and how to control them over time in order to sense the moving targets of interest. The first novelty consists in a realistic sensing model representing the coverage of a sensor network in its environment. We are proposing for that a probabilistic 3D model of sensing capacity of a sensor over its surrounding area. This model also includes information on the environment through the evaluation of line-of-sight visibility. From this sensing model, spatial optimization is conducted by searching for the best location and direction of each sensor making a network. For that purpose, we are proposing a new algorithm based on gradient descent, which has been favourably compared to other generic black box optimization methods in term of performance, while being more effective when considering processing requirements. Once the sensors are placed in the environment, the temporal optimization consists in covering well a group of moving targets in the environment. That starts by predicting the future location of the mobile targets detected by the sensors. The prediction is done either by using the history of other targets who traversed the same environment (long term prediction), or only by using the previous displacements of the same target (short term prediction). We are proposing new algorithms under each category which outperformed or produced comparable results when compared to existing methods. Once future locations of targets are predicted, the parameters of the sensors are optimized so that targets are properly covered in some future time according to the predictions. For that purpose, we are proposing a heuristics for making such sensor control, which deals with both the probabilistic targets trajectory predictions and probabilistic coverage of sensors over the targets. In the final stage, both spatial and temporal optimization method have been successfully integrated and applied, demonstrating a complete and effective pipeline for spatiotemporal optimization of sensor networks

    Optimal one-dimensional coverage by unreliable sensors

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    This paper regards the problem of optimally placing unreliable sensors in a one-dimensional environment. We assume that sensors can fail with a certain probability and we minimize the expected maximum distance from any point in the environment to the closest active sensor. We provide a computational method to find the optimal placement and we estimate the relative quality of equispaced and random placements. We prove that the former is asymptotically equivalent to the optimal placement when the number of sensors goes to infinity, with a cost ratio converging to 1, while the cost of the latter remains strictly larger.Comment: 21 pages 2 figure

    Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

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    Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

    Resilient Wireless Sensor Networks Using Topology Control: A Review

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    Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k − 1 nodes while the rest of nodes remain connected, the network is called k − connected. k is one of the most important indicators for WSNs’ self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k − connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs

    Localisation in wireless sensor networks for disaster recovery and rescuing in built environments

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    A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyProgress in micro-electromechanical systems (MEMS) and radio frequency (RF) technology has fostered the development of wireless sensor networks (WSNs). Different from traditional networks, WSNs are data-centric, self-configuring and self-healing. Although WSNs have been successfully applied in built environments (e.g. security and services in smart homes), their applications and benefits have not been fully explored in areas such as disaster recovery and rescuing. There are issues related to self-localisation as well as practical constraints to be taken into account. The current state-of-the art communication technologies used in disaster scenarios are challenged by various limitations (e.g. the uncertainty of RSS). Localisation in WSNs (location sensing) is a challenging problem, especially in disaster environments and there is a need for technological developments in order to cater to disaster conditions. This research seeks to design and develop novel localisation algorithms using WSNs to overcome the limitations in existing techniques. A novel probabilistic fuzzy logic based range-free localisation algorithm (PFRL) is devised to solve localisation problems for WSNs. Simulation results show that the proposed algorithm performs better than other range free localisation algorithms (namely DVhop localisation, Centroid localisation and Amorphous localisation) in terms of localisation accuracy by 15-30% with various numbers of anchors and degrees of radio propagation irregularity. In disaster scenarios, for example, if WSNs are applied to sense fire hazards in building, wireless sensor nodes will be equipped on different floors. To this end, PFRL has been extended to solve sensor localisation problems in 3D space. Computational results show that the 3D localisation algorithm provides better localisation accuracy when varying the system parameters with different communication/deployment models. PFRL is further developed by applying dynamic distance measurement updates among the moving sensors in a disaster environment. Simulation results indicate that the new method scales very well

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

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    Au cours des derniĂšres annĂ©es, les rĂ©seaux de capteurs ont Ă©tĂ© de plus en plus utilisĂ©s dans diffĂ©rents contextes d’application allant de la surveillance de l’environnement au suivi des objets en mouvement, au dĂ©veloppement des villes intelligentes et aux systĂšmes de transport intelligent, etc. Un rĂ©seau de capteurs est gĂ©nĂ©ralement constituĂ© de nombreux dispositifs sans fil dĂ©ployĂ©s dans une rĂ©gion d'intĂ©rĂȘt. Une question fondamentale dans un rĂ©seau de capteurs est l'optimisation de sa couverture spatiale. La complexitĂ© de l'environnement de dĂ©tection avec la prĂ©sence de divers obstacles empĂȘche la couverture optimale de plusieurs zones. Par consĂ©quent, la position du capteur affecte la façon dont une rĂ©gion est couverte ainsi que le coĂ»t de construction du rĂ©seau. Pour un dĂ©ploiement efficace d'un rĂ©seau de capteurs, plusieurs algorithmes d'optimisation ont Ă©tĂ© dĂ©veloppĂ©s et appliquĂ©s au cours des derniĂšres annĂ©es. La plupart de ces algorithmes reposent souvent sur des modĂšles de capteurs et de rĂ©seaux simplifiĂ©s. En outre, ils ne considĂšrent pas certaines informations spatiales de l'environnement comme les modĂšles numĂ©riques de terrain, les infrastructures construites humaines et la prĂ©sence de divers obstacles dans le processus d'optimisation. L'objectif global de cette thĂšse est d'amĂ©liorer les processus de dĂ©ploiement des capteurs en intĂ©grant des informations et des connaissances gĂ©ospatiales dans les algorithmes d'optimisation. Pour ce faire, trois objectifs spĂ©cifiques sont dĂ©finis. Tout d'abord, un cadre conceptuel est dĂ©veloppĂ© pour l'intĂ©gration de l'information contextuelle dans les processus de dĂ©ploiement des rĂ©seaux de capteurs. Ensuite, sur la base du cadre proposĂ©, un algorithme d'optimisation sensible au contexte local est dĂ©veloppĂ©. L'approche Ă©largie est un algorithme local gĂ©nĂ©rique pour le dĂ©ploiement du capteur qui a la capacitĂ© de prendre en considĂ©ration de l'information spatiale, temporelle et thĂ©matique dans diffĂ©rents contextes d'applications. Ensuite, l'analyse de l'Ă©valuation de la prĂ©cision et de la propagation d'erreurs est effectuĂ©e afin de dĂ©terminer l'impact de l'exactitude des informations contextuelles sur la mĂ©thode d'optimisation du rĂ©seau de capteurs proposĂ©e. Dans cette thĂšse, l'information contextuelle a Ă©tĂ© intĂ©grĂ©e aux mĂ©thodes d'optimisation locales pour le dĂ©ploiement de rĂ©seaux de capteurs. L'algorithme dĂ©veloppĂ© est basĂ© sur le diagramme de VoronoĂŻ pour la modĂ©lisation et la reprĂ©sentation de la structure gĂ©omĂ©trique des rĂ©seaux de capteurs. Dans l'approche proposĂ©e, les capteurs change leur emplacement en fonction des informations contextuelles locales (l'environnement physique, les informations de rĂ©seau et les caractĂ©ristiques des capteurs) visant Ă  amĂ©liorer la couverture du rĂ©seau. La mĂ©thode proposĂ©e est implĂ©mentĂ©e dans MATLAB et est testĂ©e avec plusieurs jeux de donnĂ©es obtenus Ă  partir des bases de donnĂ©es spatiales de la ville de QuĂ©bec. Les rĂ©sultats obtenus Ă  partir de diffĂ©rentes Ă©tudes de cas montrent l'efficacitĂ© 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

    The Deployment in the Wireless Sensor Networks: Methodologies, Recent Works and Applications

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    International audienceThe wireless sensor networks (WSN) is a research area in continuous evolution with a variety of application contexts. Wireless sensor networks pose many optimization problems, particularly because sensors have limited capacity in terms of energy, processing and memory. The deployment of sensor nodes is a critical phase that significantly affects the functioning and performance of the network. Often, the sensors constituting the network cannot be accurately positioned, and are scattered erratically. To compensate the randomness character of their placement, a large number of sensors is typically deployed, which also helps to increase the fault tolerance of the network. In this paper, we are interested in studying the positioning and placement of sensor nodes in a WSN. First, we introduce the problem of deployment and then we present the latest research works about the different proposed methods to solve this problem. Finally, we mention some similar issues related to the deployment and some of its interesting applications

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

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    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
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