4,428 research outputs found

    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

    Distributed anti-flocking control for mobile surveillance systems

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    2014-2015 > Academic research: refereed > Refereed conference paperpreprint_postprin

    Reliable cost-optimal deployment of wireless sensor networks

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    Wireless Sensor Networks (WSNs) technology is currently considered one of the key technologies for realizing the Internet of Things (IoT). Many of the important WSNs applications are critical in nature such that the failure of the WSN to carry out its required tasks can have serious detrimental effects. Consequently, guaranteeing that the WSN functions satisfactorily during its intended mission time, i.e. the WSN is reliable, is one of the fundamental requirements of the network deployment strategy. Achieving this requirement at a minimum deployment cost is particularly important for critical applications in which deployed SNs are equipped with expensive hardware. However, WSN reliability, defined in the traditional sense, especially in conjunction with minimizing the deployment cost, has not been considered as a deployment requirement in existing WSN deployment algorithms to the best of our knowledge. Addressing this major limitation is the central focus of this dissertation. We define the reliable cost-optimal WSN deployment as the one that has minimum deployment cost with a reliability level that meets or exceeds a minimum level specified by the targeted application. We coin the problem of finding such deployments, for a given set of application-specific parameters, the Minimum-Cost Reliability-Constrained Sensor Node Deployment Problem (MCRC-SDP). To accomplish the aim of the dissertation, we propose a novel WSN reliability metric which adopts a more accurate SN model than the model used in the existing metrics. The proposed reliability metric is used to formulate the MCRC-SDP as a constrained combinatorial optimization problem which we prove to be NP-Complete. Two heuristic WSN deployment optimization algorithms are then developed to find high quality solutions for the MCRC-SDP. Finally, we investigate the practical realization of the techniques that we developed as solutions of the MCRC-SDP. For this purpose, we discuss why existing WSN Topology Control Protocols (TCPs) are not suitable for managing such reliable cost-optimal deployments. Accordingly, we propose a practical TCP that is suitable for managing the sleep/active cycles of the redundant SNs in such deployments. Experimental results suggest that the proposed TCP\u27s overhead and network Time To Repair (TTR) are relatively low which demonstrates the applicability of our proposed deployment solution in practice

    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

    Movement-efficient Sensor Deployment in Wireless Sensor Networks

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    We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Two key issues in MWSNs - energy consumption, which is dominated by sensor movement, and sensing coverage - have attracted plenty of attention, but the interaction of these issues is not well studied. To take both sensing coverage and movement energy consumption into consideration, we model the sensor deployment problem as a constrained source coding problem. %, which can be applied to different coverage tasks, such as area coverage, target coverage, and barrier coverage. Our goal is to find an optimal sensor deployment to maximize the sensing coverage with specific energy constraints. We derive necessary conditions to the optimal sensor deployment with (i) total energy constraint and (ii) network lifetime constraint. Using these necessary conditions, we design Lloyd-like algorithms to provide a trade-off between sensing coverage and energy consumption. Simulation results show that our algorithms outperform the existing relocation algorithms.Comment: 18 pages, 10 figure

    Launching an efficient participatory sensing campaign: A smart mobile device-based approach

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    PublishedJournal Article© 2015 ACM. Participatory sensing is a promising sensing paradigm that enables collection, processing, dissemination and analysis of the phenomena of interest by ordinary citizens through their handheld sensing devices. Participatory sensing has huge potential in many applications, such as smart transportation and air quality monitoring. However, participants may submit low-quality, misleading, inaccurate, or even malicious data if a participatory sensing campaign is not launched effectively. Therefore, it has become a significant issue to establish an efficient participatory sensing campaign for improving the data quality. This article proposes a novel five-tier framework of participatory sensing and addresses several technical challenges in this proposed framework including: (1) optimized deployment of data collection points (DC-points); and (2) efficient recruitment strategy of participants. Toward this end, the deployment of DC-points is formulated as an optimization problem with maximum utilization of sensor and then a Wise-Dynamic DC-points Deployment (WD3) algorithm is designed for high-quality sensing. Furthermore, to guarantee the reliable sensing data collection and communication, a trajectory-based strategy for participant recruitment is proposed to enable campaign organizers to identify well-suited participants for data sensing based on a joint consideration of temporal availability, trust, and energy. Extensive experiments and performance analysis of the proposed framework and associated algorithms are conducted. The results demonstrate that the proposed algorithm can achieve a good sensing coverage with a smaller number of DC-points, and the participants that are termed as social sensors are easily selected, to evaluate the feasibility and extensibility of the proposed recruitment strategies

    Mobile Search Strategies and Detection Analysis of Nuclear Radiation Sources

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    This work focuses on detection analysis and search strategies for nuclear radiation sources in metropolitan areas with mobile sensor networks. A mobile sensor detecting a stationary nuclear source experiences continually changing statistics. In this work we provide an analysis of the probability of detection of a nuclear source that incorporates these continual changes. We apply the analysis technique to several patterns of motion including linear and circular paths. Analysis is also presented for cases in which there is a significant vertical offset between source and mobile sensor (the three-dimensional problem). The resulting expressions are computationally simple to evaluate and have application to both analysis and simulation of nuclear detection systems in a variety of scenarios. In metropolitan areas, with vehicles equipped with detectors and Global Position System (GPS) devices, we consider the design of a robust detection system to provide consistent surveillance. Various strategies for providing this surveillance with a mobile sensor network are considered and the results are compared. Both time-from-last-visit based algorithms and detection algorithms that utilize both time and probability-of-miss estimates are considered. The algorithms are shown to perform well in a variety of scenarios, and it is further shown that the algorithms that utilize probability information outperform those that do not

    Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services

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    Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings
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