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

Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

PublishedJournal Article© 2014 IEEE. Coverage of interest points and network connectivity are two main challenging and practically important issues of Wireless Sensor Networks (WSNs). Although many studies have exploited the mobility of sensors to improve the quality of coverage andconnectivity, little attention has been paid to the minimization of sensors' movement, which often consumes the majority of the limited energy of sensors and thus shortens the network lifetime significantly. To fill in this gap, this paper addresses the challenges of the Mobile Sensor Deployment (MSD) problem and investigates how to deploy mobile sensors with minimum movement to form a WSN that provides both target coverage and network connectivity. To this end, the MSD problem is decomposed into two sub-problems: the Target COVerage (TCOV) problem and the Network CONnectivity (NCON) problem. We then solve TCOV and NCON one by one and combine their solutions to address the MSD problem. The NP-hardness of TCOV is proved. For a special case of TCOV where targets disperse from each other farther than double of the coverage radius, an exact algorithm based on the Hungarian method is proposed to find the optimal solution. For general cases of TCOV, two heuristic algorithms, i.e., the Basic algorithm based on clique partition and the TV-Greedy algorithm based on Voronoi partition of the deployment region, are proposed to reduce the total movement distance ofsensors. For NCON, an efficient solution based on the Steiner minimum tree with constrained edge length is proposed. Thecombination of the solutions to TCOV and NCON, as demonstrated by extensive simulation experiments, offers a promising solutionto the original MSD problem that balances the load of different sensors and prolongs the network lifetime consequently.This work is supported in part by the National Science Foundation of China (Grant Nos. 61232001, 61103203, 61173169, and 61173051), the Major Science & Technology Research Program for Strategic Emerging Industry of Hunan (Grant No. 2012GK4054), and the Scientific Research Fund of Hunan Provincial Education Department (Grant No. 14C0030)

Energy efficient scheduling and allocation of tasks in sensor cloud

Wireless Sensor Network (WSN) is a class of ad hoc networks that has capability of self-organizing, in-network data processing, and unattended environment monitoring. Sensor-cloud is a cloud of heterogeneous WSNs. It is attractive as it can change the computation paradigm of wireless sensor networks. In Sensor-Cloud, to gain profit from underutilized WSNs, multiple WSN owners collaborate to provide a cloud service. Sensor Cloud users can simply rent the sensing services which eliminates the cost of ownership, enabling the usage of large scale sensor networks become affordable. The nature of Sensor-Cloud enables resource sharing and allows virtual sensors to be scaled up or down. It abstracts different platforms hence giving the impression of a homogeneous network. Further in multi-application environment, users of different applications may require data based on different needs. Hence scheduling scheme in WSNs is required which serves maximum users of various applications. We have proposed a scheduling scheme suitable for the multiple applications in Sensor Cloud. Scheduling scheme is based on TDMA which considers fine granularity of tasks. The performance evaluation shows the better response time, throughput and overall energy consumption as compared to the base case we developed. On the other hand, to minimize the energy consumption in WSN, we design an allocation scheme. In Sensor Cloud, we consider sparsely and densely deployed WSNs working together. Also, in a WSN there might be sparsely and densely deployed zones. Based on spatial correlation and with the help of Voronoi diagram, we turn on minimum number of sensors hence increasing WSN lifetime and covering almost 100 percent area. The performance evaluation of allocation scheme shows energy efficiency by selecting fewer nodes in comparison to other work --Abstract, page iv

Safe, Remote-Access Swarm Robotics Research on the Robotarium

This paper describes the development of the Robotarium -- a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-agent research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium as well as connects these to the particular considerations one must take when making complex hardware remotely accessible. In particular, safety must be built in already at the design phase without overly constraining which coordinated control programs the users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees.Comment: 13 pages, 7 figures, 3 code samples, 72 reference

Reliable cost-optimal deployment of wireless sensor networks

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

Distributed, adaptive deployment for nonholonomic mobile sensor networks : theory and experiments

In this work we show the Lyapunov stability and convergence of an adaptive and decentralized coverage control for a team of mobile sensors. This new approach assumes nonholonomic sensors rather than the usual holonomic sensors found in the literature. The kinematics of the unicycle model and a nonlinear control law in polar coordinates are used in order to prove the stability of the controller applied over a team of mobile sensors. This controller is adaptive, which means that the mobile sensors are able to estimate and map a density function in the sampling space without a previous knowledge of the environment. The controller is decentralized, which means that each mobile sensor has its own estimate and computes its own control input based on local information. In order to guarantee the estimate convergence, the mobile sensors implement a consensus protocol in continuous time assuming a fixed network topology and zero communication delays. The convergence and feasibility of the coverage control algorithm are verified through simulations in Matlab and Stage. The Matlab simulations consider only the kinematics of the mobile sensors and the Stage simulations consider the dynamics and the kinematics of the sensors. The Matlab simulations show successful results since the sensor network carries out the coverage task and distributes itself over the estimated density function. The adaptive law which is defined by a differential equation must be approximated by a difference equation to be implementable in Stage. The Stage simulations show positive results, however, the system is not able to achieve an accurate estimation of the density function. In spite of that, the sensors carry out the coverage task distributing themselves over the sampling space. Furthermore, some experiments are carried out using a team of four Pioneer 3-AT robots sensing a piecewise constant light distribution function. The experimental results are satisfactory since the robots carry out the coverage task. However, the accuracy of the estimation is affected by the approximation of the adaptation law by difference equations, the number of robots and sensor sensitivity. Based on the results of this research, the decentralized adaptive coverage control for nonholonomic vehicles has been analyzed from a theoretical approach and validated through simulation and experimentation with positive results. As a future work we will investigate: (i) new techniques to improve the implementation of the adaptive law in real time,(ii) the consideration of the dynamics of the mobile sensors, and (iii) the stability and convergence of the adaptive law for continuous-time variant density function