Improvement of assurance including security for wireless sensor networks using dispersed data transmission

AbstractAssurance networks are one of the essential technologies of New-generation Networks. Assurance is defined as the capability of guaranteeing functional and non-functional system properties such as dependability, security, timeliness and adaptability to heterogeneous and changing requirements. Assurance is essential for sustainable networks and this research focused specifically on providing assurance for WSNs. Node capture attacks are one prospective kind of attack on WSNs. To reduce negative effect of node capture attacks, we have previously proposed secure decentralized data transfer. In this proposed method, it was assumed that multiple paths were in place. In this paper as well, we again propose using the multipath routing method. To make multiple paths fit our previously proposed method, we have modified ATR (Augmented Tree Based Routing). We have conducted simulation experiments using our proposed method in a network simulator. The results show that our previously proposed method is effective in both cases in which the network size is small or large. In addition, we conducted other simulation experiments to measure several aspects of the assurance of our method. We measured in terms of varying parameters such as node densities, distance between the source and the destination nodes, and so on. Additionally, our method is more assured than the single path-based method

Nouveau modèle pour le positionnement des senseurs avec contraintes de localisation

RÉSUMÉ Les réseaux de capteurs sans fil continuent de constituer sans doute un développement technologique majeur. Le problème de la planification s'inscrit dans un objectif global d'amélioration des performances. Le problème de planification doit permettre d’optimiser l'emplacement des capteurs relativement à des critères afin d’obtenir une certaine qualité de service par exemple en terme de couverture et de connectivité. L’objectif de ce mémoire est de proposer une stratégie de planification des réseaux de capteurs sans fil. Cette stratégie va permettre de garantir une couverture et une connectivité accrue. L’approche commence par proposer un formalisme combinatoire du problème d’optimisation avec des contraintes qui permettent d’exprimer les objectifs de localisation, de couverture et de connectivité. L'espace de recherche est discret et le choix parmi les sites potentiels est directement lié aux caractéristiques de l'environnement du déploiement. Ainsi, on suppose qu’on connait les données concernant les coordonnées des positions probables pour mobiles et des sites potentiels, la portée du signal émis, la sensibilité de réception aussi bien des mobiles que des capteurs à installer, le nombre minimal des signaux pour la localisation et le nombre maximal de sauts permis pour le routage entre capteurs. Une heuristique initiale de type glouton et une autre basée sur la recherche avec tabous permettront d’approcher la solution optimale. Cette solution va être comparée à une borne inférieure définie à partir d’une relaxation de certaines contraintes du modèle. Les simulations réalisées ont permis de démonter la validité de l’approche de planification. Cependant certaines limitations surgissent surtout pour la modélisation de la propagation radio. En effet, nous proposons une amélioration qui se traduit par l’intégration des mesures ou des estimations pour le niveau d’interférence des signaux propagés aussi bien des mobiles que des capteurs. À notre avis, cette approche va essentiellement garantir une localisation exacte des mobiles ainsi qu’une meilleure connectivité des noeuds du réseau de capteurs sans fil.----------ABSTRACT Wireless sensor networks continue to be without doubts a major research area. The objective of the wireless sensors network planning problem is to locate the sensors while respecting a set of performance constraints. In this work we consider coverage and connectivity constraints. Moreover we impose that each mobile station be located by the sensors (i.e. location constraints). The purpose of this work is to propose a planning strategy of wireless sensor networks. It will ensure full coverage and increased connectivity. The approach begins by proposing a formalism of combinatorial optimization model with coverage, location and connectivity constraints. The search space is discrete and the choice of potential sites is directly related to the environment of deployment characteristics. Thus, we assume known the coordinates of positions for mobile stations and potential sites, the characteristics of radio propagation, the receiver sensitivity for mobiles as well as for sensors to be installed, the minimum number of signals for the location, the maximum hops allowed for routing between sensors. An initial greedy heuristic is proposed as well as search metaheuristic. Solutions found are compared to a lower bound obtained by a relaxed version of the model. The simulations show the validity of the planning approach. However, some limitations arise especially for the modeling of radio propagation. Indeed, we propose an improvement which is reflected in the integration of measures or estimates for the level of interference signals. From our perspective, this approach will essentially guarantee an exact location of mobiles and a better connectivity of the nodes of wireless sensor network

On the Fundamentals of Stochastic Spatial Modeling and Analysis of Wireless Networks and its Impact to Channel Losses

With the rapid evolution of wireless networking, it becomes vital to ensure transmission reliability, enhanced connectivity, and efficient resource utilization. One possible pathway for gaining insight into these critical requirements would be to explore the spatial geometry of the network. However, tractably characterizing the actual position of nodes for large wireless networks (LWNs) is technically unfeasible. Thus, stochastical spatial modeling is commonly considered for emulating the random pattern of mobile users. As a result, the concept of random geometry is gaining attention in the field of cellular systems in order to analytically extract hidden features and properties useful for assessing the performance of networks. Meanwhile, the large-scale fading between interacting nodes is the most fundamental element in radio communications, responsible for weakening the propagation, and thus worsening the service quality. Given the importance of channel losses in general, and the inevitability of random networks in real-life situations, it was then natural to merge these two paradigms together in order to obtain an improved stochastical model for the large-scale fading. Therefore, in exact closed-form notation, we generically derived the large-scale fading distributions between a reference base-station and an arbitrary node for uni-cellular (UCN), multi-cellular (MCN), and Gaussian random network models. In fact, we for the first time provided explicit formulations that considered at once: the lattice profile, the users’ random geometry, the spatial intensity, the effect of the far-field phenomenon, the path-loss behavior, and the stochastic impact of channel scatters. Overall, the results can be useful for analyzing and designing LWNs through the evaluation of performance indicators. Moreover, we conceptualized a straightforward and flexible approach for random spatial inhomogeneity by proposing the area-specific deployment (ASD) principle, which takes into account the clustering tendency of users. In fact, the ASD method has the advantage of achieving a more realistic deployment based on limited planning inputs, while still preserving the stochastic character of users’ position. We then applied this inhomogeneous technique to different circumstances, and thus developed three spatial-level network simulator algorithms for: controlled/uncontrolled UCN, and MCN deployments