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Permanent URI for this collectionhttp://dl.cerist.dz/handle/CERIST/34

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Subject: search.filters.subject.Localization

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    Prediction-based Localization for Mobile Wireless Sensor Networks
    (CERIST, 2014-06-05) Benkhelifa, Imane; Lamini, Chakib; Azouz, Hichem; Moussaoui, Samira
    In this paper, we propose two extensions of SDPL (Speed and Direction Prediction-based Localization) method. The first is called MA-SDPL (Multiple Anchors SDPL) which uses multiple mobile anchors instead of only one. Each anchor has its own trajectory and its own departure point. The goal is to ensure a total coverage of the sensor field and to multiply the chance of receiving anchor beacons. Anchor Beacons help localizing mobile sensors. As a consequence, the location estimation will be enhanced. The second method deals with one mobile anchor but with the ability of multi-hoping, that is, when a sensor estimates that it is well enough localized, it sends beacons to its h-neighborhood about its current location, hence, it plays the role of an additional anchor. This solution reduces the cost comparing to when using multiple anchors and allows rapid location propagation. Simulation results show that the two extensions improve better the ratio of localized sensors and reduce the location error compared to the basic SDPL. We have also tested them in a noisy environment to be closer to a real deployment.
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    Point In half symmetric LEns : A new range-free localization protocol for wireless sensor networks
    (CERIST, 2011-02) Lasla, Noureddine; Derhab, Abdelouahid; Ouadjaout, Abdelraouf; Bagaa, Miloud; Badache, Nadjib
    As location information is used by many sensor network applications, localization is considered a keystone in their design. Existing localization protocols can be classi ed as range-based or range-free approaches. Range- based approaches are costly as they require embedding each sensor node with an additional hardware to estimate inter-node distances. In contrast, the range-free approaches are cheaper, and they estimate node position by collecting information from some special nodes with known location called anchors. Thus, compared with range- based approaches, the range-free ones are more suitable for WSNs. In this paper, we propose PIV (Point In half Vesica-piscis), a new distributed range-free localization protocol for wireless sensor networks. PIV is designed based on the geometric concept of Vesica-piscis, which helps to relax some unrealistic assumptions and incur the lower cost. Complexity analysis and simulations results show that PIV has the lowest message cost among the existing localization schemes and o ers the best trade-o between location accuracy and ratio of localized nodes.
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    Speed and Direction Prediction-based Localization for Mobile Wireless Sensor Networks
    (CERIST, 2012) Benkhelifa, Imane; Moussaoui, Samira
    Plusieurs techniques de localisation ont été proposées pour les réseaux de capteurs sans fils. Cependant, peu considèrent la mobilité des capteurs. Dans cet article, nous proposons une technique de localisation efficace et pratique spécialement conçue pour les réseaux de capteurs mobiles, nommée Speed and Direction Prediction-based Localization. Cette méthode utilise une seule ancre mobile traversant la zone de déploiement avec une trajectoire prédéfinie tout en diffusant sa position à ses capteurs voisins. Nous nous intéressons principalement à l’amélioration de la précision et l’efficacité du positionnement avec une meilleure utilisation des informations collectées par un capteur. En utilisant les informations de positionnement précédentes, le nœud prédit sa vitesse et sa direction de déplacement afin de rapprocher sa position estimée vers sa position réelle. L’évaluation de notre solution montre que cette technique de prédiction bénéficie à la fois, de la prédiction et la trajectoire de l’ancre mobile, d’une manière plus efficace que des solutions précédentes. Les résultats de simulation montrent que notre algorithme dépasse la méthode de Monté Carlo conventionnel et sa variante MCB en diminuant l’erreur de positionnement par plus de 56%. Abstract : Many low-cost localization techniques have been proposed for wireless sensor networks. However, few consider the mobility of networked sensors. In this paper, we propose an effective and practical localization technique especially designed for mobile sensor networks called Speed and direction Prediction-based Localization. It uses a single mobile anchor travelling with a predefined trajectory while periodically broadcasts its current location coordinates to the nearby sensors. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers. Using previous location information, the node predicts its speed and direction to move the estimated position closer to its real position. The evaluation of our solution shows that our technique takes benefit from both, prediction and anchor trajectory, in a more effective manner than previous solutions. The simulation results show that our algorithm outperforms conventional Monte Carlo localization schemes by decreasing estimation errors by up to 56%.
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    Geographic Routing Protocols in Wireless Sensor Networks
    (CERIST, 2013) Benkhelifa, Imane; Nouali-Taboudjemat, Nadia
    The number of applications that can benefit from efficient geographic routing is impressive. As a consequence, numerous routing protocols have been developed to better accomplish the routing process according to the application requirements. In this paper, we surveyed about twenty geographic protocols. To better match them with applications, we classified them into four categories: (i) QoS-based protocols which are mainly real-time protocols used to transmit urgent message, (ii) Multipath-based that are protocols allowing transmission of packets over multi paths alternatively or concurrently, (iii) Protocols supporting mobility especially those concerned by routing information towards mobile sinks, we finally presented protocols that consider (iv) localization errors in routing decisions since sensors cannot always get accurate positions.