Speed and Direction Prediction-based Localization for Mobile Wireless Sensor Networks
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Date
2012
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CERIST
Abstract
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%.
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%.
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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Keywords
Les réseaux de capteurs mobiles, Localisation, Prédiction, Ancre Mobile, Mobile Sensor Networks, Localization, Prediction, Mobile anchor