International Conference Papers

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Start date: 2010Subject: search.filters.subject.Prediction

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    On the challenges of mobility prediction in smart cities
    (Copernicus Publications, 2020) Boukhedouma, H.; Meziane, Abdelkrim; Hammoudi, S.; Benna, Amel
    The mass of data generated from people’s mobility in smart cities is constantly increasing, thus making a new business for large companies. These data are often used for mobility prediction in order to improve services or even systems such as the development of location-based services, personalized recommendation systems, and mobile communication systems. In this paper, we identify the mobility prediction issues and challenges serving as guideline for researchers and developers in mobility prediction. To this end, we first identify the key concepts and classifications related to mobility prediction. We then, focus on challenges in mobility prediction from a deep literature study. These classifications and challenges are for serving further understanding, development and enhancement of the mobility prediction vision.
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    Prediction- based Localization for Mobile Wireless Sensor Networks
    (IEEE, 2014-09) 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 hneighborhood 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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    Speed and Direction Prediction-based Localization for Mobile Wireless Sensor Networks
    (CARI, 2012-10) Benkhelifa, Imane; Moussaoui, Samira
    In this paper, we propose an efficient and practical localization technique especially designed for mobile sensor networks. It uses a single mobile anchor travelling with a predefined trajectory while periodically broadcasts its current location coordinates to the nearby sensors. Using previous location information, the node predicts its speed and direction. 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 and its variant MCB.
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    Speed and Direction Prediction-based Localization for Mobile Wireless Sensor Networks
    (IEEE CONFERENCE PUBLICATIONS, 2012) Benkhelifa, Imane; Moussaoui, Samira
    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 copes with 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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    Locating Emergency Responders using Mobile Wireless Sensor Networks
    (ISCRAM, 2013-05) Benkhelifa, Imane; Moussaoui, Samira; Nouali-Taboudjemat, Nadia
    Emergency response in disaster management using wireless sensor networks has recently become an interest of many researchers in the world. This interest comes from the growing number of disasters and crisis (natural or man-made) affecting millions of lives and the easy-use of new and cheap technologies. This paper details another application of WSN in the post disaster scenario and comes up with an algorithm for localization of sensors attached to mobile responders (firefighters, policemen, first aid agents, emergency nurses, etc) while assisted by a mobile vehicle (fire truck, police car, or aerial vehicle like helicopters) called mobile anchor, sent to supervise the rescue operation. This solution is very efficient and rapidly deployable since no pre-installed infrastructure is needed. Also, there is no need to equip each sensor with a GPS receiver which is very costly and may increase the sensor volume. The proposed technique is based on the prediction of the rescuers velocities and directions considering previous position estimations. The evaluation of our solution shows that our technique takes benefit from prediction 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 with more than 50%.