Sensing clouds: A distributed cooperative target tracking with tiny binary noisy sensors |
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| Authors: | Tal Marian Osnat Mokryn Yuval Shavitt |
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| Affiliation: | 1. School of Electrical Engineering, Tel-Aviv University, Israel;2. School of Computer Science, Tel-Aviv-Yaffo College, Israel;1. National University of Rwanda, Huye, Rwanda;2. SRM University, Chennai, India;1. Department of Electrical, Electronics and Computer Science Engineering, Faculty of Engineering, University of Catania, 6 A. Doria Street, 95125 Catania, Italy;2. Computer Laboratory, Department of Computer Science, University of Cambridge (UK), 15 JJ Thomson Avenue, Cambridge CB3 0FD, United Kingdom;1. Univ. Politècnica de Catalunya, Dept. d''Arquitectura de Computadors, c/Jordi Girona, 1-3, Barcelona, Spain;2. Univ. Politècnica de València, Communications Dep., València, Spain;1. Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong;2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong;1. Department of Electrical Engineering, University of Rome “Tor Vergata”, Rome, Italy;2. Department of Computer Science, University of Rome “La Sapienza”, Rome, Italy |
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| Abstract: | This paper suggests a novel algorithm for mobile object tracking using wireless sensor networks (WSNs). The paper assumes a future model of WSNs, where a large number of low to medium range inexpensive and noisy sensors are distributed randomly over an area. The distributed algorithm is based on short range communication between neighboring sensors, and is designed to work with very basic low cost binary sensors, that can report only a sensing, not sensing value.Neighboring sensors that sense the object form a cloud around the object which is dynamically updated as the object moves. To save energy on reporting a subset of the cloud, the cloud core, is elected. A trade-off between the accuracy and the core size (namely transmission power) is presented, as well as an extensive simulation study. Our algorithm works well with false negative sensing and up to 10% false positive sensing. |
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| Keywords: | Sensor networks Distributed algorithms Target tracking Tiny binary sensors |
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