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Self-organizing Mobile Sensor Network: Distributed Topology Control Framework

Self-organizing Mobile Sensor Network: Distributed Topology Control Framework

Geunho Lee, Nak Young Chong
Copyright: © 2011 |Pages: 26
ISBN13: 9781616928575|ISBN10: 1616928573|EISBN13: 9781616928582
DOI: 10.4018/978-1-61692-857-5.ch026
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MLA

Lee, Geunho, and Nak Young Chong. "Self-organizing Mobile Sensor Network: Distributed Topology Control Framework." Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives, edited by Nak-Young Chong and Fulvio Mastrogiovanni, IGI Global, 2011, pp. 558-583. https://doi.org/10.4018/978-1-61692-857-5.ch026

APA

Lee, G. & Chong, N. Y. (2011). Self-organizing Mobile Sensor Network: Distributed Topology Control Framework. In N. Chong & F. Mastrogiovanni (Eds.), Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives (pp. 558-583). IGI Global. https://doi.org/10.4018/978-1-61692-857-5.ch026

Chicago

Lee, Geunho, and Nak Young Chong. "Self-organizing Mobile Sensor Network: Distributed Topology Control Framework." In Handbook of Research on Ambient Intelligence and Smart Environments: Trends and Perspectives, edited by Nak-Young Chong and Fulvio Mastrogiovanni, 558-583. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61692-857-5.ch026

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Abstract

Ambient Intelligence (AmI) is a multidisciplinary approach aimed at enriching physical environments with a network of distributed devices, such as sensors, actuators, and computational resources, in order to support humans in achieving their everyday task. Within the framework of AmI, this chapter presents decentralized coordination for a swarm of autonomous robotic sensors building intelligent environments adopting AmI. The large-scale robotic sensors are regarded as a swarm of wireless sensors mounted on spatially distributed autonomous mobile robots. Therefore, motivated by the experience gained during the development and usage for decentralized coordination of mobile robots in geographically constrained environments, our work introduces the following two detailed functions: self-configuration and flocking. In particular, this chapter addresses the study of a unified framework which governs the adaptively self-organizing processes for a swarm of autonomous robots in the presence of an environmental uncertainty. Based on the hypothesis that the motion planning for robot swarms must be controlled within the framework, the two functions are integrated in a distributed way, and each robot can form an equilateral triangle mesh with its two neighbors in a geometric sense. Extensive simulations are performed in two-dimensional unknown environments to verify that the proposed method yields a computationally efficient, yet robust deployment.

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