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Efficient Obstacle Avoidance for Sensory Data Propagation in Wireless Systems

Efficient Obstacle Avoidance for Sensory Data Propagation in Wireless Systems

Pierre Leone, Luminita Moraru, Sotiris Nikoletseas, Jose D. P. Rolim
Copyright: © 2010 |Pages: 33
ISBN13: 9781615207718|ISBN10: 1615207716|ISBN13 Softcover: 9781616922863|EISBN13: 9781615207725
DOI: 10.4018/978-1-61520-771-8.ch011
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MLA

Leone, Pierre, et al. "Efficient Obstacle Avoidance for Sensory Data Propagation in Wireless Systems." Wireless Network Traffic and Quality of Service Support: Trends and Standards, edited by Thomas D. Lagkas, et al., IGI Global, 2010, pp. 271-303. https://doi.org/10.4018/978-1-61520-771-8.ch011

APA

Leone, P., Moraru, L., Nikoletseas, S., & Rolim, J. D. (2010). Efficient Obstacle Avoidance for Sensory Data Propagation in Wireless Systems. In T. Lagkas, P. Angelidis, & L. Georgiadis (Eds.), Wireless Network Traffic and Quality of Service Support: Trends and Standards (pp. 271-303). IGI Global. https://doi.org/10.4018/978-1-61520-771-8.ch011

Chicago

Leone, Pierre, et al. "Efficient Obstacle Avoidance for Sensory Data Propagation in Wireless Systems." In Wireless Network Traffic and Quality of Service Support: Trends and Standards, edited by Thomas D. Lagkas, Pantelis Angelidis, and Loukas Georgiadis, 271-303. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-771-8.ch011

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Abstract

The authors study the problem of fast and robust data propagation in wireless sensor networks in the presence of obstacles obstructing communication. They survey representative state of the art techniques, such as variations of geographic routing, which is known to scale well, mainly due to its greedy nature and low memory requirements. Still, most of these algorithms are concerned with finding some path, while the optimality of the path is difficult to achieve. Towards improving QoS, and especially latency for time-critical applications, in this chapter, the authors are presenting a novel geographic routing algorithm with obstacle avoidance properties. It aims at finding the optimal path from a source to a destination when some areas of the network are unavailable for routing due to low local density or obstacle presence. It locally and gradually with time (but, as we show, quite fast) evaluates and updates the suitability of the previously used paths and ignores non optimal paths for further routing. The performance comparison to existing state of the art protocols shows that this approach performs much better in terms of path length, thus minimizing latency and space, while introducing low overhead and being energy efficient.

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