Gradient-Driven Target Acquisition in Mobile Wireless Sensor Networks
Q. Zhang, G. Sobelman, and T. He. Mobile Ad-hoc and Sensor Networks, volume 4325 of Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 10.1007/11943952_31.(2006)
Abstract
Navigation of mobile wireless sensor networks and fast target acquisition without a map are two challenging problems in search and rescue applications. In this paper, we propose and evaluate a novel Gradient Driven method, called GraDrive. Our approach integrates per-node prediction with global collaborative prediction to estimate the position of a stationary target and to direct mobile nodes towards the target along the shortest path. We demonstrate that a high accuracy in localization can be achieved much faster than other random work models without any assistance from stationary sensor networks. We evaluate our model through a light-intensity matching experiment in MicaZ motes, which indicates that our model works well in a wireless sensor network environment. Through simulation, we demonstrate almost a 40% reduction in the target acquisition time, compared to a random walk model, while obtaining less than 2 unit error in target position estimation. Keywords: Wireless Sensor Network, Navigation, Localization, Probabilistic Model, Rescue.
%0 Book Section
%1 springerlink:10.1007/11943952_31
%A Zhang, Qingquan
%A Sobelman, Gerald
%A He, Tian
%B Mobile Ad-hoc and Sensor Networks
%D 2006
%E Cao, Jiannong
%E Stojmenovic, Ivan
%E Jia, Xiaohua
%E Das, Sajal
%I Springer Berlin / Heidelberg
%K wlanpos
%P 365-376
%T Gradient-Driven Target Acquisition in Mobile Wireless Sensor Networks
%U http://dx.doi.org/10.1007/11943952_31
%V 4325
%X Navigation of mobile wireless sensor networks and fast target acquisition without a map are two challenging problems in search and rescue applications. In this paper, we propose and evaluate a novel Gradient Driven method, called GraDrive. Our approach integrates per-node prediction with global collaborative prediction to estimate the position of a stationary target and to direct mobile nodes towards the target along the shortest path. We demonstrate that a high accuracy in localization can be achieved much faster than other random work models without any assistance from stationary sensor networks. We evaluate our model through a light-intensity matching experiment in MicaZ motes, which indicates that our model works well in a wireless sensor network environment. Through simulation, we demonstrate almost a 40% reduction in the target acquisition time, compared to a random walk model, while obtaining less than 2 unit error in target position estimation. Keywords: Wireless Sensor Network, Navigation, Localization, Probabilistic Model, Rescue.
@incollection{springerlink:10.1007/11943952_31,
abstract = {Navigation of mobile wireless sensor networks and fast target acquisition without a map are two challenging problems in search and rescue applications. In this paper, we propose and evaluate a novel Gradient Driven method, called GraDrive. Our approach integrates per-node prediction with global collaborative prediction to estimate the position of a stationary target and to direct mobile nodes towards the target along the shortest path. We demonstrate that a high accuracy in localization can be achieved much faster than other random work models without any assistance from stationary sensor networks. We evaluate our model through a light-intensity matching experiment in MicaZ motes, which indicates that our model works well in a wireless sensor network environment. Through simulation, we demonstrate almost a 40% reduction in the target acquisition time, compared to a random walk model, while obtaining less than 2 unit error in target position estimation. Keywords: Wireless Sensor Network, Navigation, Localization, Probabilistic Model, Rescue.},
added-at = {2010-10-13T11:05:18.000+0200},
affiliation = {Department of Electrical and Computer Engineering, University of Minnesota, Twin cities USA USA},
author = {Zhang, Qingquan and Sobelman, Gerald and He, Tian},
biburl = {https://www.bibsonomy.org/bibtex/2168e13c5467090e17068af743ae90583/kw},
booktitle = {Mobile Ad-hoc and Sensor Networks},
editor = {Cao, Jiannong and Stojmenovic, Ivan and Jia, Xiaohua and Das, Sajal},
interhash = {10ad583b7bcebb998d0a85d0b82ce8de},
intrahash = {168e13c5467090e17068af743ae90583},
keywords = {wlanpos},
note = {10.1007/11943952_31},
pages = {365-376},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
timestamp = {2010-10-13T11:05:18.000+0200},
title = {Gradient-Driven Target Acquisition in Mobile Wireless Sensor Networks},
url = {http://dx.doi.org/10.1007/11943952_31},
volume = 4325,
year = 2006
}