A Traffic Reduction Method for Centralized RSSI-Based Location Estimation in Wireless Sensor Networks
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概要
- 論文の詳細を見る
In a centralized localization scenario, the limited throughput of the central node constrains the possible number of target node locations that can be estimated simultaneously. To overcome this limitation, we propose a method which effectively decreases the traffic load associated with target node localization, and therefore increases the possible number of target node locations that can estimated simultaneously in a localization system based on received signal strength indicator (RSSI) and maximum likelihood estimation. Our proposed method utilizes a threshold which limits the amount of forwarded RSSI data to the central node. As the threshold is crucial to the method, we further propose a method to theoretically determine its value. We experimentally verified the proposed method in various environments and the experimental results revealed that the method can reduce the load by 32-64% without significantly affecting the estimation accuracy.
- (社)電子情報通信学会の論文
- 2008-06-01
著者
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Hara Shinsuke
Osaka City Univ. Osaka Jpn
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Hara Shinsuke
Graduate School Of Engineering Osaka University
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Hara Shinsuke
Graduate School Of Engineering Osaka City University
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Hara Shinsuke
Osaka City Univ. Osaka‐shi Jpn
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ZEMEK Radim
Graduate School of Engineering, Osaka University
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YANAGIHARA Kentaro
Corporate R&D Center, Oki Electric Industry Co., Ltd.
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KITAYAMA Ken-ichi
Graduate School of Engineering, Osaka University
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Zemek Radim
Graduate School Of Engineering Osaka University
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Kitayama K
Department Of Electronic And Information Systems Osaka University
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Kitayama Ken-ichi
Graduate School Of Engineering Osaka University
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Kitayama Kenichi
Dept. Of Electronics And Information Systems Osaka University
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Yanagihara Kentaro
Corporate R&d Center Oki Electric Industry Co. Ltd.
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Hara Shinsuke
Osaka City Univ.
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Yanagihara Kentaro
Corporate R&d Center Oki Electric Ind. Co. Ltd.
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Kitayama Ken-ichi
Department Of Electronic And Information Systems Osaka University
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