| Project/Area Number |
17500043
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Computer system/Network
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
SUGANO Masashi Osaka Prefecture University, School of Comprehensive Rehabilitation, Associate Professor, 総合リハビリテーション学部, 助教授 (80290386)
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| Project Period (FY) |
2005 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,000,000 (Direct Cost: ¥3,000,000)
Fiscal Year 2006: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2005: ¥1,900,000 (Direct Cost: ¥1,900,000)
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| Keywords | sensor network / simulation / localization / cluster / energy consumption / swarm intelligence / スループット |
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| Research Abstract |
In large-scale sensor networks, multi-hop communication between sensor nodes is necessary to cover a large monitoring region. Moreover, sensor nodes should be grouped into clusters to enhance scalability and robustness. We examine the characteristics of multi-hop communication between clusters in large-scale sensor networks and compare them with other routing methods. We also apply CSMA/CA since the information of only neighboring sensor nodes is necessary. As a result, using CSMA/CA, power consumption increases by 12% and the packet collection time becomes about four times longer in comparison to using TDMA based on location information of all sensor nodes.
Furthermore, to verify the validity of our autonomous indoor localization system for sensor networks in an actual environment, we implemented it in a wireless sensor network based on the ZigBee standard. The system automatically estimates the distance between sensor nodes by measuring the RSSI (received signal strength indicator) at an appropriate number of sensor nodes. Through experiments, we clarified the validity of our data collection and position estimation techniques. The results show that the position estimation error was reduced to 1.5-2 m.
Moreover, we propose a new scheme of gathering data from sensor networks with multi-sink configurations inspired by the swarm intelligence. Each sensor node determines its next action through repeated interaction and feedback from its neighbors and environments in this scheme. Advantageous clustering and routing emerge in network level from these actions. Our simulation results revealed that the proposed scheme can reliably deliver event information to the sink nodes, is robust over very-poor-quality wireless channels, and has self-recovery capability to deal with sensor-node failure and even that in sink nodes.
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