Tree-based Tracking Method to Predict Mobile Sink’s Moving Paths in Wireless Sensor Networks
碩士 === 國立臺北科技大學 === 電子電腦與通訊產業研發碩士專班 === 95 === Wireless sensor network, WSN, consists of many small volume, low cost and low energy sensing devices. The network system is established by deploying a great deal of deployed sensor nodes. The node contains sensor, wireless communication and simple micro...
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ndltd-TW-095TIT056500222019-06-27T05:10:14Z http://ndltd.ncl.edu.tw/handle/futrbu Tree-based Tracking Method to Predict Mobile Sink’s Moving Paths in Wireless Sensor Networks 樹狀式追蹤法於無線感測網路中預測行動收集點之移動路徑 Chi-Yi Ding 丁之[ㄨ虎] 碩士 國立臺北科技大學 電子電腦與通訊產業研發碩士專班 95 Wireless sensor network, WSN, consists of many small volume, low cost and low energy sensing devices. The network system is established by deploying a great deal of deployed sensor nodes. The node contains sensor, wireless communication and simple microprocessor. Hence, sensor nodes can be numerously deployed in the desired monitoring environment, and the sensor networks can be constructed quickly and easily. Energy management is a great research issue for sensor networks. However making nodes into sleeping mode can save the power consumption, and the lifetime of the sensor network could be prolonged. For saving node’s energy, we propose a moving path tree (MPT) to record all moving paths of the mobile sink. By analyzing MPT, the mobile sink can predict the most possible zone where it may move into at the next step. Therefore, only the nodes in the zone require to be waked up, and others still stay in sleep mode for saving energy. Besides predicting the moving paths, analyzing MPT could judge whether a sensor node will be invalid or not. If the nodes are highly waked to predict the mobile sink’s moving path, they may be failed soon. The experimental results reveal that if the sink lacks of moving paths recorded to be compared, then create a zone of 180° to predict the next moving path, the accuracy of prediction may reach up to 80%. When the MPT have recorded more moving paths gradually, the prediction range can be shinked from 180° to 90°, and its accuracy of prediction may also reach above 80%. Our MPT prediction may outperform the traditional omni-directional in the accuracy and the degree of saving energy. Moreover, the analysis of the MPT can detect the residue power energy of nodes and then start the backup paths to keep the network workable when some nodes may be invalid and lead to a hole soon. Chiu-Ching Tuan 段裘慶 2007 學位論文 ; thesis 93 zh-TW |
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碩士 === 國立臺北科技大學 === 電子電腦與通訊產業研發碩士專班 === 95 === Wireless sensor network, WSN, consists of many small volume, low cost and low energy sensing devices. The network system is established by deploying a great deal of deployed sensor nodes. The node contains sensor, wireless communication and simple microprocessor. Hence, sensor nodes can be numerously deployed in the desired monitoring environment, and the sensor networks can be constructed quickly and easily.
Energy management is a great research issue for sensor networks. However making nodes into sleeping mode can save the power consumption, and the lifetime of the sensor network could be prolonged. For saving node’s energy, we propose a moving path tree (MPT) to record all moving paths of the mobile sink. By analyzing MPT, the mobile sink can predict the most possible zone where it may move into at the next step. Therefore, only the nodes in the zone require to be waked up, and others still stay in sleep mode for saving energy.
Besides predicting the moving paths, analyzing MPT could judge whether a sensor node will be invalid or not. If the nodes are highly waked to predict the mobile sink’s moving path, they may be failed soon.
The experimental results reveal that if the sink lacks of moving paths recorded to be compared, then create a zone of 180° to predict the next moving path, the accuracy of prediction may reach up to 80%. When the MPT have recorded more moving paths gradually, the prediction range can be shinked from 180° to 90°, and its accuracy of prediction may also reach above 80%. Our MPT prediction may outperform the traditional omni-directional in the accuracy and the degree of saving energy.
Moreover, the analysis of the MPT can detect the residue power energy of nodes and then start the backup paths to keep the network workable when some nodes may be invalid and lead to a hole soon.
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author2 |
Chiu-Ching Tuan |
author_facet |
Chiu-Ching Tuan Chi-Yi Ding 丁之[ㄨ虎] |
author |
Chi-Yi Ding 丁之[ㄨ虎] |
spellingShingle |
Chi-Yi Ding 丁之[ㄨ虎] Tree-based Tracking Method to Predict Mobile Sink’s Moving Paths in Wireless Sensor Networks |
author_sort |
Chi-Yi Ding |
title |
Tree-based Tracking Method to Predict Mobile Sink’s Moving Paths in Wireless Sensor Networks |
title_short |
Tree-based Tracking Method to Predict Mobile Sink’s Moving Paths in Wireless Sensor Networks |
title_full |
Tree-based Tracking Method to Predict Mobile Sink’s Moving Paths in Wireless Sensor Networks |
title_fullStr |
Tree-based Tracking Method to Predict Mobile Sink’s Moving Paths in Wireless Sensor Networks |
title_full_unstemmed |
Tree-based Tracking Method to Predict Mobile Sink’s Moving Paths in Wireless Sensor Networks |
title_sort |
tree-based tracking method to predict mobile sink’s moving paths in wireless sensor networks |
publishDate |
2007 |
url |
http://ndltd.ncl.edu.tw/handle/futrbu |
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