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Sensor Selection Without Inter-Sensor Communication Based on Observed Sensor Data.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Sensor Selection Without Inter-Sensor Communication Based on Observed Sensor Data./
作者:
Fang, Xubin.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:
36 p.
附註:
Source: Masters Abstracts International, Volume: 84-11.
Contained By:
Masters Abstracts International84-11.
標題:
Electrical engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30487061
ISBN:
9798379559069
Sensor Selection Without Inter-Sensor Communication Based on Observed Sensor Data.
Fang, Xubin.
Sensor Selection Without Inter-Sensor Communication Based on Observed Sensor Data.
- Ann Arbor : ProQuest Dissertations & Theses, 2023 - 36 p.
Source: Masters Abstracts International, Volume: 84-11.
Thesis (M.S.)--Lehigh University, 2023.
This item must not be sold to any third party vendors.
Reducing the number of transmissions in a wireless sensor network (WSN) to improve its energy efficiency has always been of great interest. We consider a new approach where only a fixed number of sensors will send data to the fusion center. We provide proofs and numerical results on the optimality of this approach in the case of selecting one out of two sensors. We also demonstrate numerically, the significant gains of this approach in the case of selecting 1 out of N sensors when compared to random selection, in terms of the perspective of the accuracy of decision making. We also show the performance of an approach for selecting m out of N sensors.
ISBN: 9798379559069Subjects--Topical Terms:
596380
Electrical engineering.
Subjects--Index Terms:
Energy efficiency
Sensor Selection Without Inter-Sensor Communication Based on Observed Sensor Data.
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Reducing the number of transmissions in a wireless sensor network (WSN) to improve its energy efficiency has always been of great interest. We consider a new approach where only a fixed number of sensors will send data to the fusion center. We provide proofs and numerical results on the optimality of this approach in the case of selecting one out of two sensors. We also demonstrate numerically, the significant gains of this approach in the case of selecting 1 out of N sensors when compared to random selection, in terms of the perspective of the accuracy of decision making. We also show the performance of an approach for selecting m out of N sensors.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30487061
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