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A Precision Operation Optimization for Detection-Based Sensor Networks

delete2016-03-01
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PRE
AI
F
Frank Yeong‐Sung Lin *
C
Chiu‐Han Hsiao
Y
Yean‐Fu Wen
DOI:10.1109/AINA.2016.30delete
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摘要

摘要

En 中文
Wireless Sensor Networks (WSNs), which are connected by sensor nodes with wireless communication techniques, are used to collect, process, and transmit the various types of environmental data, such as temperature, fire and smoke detectors, CO gas, humidity, pressure, and video-based data,. The network lifetime is limited due to sensors are equipped with batteries. The measurement uncertainties and detection faults lead higher power consumption by retransmissions due to event detection error. These two factors influence with each other. In this study, there are two indicators are introduced for detection errors, false positive (FP) and false negative (FN) to evaluate a detection-based sensor network. For operation perspectives, three models are addressed to near-optimize the network lifetime and detection errors. Linear-and non-linear mathematical planning problems are formulated as (1) to minimize event FP rate subject to FN constraints; (2) to minimize the network energy consumption subject to detection accuracy requirements, turning sensor ON/OFF decisions, the required number of voting nodes, and given FP/FN constraints; and (3) to extend from the previous model as a planning problem for various types of sensor nodes deployed with diverse FP/FN characteristics and costs. An integer programming (IP) tool, LINGO, is adopted to solve the problems with computational experiments on several situations. The experimental results show that the objectives can be achieved; lots of useful hints or configurations can be applied for system implementations to improve system performance efficiently and effectively.
Keyword:
event detection
energy consumption
false positive
false negative
density deployment
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期刊

I
IEEE 17TH INT CONF ON DEPENDABLE, AUTONOM AND SECURE COMP / IEEE 17TH INT CONF ON PERVAS INTELLIGENCE AND COMP / IEEE 5TH INT CONF ON CLOUD AND BIG DATA COMP / IEEE 4TH CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/CBDCOM/CYBERSCITECH)
IF:
0
论文数:
56
被引数:
0

机构

N
National Taiwan University
学者数:
4.7W
论文数: 4.2W
被引数: 3.6W
N
National Taipei University
学者数:
845
论文数: 1.0K
被引数: 1.6K
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