Paper Info
Title
Data fusion of multi-sensor for IOT precise measurement based on improved PSO algorithms
Abstract
This work proposes an improved particle swarm optimization (PSO) method to increase the measurement precision of multi-sensors data fusion in the Internet of Things (IOT) system. Critical IOT technologies consist of a wireless sensor network, RFID, various sensors and an embedded system. For multi-sensor data fusion computing systems, data aggregation is a main concern and can be formulated as a multiple dimensional based on particle swarm optimization approaches. The proposed improved PSO method can locate the minimizing solution to the objective cost function in multiple dimensional assignment themes, which are considered in particle swarm initiation, cross rules and mutation rules. The optimum seclusion can be searched for efficiently with respect to reducing the search range through validated candidate measures. Experimental results demonstrate that the proposed improved PSO method for multi-sensor data fusion is highly feasible for IOT system applications.
Year
DOI
Venue
2012
10.1016/j.camwa.2012.03.092
Computers & Mathematics with Applications
Keywords
Field
DocType
multi-sensors data fusion,proposed improved pso method,improved particle swarm optimization,improved pso algorithm,critical iot technology,data aggregation,multi-sensor data fusion,multi-sensor data fusion computing,iot precise measurement,iot system application,particle swarm initiation,embedded system,rfid,wireless sensor network,particle swarm optimization,internet of things,data fusion
Particle swarm optimization,Data mining,Internet of Things,Sensor fusion,Multi-swarm optimization,Measurement precision,Wireless sensor network,Data aggregator,Computing systems,Mathematics
Journal
Volume
Issue
ISSN
64
5
0898-1221
Citations 
PageRank 
References 
13
0.73
18
Authors
2
Name
Order
Citations
PageRank
Wen-tsai Sung116320.23
Ming-Han Tsai2364.84