Employed BPN to Multi-sensors Data Fusion for Environment Monitoring Services
ATC '09 Proceedings of the 6th International Conference on Autonomic and Trusted Computing
Computers & Mathematics with Applications
A novel parallel hybrid intelligence optimization algorithm for a function approximation problem
Computers & Mathematics with Applications
PSO-SFDD: Defense against SYN flooding DoS attacks by employing PSO algorithm
Computers & Mathematics with Applications
Channel aware decision fusion in wireless sensor networks
IEEE Transactions on Signal Processing
The design space of wireless sensor networks
IEEE Wireless Communications
Localization systems for wireless sensor networks
IEEE Wireless Communications
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Optimal Power Scheduling for Correlated Data Fusion in Wireless Sensor Networks via Constrained PSO
IEEE Transactions on Wireless Communications
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
IEEE Communications Magazine
Shadow detecting using particle swarm optimization and the Kolmogorov test
Computers & Mathematics with Applications
Energy-efficient detection in sensor networks
IEEE Journal on Selected Areas in Communications
Cryptanalysis of the Cho et al. protocol: A hash-based RFID tag mutual authentication protocol
Journal of Computational and Applied Mathematics
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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.