Neurocomputing
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
GPS-Free Positioning in Mobile ad-hoc Networks
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 9 - Volume 9
A New In-Door Location Detection Method Adopting Learning Algorithms
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
The cricket indoor location system
The cricket indoor location system
Wireless sensor network survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
A soft computing approach to localization in wireless sensor networks
Expert Systems with Applications: An International Journal
Springer Handbook of Acoustics
Springer Handbook of Acoustics
RFID and Sensor Networks: Architectures, Protocols, Security, and Integrations
RFID and Sensor Networks: Architectures, Protocols, Security, and Integrations
Mobile element assisted cooperative localization for wireless sensor networks with obstacles
IEEE Transactions on Wireless Communications
Quantum Physics for Scientists and Technologists: Fundamental Principles and Applications for Biologists, Chemists, Computer Scientists, and Nanotechnologists
Distributed Large-Scale Dimensional Metrology: New Insights
Distributed Large-Scale Dimensional Metrology: New Insights
Localization of industrial wireless sensor networks: an artificial neural network approach
HoloMAS'11 Proceedings of the 5th international conference on Industrial applications of holonic and multi-agent systems for manufacturing
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One of the imperative problems in the realm of wireless sensor networks is the problem of wireless sensors localization. Despite the fact that much research has been conducted in this area, many of the proposed approaches produce unsatisfactory results when exposed to the harsh, uncertain, noisy conditions of a manufacturing environment. In this study, we develop an artificial neural network approach to moderate the effect of the miscellaneous noise sources and harsh factory conditions on the localization of the wireless sensors. Special attention is given to investigate the effect of blockage and ambient conditions on the accuracy of mobile node localization. A simulator, simulating the noisy and dynamic shop conditions of manufacturing environments, is employed to examine the neural network proposed. The neural network performance is also validated through some actual experiments in real-world environment prone to different sources of noise and signal attenuation. The simulation and experimental results demonstrate the effectiveness and accuracy of the proposed methodology.