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
A New In-Door Location Detection Method Adopting Learning Algorithms
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Tracking moving devices with the cricket location system
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Novel self-configurable positioning technique for multihop wireless networks
IEEE/ACM Transactions on Networking (TON)
The cricket indoor location system
The cricket indoor location system
A soft computing approach to localization in wireless sensor networks
Expert Systems with Applications: An International Journal
RFID and Sensor Networks: Architectures, Protocols, Security, and Integrations
RFID and Sensor Networks: Architectures, Protocols, Security, and Integrations
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
An artificial neural network approach to the problem of wireless sensors network localization
Robotics and Computer-Integrated Manufacturing
I3WSN: Industrial Intelligent Wireless Sensor Networks for indoor environments
Computers in Industry
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In this study, we evaluate the performance of three types of techniques, namely neural based, Kalman filter based and trilateration based techniques, having been proposed to tackle the problem of real-time mobile sensor node tracking in a wireless sensor network with passive architecture. To investigate the performance of the aforementioned techniques under real-world circumstances, a small-scale wireless sensor network is deployed in an environment prone to multiple noise sources, multi-path and signal attenuation phenomena. The network makes use of a 433MHz MICA2 based Cricket platform, which is comprised of 6 Cricket motes, at least one of which is mobile. The network utilizes a passive architecture in which any mobile mote receives the Beacon signals to localize itself. Subsequently, a neural based approach is compared with a trilateration and a Kalman filter based technique. The results obtained corroborate the efficiency and advanced performance of the neural based approach.