The anatomy of a context-aware application
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
LANDMARC: Indoor Location Sensing Using Active RFID
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
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
Comparisons of Three Kalman Filter Tracking Algorithms in Sensor Network
IWNAS '06 Proceedings of the 2006 International Workshop on Networking, Architecture, and Storages
High-Performance Wide-Area Optical Tracking: The HiBall Tracking System
Presence: Teleoperators and Virtual Environments
Comparison of MLP neural network and Kalman filter for localization in wireless sensor networks
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
Indoor Localization Using Neural Networks with Location Fingerprints
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Localization using radial basis function networks and signal trength fingerprints in WLAN
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Bluetooth indoor localization with multiple neural networks
ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
Journal of Network and Computer Applications
Adaptive clustering for device free user positioning utilizing passive RFID
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Noisy distance measurements are a pervasive problem in localization in wireless sensor networks. Neural networks are not commonly used in localization, however, our experiments in this paper indicate neural networks are a viable option for solving localization problems. In this paper we qualitatively compare the performance of three different families of neural networks: Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Recurrent Neural Networks (RNN). The performance of these networks will also be compared with two variants of the Kalman Filter which are traditionally used for localization. The resource requirements in term of computational and memory resources will also be compared. In this paper, we show that the RBF neural network has the best accuracy in localizing, however it also has the worst computational and memory resource requirements. The MLP neural network, on the other hand, has the best computational and memory resource requirements.