A wireless LAN-based indoor positioning technology
IBM Journal of Research and Development
Signal threshold adaptation for vertical handoff in heterogeneous wireless networks
Mobile Networks and Applications
Introducing a decision tree-based indoor positioning technique
Expert Systems with Applications: An International Journal
Analysis of handoff in a location-aware vertical multi-access network
Computer Networks: The International Journal of Computer and Telecommunications Networking - Wireless IP through integration of wireless LAN and cellular networks
Clustering-based location in wireless networks
Expert Systems with Applications: An International Journal
Computer Networks: The International Journal of Computer and Telecommunications Networking
A new 4G architecture providing multimode terminals always best connected services
IEEE Wireless Communications
Mobility using IEEE 802.21 in a heterogeneous IEEE 802.16/802.11-based, IMT-advanced (4G) network
IEEE Wireless Communications
IEEE Communications Magazine
Probabilistic neural-network structure determination for pattern classification
IEEE Transactions on Neural Networks
Adaptive probabilistic neural networks for pattern classification in time-varying environment
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
Maximum likelihood training of probabilistic neural networks
IEEE Transactions on Neural Networks
Multi-agent location system in wireless networks
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
This paper proposes a novel user motion detection (UMD) model called PNN-MSMD and its application on seamless handoff. The PNN-MSMD uses a novel signal analysis model to identify the movement of a mobile device without using a location system. The PNN-MSMD integrates multiple signal processing sensors with probabilistic neural network (PNN) to analyze the received signal strength (RSS) of a mobile device. The PNN uses the variation of RSS derived from the sensors as input training data to learn the moving patterns and generate the classification rules for detecting the motion state of a mobile device. The detection results can help mobile devices to enhance handoff processes. Computer simulations show that the proposed PNN-MSMD based handoff algorithm performs better than four traditional handoff algorithms and two motion detection based handoff algorithms. The PNN-MSMD method can save up to 82.56% power consumption and reduce up to 42.51% number of handoffs.