Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Spatial Model Based on the Notions of Spatial Conceptual Map and of Object's Influence Areas
COSIT '99 Proceedings of the International Conference on Spatial Information Theory: Cognitive and Computational Foundations of Geographic Information Science
Efficient multi-agent path planning
Proceedings of the Eurographic workshop on Computer animation and simulation
Real-time Path Planning for Navigation in Unknown Environment
TPCG '03 Proceedings of the Theory and Practice of Computer Graphics 2003
A Mobility Prediction Architecture Based on Contextual Knowledge and Spatial Conceptual Maps
IEEE Transactions on Mobile Computing
An Improved Genetic Algorithm of Optimum Path Planning for Mobile Robots
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Packet-switched network selection with the highest QoS in 4G networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Context-awareness handoff planning in heterogeneous wireless networks
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Mobility management in ubiquitous environments
Personal and Ubiquitous Computing
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A ubiquitous network aims to provide users intelligent human-centric context-aware services at anytime anywhere. Path planning in a ubiquitous network considers users' needs and surrounding context to plan the best path which is very different from that of car navigation or mobile robot research currently available. In this paper, we propose a context-aware path planning mechanism based on spatial conceptual map (SCM) and genetic algorithm (GA), referred to as UbiPaPaGo. SCM model is adopted to represent the real map of the surrounding environment. GA is a robust heuristic algorithm that devotes to UbiPaPaGo to plan the optimal path. The goal of UbiPaPaGo is to automatically find the best-fitting path that satisfies multiple requirements of individual user. A prototype of the UbiPaPaGo has been implemented to show its feasibility. Our numerical results also indicate that the proposed UbiPaPaGo is very efficient.