Fuzzy logic and neurofuzzy applications explained
Fuzzy logic and neurofuzzy applications explained
Mobile computing middleware for context-aware applications
Proceedings of the 24th International Conference on Software Engineering
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
An adaptive middleware infrastructure for mobile computing
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Service Adaptation Using Fuzzy Theory in Context-Aware Mobile Computing Middleware
RTCSA '05 Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
An Adaptive Middleware Infrastructure Incorporating Fuzzy Logic for Mobile Computing
NWESP '05 Proceedings of the International Conference on Next Generation Web Services Practices
CARISMA: Context-Aware Reflective mIddleware System for Mobile Applications
IEEE Transactions on Software Engineering
A control-based middleware framework for quality-of-service adaptations
IEEE Journal on Selected Areas in Communications
A fuzzy service adaptation based on QoS satisfaction
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
Review: Application mobility in pervasive computing: A survey
Pervasive and Mobile Computing
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In a mobile environment, it is desirable for mobile applications to adapt their behaviors to the changing context. However, adaptation mechanism may emphasize more on overall system performance, while neglecting the needs of individual. We present a generalized Adaptive Middleware Infrastructure (AMI) to cater for individual needs in a fair manner, while maintaining optimal system performance. Furthermore, due to the vagueness in context nature and uncertainty in context aggregation for adaptation, we propose a Fuzzy-based Service Adaptation Model (FSAM) to achieve generality and improve the effectiveness of service adaptation. By fuzzification of the context and measuring the fitness degree between the current context and the optimal situation, FSAM adopts the most appropriate service. We have evaluated the FSAM inference engine within the middleware AMI by an application Campus Assistant. The performance is analyzed and compared with a conventional threshold-based approach