Intelligent backtracking on constraint satisfaction problems: experimental and theoretical results
Intelligent backtracking on constraint satisfaction problems: experimental and theoretical results
A Middleware Infrastructure for Active Spaces
IEEE Pervasive Computing
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Aura: an Architectural Framework for User Mobility in Ubiquitous Computing Environments
WICSA 3 Proceedings of the IFIP 17th World Computer Congress - TC2 Stream / 3rd IEEE/IFIP Conference on Software Architecture: System Design, Development and Maintenance
PCOM - A Component System for Pervasive Computing
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Journal of Artificial Intelligence Research
A general backtrack algorithm that eliminates most redundant tests
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Efficient resource-aware hybrid configuration of distributed pervasive applications
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Adaptive Composition of Distributed Pervasive Applications in Heterogeneous Environments
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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The adaptation of pervasive applications is in the focus of many current research projects. While decentralized adaptation is mandatory in infrastructureless ad hoc scenarios, most realistic pervasive application scenarios are situated in heterogeneous environments where additional computation power of resource-rich devices can be exploited. Therefore, we propose a hybrid approach to application configuration that applies centralized as well as decentralized configuration as appropriate in the given environment. In this paper we introduce the Direct Backtracking algorithm that represents an efficient way for centralized configuration and adaptation of pervasive applications in heterogeneous scenarios. In our evaluation, we show that compared with other centralized algorithms, our algorithm significantly reduces adaptation latency as it avoids unnecessary adaptations that arise in many other backtracking algorithms, without significantly increasing memory waste. This is achieved by introducing two mechanisms: 1. proactive backtracking avoidance and 2. intelligent backtracking.