A logic-based calculus of events
New Generation Computing
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Computer Networks: The International Journal of Computer and Telecommunications Networking
ICPPW '07 Proceedings of the 2007 International Conference on Parallel Processing Workshops
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SERVICES '08 Proceedings of the 2008 IEEE Congress on Services - Part I
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DSD '08 Proceedings of the 2008 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools
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Electronic Notes in Theoretical Computer Science (ENTCS)
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SEA '07 Proceedings of the 11th IASTED International Conference on Software Engineering and Applications
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SEUS '09 Proceedings of the 7th IFIP WG 10.2 International Workshop on Software Technologies for Embedded and Ubiquitous Systems
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Proceedings of the 2010 ICSE Workshop on Software Engineering for Sensor Network Applications
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Proceedings of the 2010 ICSE Workshop on Software Engineering for Sensor Network Applications
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ECSA'07 Proceedings of the First European conference on Software Architecture
Runtime variability for dynamic reconfiguration in wireless sensor network product lines
Proceedings of the 16th International Software Product Line Conference - Volume 2
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This paper proposes and evaluates a model-driven performance engineering framework for wireless sensor networks (WSNs). The proposed framework, called Moppet, is designed for application developers to rapidly implement WSN applications and estimate their performance. It leverages the notion of feature modeling so that it allows developers to graphically and intuitively specify features (e.g., functionalities and configuration policies) in their applications. It also validates a set of constraints among features and generates application code. Moppet also uses event calculus in order to estimate a WSN application's performance without generating its code nor running it on simulators and real networks. Currently, it can estimate power consumption and lifetime of each sensor node. Experimental results show that, in a small-scale WSN of 16 iMote nodes, Moppet's average performance estimation error is 8%. In a large-scale simulated WSN of 400 nodes, its average estimation error is 2%. Moppet scales well to the network size with respect to estimation accuracy. Moppet generates lightweight nesC code that can be deployed with TinyOS on resource-limited nodes. The current experimental results show that Moppet is well-applicable to implement biologically-inspired routing protocols such as pheromone-based gradient routing protocols and estimate their performance.