Introduction to Algorithms
TOSSIM: accurate and scalable simulation of entire TinyOS applications
Proceedings of the 1st international conference on Embedded networked sensor systems
Avrora: scalable sensor network simulation with precise timing
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Congestion Avoidance Based on Lightweight Buffer Management in Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Adaptive design optimization of wireless sensor networks using genetic algorithms
Computer Networks: The International Journal of Computer and Telecommunications Networking
Analysing qos trade-offs in wireless sensor networks
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
Simulating wireless and mobile networks in OMNeT++ the MiXiM vision
Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops
Algorithms and Protocols for Wireless Sensor Networks
Algorithms and Protocols for Wireless Sensor Networks
Ultra-Low Energy Wireless Sensor Networks in Practice: Theory, Realization and Deployment
Ultra-Low Energy Wireless Sensor Networks in Practice: Theory, Realization and Deployment
Proceedings of the 4th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
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With the increasing capabilities of Wireless Sensor Networks (WSN), complexity and expectation of the WSN applications increase as well. In order to make design-space exploration possible, it is necessary to have fast models that provide adequate insight in system behavior. In this paper, we propose a highly abstracted, hierarchical, system-level modeling method for WSN. Based on the model properties, fast simulation techniques can be applied. First, an abstract discrete event simulation based on a Probabilistic Graph Model (PGM) is introduced. Then, a fast Monte Carlo simulation approach is proposed for speeding up the simulation process. This approach combines Stochastic-Variable Graph Models (SVGM), providing a high level of abstraction, with shortest path calculations. As a case study, a temperature mapping application in a gossip-based WSN is used, showing a good accuracy of the model predictions.