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Performance evaluation of sensor networks by statistical modeling and euclidean model checking

Performance evaluation of sensor networks by statistical modeling and euclidean model checking Performance Evaluation of Sensor Networks by Statistical Modeling and Euclidean Model Checking YOUNGMIN KWON, Microsoft Corporation GUL AGHA, University of Illinois at Urbana Champaign Modeling and evaluating the performance of large-scale wireless sensor networks (WSNs) is a challenging problem. The traditional method for representing the global state of a system as a cross product of the states of individual nodes in the system results in a state space whose size is exponential in the number of nodes. We propose an alternative way of representing the global state of a system: namely, as a probability mass function (pmf) which represents the fraction of nodes in different states. A pmf corresponds to a point in a Euclidean space of possible pmf values, and the evolution of the state of a system is represented by trajectories in this Euclidean space. We propose a novel performance evaluation method that examines all pmf trajectories in a dense Euclidean space by exploring only finite relevant portions of the space. We call our method Euclidean model checking. Euclidean model checking is useful both in the design phase--where it can help determine system parameters based on a specification--and in the evaluation phase--where it can help verify http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Sensor Networks (TOSN) Association for Computing Machinery

Performance evaluation of sensor networks by statistical modeling and euclidean model checking

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1550-4859
DOI
10.1145/2489253.2489256
Publisher site
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Abstract

Performance Evaluation of Sensor Networks by Statistical Modeling and Euclidean Model Checking YOUNGMIN KWON, Microsoft Corporation GUL AGHA, University of Illinois at Urbana Champaign Modeling and evaluating the performance of large-scale wireless sensor networks (WSNs) is a challenging problem. The traditional method for representing the global state of a system as a cross product of the states of individual nodes in the system results in a state space whose size is exponential in the number of nodes. We propose an alternative way of representing the global state of a system: namely, as a probability mass function (pmf) which represents the fraction of nodes in different states. A pmf corresponds to a point in a Euclidean space of possible pmf values, and the evolution of the state of a system is represented by trajectories in this Euclidean space. We propose a novel performance evaluation method that examines all pmf trajectories in a dense Euclidean space by exploring only finite relevant portions of the space. We call our method Euclidean model checking. Euclidean model checking is useful both in the design phase--where it can help determine system parameters based on a specification--and in the evaluation phase--where it can help verify

Journal

ACM Transactions on Sensor Networks (TOSN) – Association for Computing Machinery

Published: Jul 1, 2013

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