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

Youngmin Kwon, Gul Agha

Research output: Contribution to journalArticlepeer-review


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 performance properties of a system. We illustrate the utility of Euclidean model checking by using it to design a time difference of arrival (TDoA) distance measurement protocol and to evaluate the protocol's implementation on a 90-node WSN. To facilitate such performance evaluations, we provide a Markov model estimation method based on applying a standard statistical estimation technique to samples resulting from the execution of a system.

Original languageEnglish (US)
Article number39
JournalACM Transactions on Sensor Networks
Issue number4
StatePublished - Jul 2013


  • DTMC estimation
  • ILTL
  • Performance evaluation
  • Probabilistic model checking
  • Statistical testing
  • TDoA
  • Wireless sensor networks

ASJC Scopus subject areas

  • Computer Networks and Communications


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