4.6 Article

WSN-SES/MB: System Entity Structure and Model Base Framework for Large-Scale Wireless Sensor Networks

Journal

SENSORS
Volume 21, Issue 2, Pages -

Publisher

MDPI
DOI: 10.3390/s21020430

Keywords

wireless sensor networks; network simulator; system entity structure; model base; discrete event system specification

Funding

  1. MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program [IITP-2020-2018-0-01799]
  2. Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2017R1D1A1B03034644]

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The paper proposes a novel framework for constructing the structure and models of large-scale wireless sensor networks based on discrete event system specification formalism. This framework saves time and cost in constructing simulation models and increases model diversity through a pruning algorithm in the modeling process.
Large-scale wireless sensor networks are characterized by stringent energy and computation restrictions. It is exceedingly difficult to change a sensor network's environment configurations, such as the number of sensor nodes, after deployment of the nodes. Although several simulators are able to variously construct simulation models for sensor networks before their deployment, the configurations should be modified with extra human effort as the simulators cannot freely generate diverse models. In this paper, we propose a novel framework, called a system entity structure and model base for large-scale wireless sensor networks (WSN-SES/MB), which is based on discrete event system specification formalism. Our proposed framework synthesizes the structure and models for sensor networks through our modeling construction process. The proposed framework achieves time and cost savings in constructing discrete event simulation-based models. In addition, the framework increases the diversity of simulation models by the process's pruning algorithm. The simulation results validate that the proposed framework provides up to 8% time savings and up to 23% cost savings as compared to the manual extra effort.

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