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A simulation model for area coverage and loss probability on mobile sensor networks

Author

Listed:
  • F. C. S. Eiras

    (University of São Paulo)

  • W. L. Zucchi

    (University of São Paulo)

Abstract

The importance and applications of sensor networks have grown significantly in recent years. These networks are implemented in an ad-hoc manner, where messages pass through multiple intermediate nodes until they reach a central node (sink node). Depending on the type of application, the message loss rate can compromise its performance and even make its use unfeasible. The objective of this work is to obtain the probability of loss in the transmission of a message in a network of multiple hops through a simulator developed in the scope of this work as a tool of low computational cost and easy access, considering connectivity and displacement at high speed of the nodes. For the validation of the simulator, simple cases were used where the probability of loss obtained was compared with mathematical results deduced here. The results presented allow dimensioning the characteristics of coverage and number of nodes in a sensor network.

Suggested Citation

  • F. C. S. Eiras & W. L. Zucchi, 2021. "A simulation model for area coverage and loss probability on mobile sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(1), pages 3-16, January.
    • Handle: RePEc:spr:telsys:v:76:y:2021:i:1:d:10.1007_s11235-020-00698-2
    DOI: 10.1007/s11235-020-00698-2
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    References listed on IDEAS

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    1. Chakraborty, Suparna & Goyal, N.K. & Mahapatra, S. & Soh, Sieteng, 2020. "A Monte-Carlo Markov chain approach for coverage-area reliability of mobile wireless sensor networks with multistate nodes," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
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    1. F. C. S. Eiras & W. L. Zucchi, 2022. "Measuring synchronization precision in mobile sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 81(2), pages 253-267, October.

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