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Efficient Data Reporting in a Multi-Object Tracking Using WSNs

Author

Listed:
  • Fatma H. Elfouly

    (Department of Electronics and Electrical Communications, Higher Institute of Engineering, El-Shorouk Academy, El-Shorouk City, Egypt)

  • Rabie A. Ramadan

    (Department of Computer Science and Engineering College, Hail University, Hail, Saudi Arabia)

  • Mohamed I. Mahmoud

    (Department of Control Engineering and Industrial Electronics, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt)

  • Moawad I. Dessouky

    (Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt)

Abstract

Object tracking is one of the most important applications in wireless sensor networks (WSNs). Many recent articles have been dedicated to localization of objects; however, few of these articles were concentrated on the reliability of network data reporting along with objects localization. In this work, the authors propose an efficient data reporting method for object tracking in WSNs. This paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in the authors' model where the sensed data can reach the sink node in a more reliable way. This work first formulates the problem as 0/1 Integer Linear Programming (ILP) problem, and then proposes a SWARM intelligence for solving the optimization problem. Through simulation, the performance of proposed method to report information about the detected objects to the sink is compared with the previous works related to the authors' topic, such as LR-based object tracking algorithm, SEB, EPWSN, and ACO.

Suggested Citation

  • Fatma H. Elfouly & Rabie A. Ramadan & Mohamed I. Mahmoud & Moawad I. Dessouky, 2017. "Efficient Data Reporting in a Multi-Object Tracking Using WSNs," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 6(1), pages 38-57, January.
    • Handle: RePEc:igg:jsda00:v:6:y:2017:i:1:p:38-57
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    Cited by:

    1. Sundarakumar M. R. & Mahadevan G. & Ramasubbareddy Somula & Sankar Sennan & Bharat S. Rawal, 2021. "An Approach in Big Data Analytics to Improve the Velocity of Unstructured Data Using MapReduce," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(4), pages 1-25, October.

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