Optimal and Meta-Heuristic Algorithms for Object Tracking in Multi-Sink Wireless Sensor Networks

Authors

  • Fatma H. El-Fouly
  • Rabie A. Ramadan

Keywords:

object tracking, swarm intelligence, multi-sink, reliability, energy balance, traffic aware

Abstract

Multi-sink Wireless Sensor Networks (WSNs) are being used in many applications due to its significant advantages over the single sink. One of the major applications in WSNs is object tracking due to its wide real-life applications such as wildlife animal monitoring and military area intrusion detection. Many of the prior researches on object tracking in WSNs have focused on tracking the location of objects accurately but few researches on data reporting. In this work, we propose an efficient data reporting method for object tracking in multi-sink WSNs. Since the energy resources are limited in the sensor nodes, full utilization of resources with minimum energy remains the main consideration when a WSN application is designed. Moreover, Network reliability has become an essential aspect that should be considered beside energy conservation to guarantee the quality of network. Consequentially, 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 our 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, proposes a new scheme for selecting the optimal sink for data transmission and then proposes a swarm intelligence for solving the optimization problem. Through simulation, the performance of the proposed approach is evaluated and analyzed compared with the previous work which is related to our topic such as DTAR, NBPR, and MSDDGR protocols.

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Published

2021-03-05

How to Cite

H. El-Fouly, F. ., & A. Ramadan, R. . (2021). Optimal and Meta-Heuristic Algorithms for Object Tracking in Multi-Sink Wireless Sensor Networks. WAS Science Nature (WASSN) ISSN: 2766-7715, 2(1). Retrieved from https://worldascience.com/journals/index.php/wassn/article/view/8

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Computer Science & Mathematics

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