KSII Transactions on Internet and Information Systems (TIIS)

  • 제12권6호
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  • Pages.2658-2679
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  • 2018
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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A Novel Random Scheduling Algorithm based on Subregions Coverage for SET K-Cover Problem in Wireless Sensor Networks

  • Muhammad, Zahid (Electrical and Computer Engineering Department, Sungkyunkwan University) ;
  • Roy, Abhishek (System Design Lab., Networks Division Samsung Electronics) ;
  • Ahn, Chang Wook (Department of Electrical Engineering & Computer Science, Gwangju Institute of Science and Technology (GIST)) ;
  • Sachan, Ruchi (Electrical and Computer Engineering Department, Sungkyunkwan University) ;
  • Saxena, Navrati (Electrical and Computer Engineering Department, Sungkyunkwan University)
  • 투고 : 2017.08.29
  • 심사 : 2018.01.15
  • 발행 : 2018.06.30
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초록

This paper proposes a novel Random Scheduling Algorithm based on Subregion Coverage (RSASC), to solve the SET K-cover problem (an NP-complete problem). SET K-cover problem distributes the set of sensors into the maximum number of mutually exclusive subsets (MESSs) in such a way that each of them can be scheduled for lifetime extension of WSN. Sensor coverage divides the target region into different subregions. RSASC first sorts the subregions in the ascending order concerning their sensor coverage. Then, it forms the subregion groups according to their similar sensor coverage. Lastly, RSASC ensures the K-coverage of each subregion from every group by randomly scheduling the sensors. We consider the target-coverage and area-coverage applications of WSN to analyze the usefulness of our proposed RSASC algorithm. The distinct quality of RSASC is that it utilizes less number of deployed sensors (33% less) to form the optimum number of MESSs with the higher computational speed (saves more than 93% of the time) as compared to the existing three algorithms.

키워드

과제정보

연구 과제번호 : Development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis

연구 과제 주관 기관 : National Research Foundation of Korea (NRF), IITP

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