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Continuous Objects Detection Based on Optimized Greedy Algorithm in IoT Sensing Networks

  • Jin Diao
  • Deng Zhao
  • Jine Tang
  • Zehui Cheng
  • Zhangbing ZhouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11637)

Abstract

Sensing network of the Internet of Things (IoT) has become the infrastructure for facilitating the monitoring of potential events, where the accuracy and energy-efficiency are essential factors to be considered when determining the boundary of continuous objects. This article proposes an energy-efficient boundary detection mechanism in IoT sensing network. Specifically, a sleeping mechanism is adopted to detect the relatively coarse boundary through applying the convex hull algorithm. Leveraging the analysis of the relation for corresponding boundary nodes, the area around a boundary node is categorized as three types of sub-areas with descending possibility of event occurrence. An optimized greedy algorithm is adopted to selectively activate certain numbers of 1-hop neighboring IoT nodes in respective sub-areas, to avoid the activation of all 1-hop neighboring nodes in a flooding manner. Consequently, the boundary is refined and optimized according to sensory data of these activated IoT nodes. Experimental results demonstrate that our method can achieve better detection accuracy, while reducing energy consumption to a large extent, compared to the state of arts.

Keywords

Boundary detection Continuous objects IoT sensing networks Energy efficiency Greedy algorithm 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant no. 61772479 and 61662021).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jin Diao
    • 1
  • Deng Zhao
    • 1
  • Jine Tang
    • 2
  • Zehui Cheng
    • 3
  • Zhangbing Zhou
    • 1
    • 4
    Email author
  1. 1.School of Information EngineeringChina University of GeosciencesBeijingChina
  2. 2.School of Artificial IntelligenceHebei University of TechnologyTianjinChina
  3. 3.Computer Science DepartmentUniversity of CaliforniaSanta CruzUSA
  4. 4.Computer Science DepartmentTELECOM SudParisÉvryFrance

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