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A Low-Cost Service Node Selection Method in Crowdsensing Based on Region-Characteristics

  • Zhenlong Peng
  • Jian AnEmail author
  • Xiaolin Gui
  • Dong Liao
  • RuoWei Gui
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

Crowdsensing is a human-centred perception model. Through the cooperation of multiple nodes, an entire sensing task is completed. To improve the efficiency of accomplishing sensing missions, a proper and cost-effective set of service nodes is needed to perform tasks. In this paper, we propose a low-cost service node selection method based on region features, which builds on relationships between task requirements and geographical locations. The method uses DBSCAN to cluster service nodes and calculate the centre point of each cluster. The region then is divided into regions according to rules of Voronoi diagram. Local feature vectors are constructed according to the historical records in each divided region. When a particular perception task arrives, Analytic Hierarchy Process (AHP) is used to match the feature vector of each region to mission requirements to get a certain number of service nodes satisfying the characteristics. To get a lower cost output, a revised Greedy Algorithm is designed to filter the exported service nodes to get the required low-cost service nodes. Experimental results suggest that the proposed method shows promise in improving service node selection accuracy and the timeliness of finishing tasks.

Keywords

Crowdsensing Service node selection Local feature vector 

Notes

Acknowledgments

This work was supported partly by the NSFC Grant No 61502380, partly by Science and Technology Program of Shenzhen (JCYJ20170816100939373).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhenlong Peng
    • 1
    • 3
  • Jian An
    • 2
    Email author
  • Xiaolin Gui
    • 1
    • 4
  • Dong Liao
    • 1
    • 4
  • RuoWei Gui
    • 1
    • 4
  1. 1.School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anPeople’s Republic of China
  2. 2.Xi’an Jiaotong University Shenzhen Research SchoolHigh-Tech ZoneShenzhenPeople’s Republic of China
  3. 3.TSL Business SchoolQuanzhou Normal UniversityQuanzhouPeople’s Republic of China
  4. 4.Shaanxi Province Key Laboratory of Computer NetworkXi’anPeople’s Republic of China

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