Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 10
2971-2975

Research on selection method of crowd sensing participants based on GACO

Li Jianjun1,2
Wang Xiaoling1,2
Yang Yu1,2
Fu Jia1,2
1. School of Computer & Information Engineering, Harbin University of Commerce, Harbin 150028, China
2. Heilongjiang Provincial Key Laboratory of Electronic Commerce & Information Processing, Harbin 150028, China
DOI: 10.19734/j.issn.1001-3695.2019.06.0201

Abstract

Participant selection method is one of the important contents of crowd sensing research. Existing research still has some shortcomings. Only the attributes such as task time or task area coverage are considered, which makes the selected participants perform tasks less efficiently. Therefore, in order to comprehensively consider the task time and task area coverage constraints, this paper proposed a selection method of crowd sensing participant based on the greedy ant colony algorithm(PSGACO) to achieve the highest task execution efficiency and the minimum incentive cost of the crowd sensing platform. The method mainly selected the participants who were suitable for performing the publishing task by the concentration of the ant pheromone concentration of the candidate participants, and greatly improved the task execution efficiency. Finally, it compared the proposed PS-GACO method with the common participant selection method through simulation experiments. The experimental results show that PS-GACO is superior to the other two methods in terms of algorithm running time, task execution efficiency and incentive cost, and has a good application prospect for the crowd sensing participant selection.

Foundation Support

国家自然科学基金资助项目(60975071)
黑龙江省新型智库研究项目(18ZK015)
黑龙江省哲学社会科学研究规划项目(17GLE298,16EDE16)
哈尔滨商业大学校级课题资助项目(18XN065)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.06.0201
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 10
Section: Algorithm Research & Explore
Pages: 2971-2975
Serial Number: 1001-3695(2020)10-018-2971-05

Publish History

[2020-10-05] Printed Article

Cite This Article

李建军, 汪校铃, 杨玉, 等. 基于GACO的群智感知参与者选择方法研究 [J]. 计算机应用研究, 2020, 37 (10): 2971-2975. (Li Jianjun, Wang Xiaoling, Yang Yu, et al. Research on selection method of crowd sensing participants based on GACO [J]. Application Research of Computers, 2020, 37 (10): 2971-2975. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)