Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 273-277.doi: 10.11896/JsJkx.190700040

• Computer Network • Previous Articles     Next Articles

Emergency Task Assignment Method Based on CQPSO Mobile Crowd Sensing

LI Jian-Jun, WANG Xiao-ling, YANG Yu and FU Jia   

  1. School of Computer and Information Engineering,Harbin University of Commerce,Harbin 150028,China
    HeilongJiang Provincial Key Laboratory of Electronic Commerce and Information Processing,Harbin 150028,China
  • Published:2020-07-07
  • About author:LI Jian-Jun, born in 1973, Ph.D, asso-ciate professor.His main research inte-rests include E-commerce and business intelligence.
    WANG Xiao-ling, born in 1994, postgraduate.Her main research interests include business intelligence and so on.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (60975071),HeilongJiang Province New Think Tank Research ProJect (18ZK015),HeilongJiang Province Philosophy and Social Science Research ProJect (17GLE298,16EDE16),Harbin University of Commerce School-level ProJect (18XN065),Harbin University of Commerce Ph.D.Research Foundation Fund (2019DS029).

PDF (PC)

765

Abstract: In view of the problem of emergency task assignment in mobile crowd sensing task assignment type,and considering how to assign tasks under certain time constraints,with the lowest perceived cost and the maximum number of tasks,and extends it with a swarm intelligence algorithm,a method based on chaotic quantum particle swarm emergency task assignment (CQPSOETA) is proposed.Experimental results show that the chaotic quantum particle swarm optimization algorithm has a good application effect in the allocation of mobile crowd sensing emergency tasks.It can achieve the emergency task assignment optimization goal in a short time,greatly improve the convergence speed of the algorithm,avoid falling into local optimum,and obtain global optimal effect.

Key words: Chaotic quantum particle swarm, Emergency task assignment, Mobile crowd sensing, Swarm intelligence

CLC Number: 

  • TP391
[1] LUIS G J,IDALIDES J,VERGARA L,et al.A Survey of Incentive Techniques for Mobile Crowd Sensing .IEEE Internet of Things Journal,2015,2(5):370-380.
[2] WANG L Y,ZHANG D Q,WANG Y S,et al.Sparse mobile crowdsensing:challenges and opportunities.IEEE Communications Magazine,2016,54(7):161-167.
[3] CARDONE G,FOSCHINI L,BELLAVISTA P,et al.Fostering participation in smart cities:A geo-social crowd sensing platform .IEEE Communications Magazine,2013,51(6):112-119.
[4] LIU Y,GUO B,WU W L,et al.Multitask-Oriented participant selection in mobile crowd sensing.Chinese Journal of Computers,2017,40(8):1872-1887.
[5] REDDY S,ESTRIN D,SRIVASTAVA M.Recruitment framework for participatory sensing data collections//Proc of the 8th Int Conf on Pervasive Computing.New York:ACM,2010:138-155.
[6] ZHANG D Q,XIONG H Y,WANG L Y,et al.CrowdRecruiter:Selecting participants for piggyback crowd sensing under probabilistic coverage constrain//Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing.2014:703-714.
[7] OZYAGCI O Z,MATSKIN M.Truthful incentive mechanism for mobile crowd sensing with smart consumer devices //Proc of the 40th Annual Computer Software and Applications Conference.2016:282-287.
[8] WANG J,TANG J,YANG D,et al.Quality-aware and finegrained incentive mechanisms for mobile crowd sensing//Proc of the 36th International Conference on Distributed Computing Systems.2016:354-363.
[9] YANG G,HE S,SHI Z,et al.Promoting cooperation by the social incentive mechanism in mobile crowd sensing .IEEE Communications Magazine,2017,55(3):86-92.
[10] POURYAZDAN M,KANTARCI B,SOYATA T,et al.Quantifying user reputation scores,data trustworthiness,and user incentives in mobile crowd-sensing .IEEE Access,2017,5:1382-1397.
[11] JIN H,SU L,DING B,et al.Enabling privacy-preserving incentives for mobile crowd sensing systems //Proc of the 36th International Conference on Distributed Computing Systems.2016:344-353.
[12] AZZAM R,MIZOUNI R,OTROK H,et al.GRS:A GroupBased Recruitment System for Mobile Crowd Sensing.Journal of Network & Computer Applications,2016,72:38-50.
[13] MESSAOUD R B,GHAMRI D Y.Fair Qo I and Energ aware Task Allocation in Participatory Sensing//Proc of IEEE Wireless Communications and Networking Conference.2016:1-6.
[14] WANG Z,HUANG D,WU H,et al.Qos-constrained sensing task assignment for mobile crowd sensing//Proc of IEEE Global Communications Conference.2017:311-316.
[15] XIAO M,WU J,HUANG H,et al.Deadline-sensitive user recruitment for probabilistically collaborative mobile crowdsensing //Proc of the 36th International Conference on Distributed Computing Systems.2017:721-722.
[16] WANG J T,WANG F,WANG Y S,et al.HyTasker:Hybrid Task Allocation in Mobile Crowd Sensing .IEEE Transactions on Mobile Computing.2018:1-13.
[17] XING Q,SUN X M,YUAN C M.Assignment mechanism for spatial tasks in mobile crowd sensing .Application Research of Computers.http://kns.cnki.net/kcms/detail/51.1196.tp.20181225.1627.013.html.
[18] SONG Z H,LI Z,CHEN X.Mobile crowdsensing task distribution mechanism based on compressed sensing .Journal of Computer Applications,2019,39(1):15-21.
[19] WANG L,YU Z W GUO B,et al.Crowd sensing socialization task allocation based on mobile social network .Journal of ZheJiang University(Engineering Science),2018,52(9):1709-1716.
[20] WANG L,YU Z,GUO B,et al.Mobile Crowd Sensing Task Optimal Allocation:A Mobility Pattern Matching Perspective .Frontiers of Computer Science,2018,12(2):231-244.
[21] LIU G.Research on Task Assignment Model under Constraints .Shanxi:Xi’an University of Technology,2018.
[1] ZHANG Zhi-qiang, LU Xiao-feng, SUI Lian-sheng, LI Jun-huai. Salp Swarm Algorithm with Random Inertia Weight and Differential Mutation Operator [J]. Computer Science, 2020, 47(8): 297-301.
[2] ZHANG Xin-ming, LI Shuang-qian, LIU Yan, MAO Wen-tao, LIU Shang-wang, LIU Guo-qi. Coyote Optimization Algorithm Based on Information Sharing and Static Greed Selection [J]. Computer Science, 2020, 47(5): 217-224.
[3] HUANG Guang-qiu, LU Qiu-qin. Vertical Structure Community System Optimization Algorithm [J]. Computer Science, 2020, 47(4): 194-203.
[4] HUANG Guang-qiu,LU Qiu-qin. Protected Zone-based Population Migration Dynamics Optimization Algorithm [J]. Computer Science, 2020, 47(2): 186-194.
[5] LI Zhuo, XU Zhe, CHEN Xin, LI Shu-qin. Location-related Online Multi-task Assignment Algorithm for Mobile Crowd Sensing [J]. Computer Science, 2019, 46(6): 102-106.
[6] ZHANG Xiao-feng, WANG Xiu-ying. Comprehensive Review of Grey Wolf Optimization Algorithm [J]. Computer Science, 2019, 46(3): 30-38.
[7] CHENG Xian-yi, SHI Quan, ZHU Jian-xin, CHEN Feng-mei, DAI Ran-ran. VID Model of Vehicles-infrastructure-driver Collaborative Control in Big Data Environment [J]. Computer Science, 2019, 46(11A): 185-188.
[8] ZHAO Hong-wei, TIAN Li-wei. Cloud Computing Resource Scheduling Strategy Based on Improved Bacterial Foraging Algorithm [J]. Computer Science, 2019, 46(11): 309-314.
[9] XIAO Liang and LIU Si-tong. Asynchronous Collaborative Chicken Swarm Optimization with Mutation Based on Cognitive Diversity [J]. Computer Science, 2017, 44(Z6): 99-104.
[10] LI Yu-ling, WANG Xiu-ling and ZHOU Jian-ming. Cooperative Electromagnetic Compatibility Control Complexity Optimization Algorithm Based on Cluster Filter for Mobile Crowd Sensing [J]. Computer Science, 2016, 43(4): 115-117.
[11] ZHANG Jia-fan, GUO Bin, LU Xin-jiang, YU Zhi-wen and ZHOU Xing-she. Approach for Urban Popular Event Detection Using Mobile Crowdsourced Data [J]. Computer Science, 2015, 42(Z6): 5-9.
[12] SUN Zhen-long, LI Xiao-ye and WANG Ying. Improved Simple Particle Swarm Optimization Algorithm [J]. Computer Science, 2015, 42(Z11): 86-88.
[13] FU Qiang,TONG Nan,ZHONG Cai-ming and ZHAO Yi-ming. Firefly Algorithm Based on Improved Evolutionism [J]. Computer Science, 2014, 41(3): 228-231.
[14] LIU Jian-hua,ZHANG Yong-hui,ZHOU Li and HE Wen-wu. Particle Swarm Optimization with Weight Increasing [J]. Computer Science, 2014, 41(3): 59-65.
[15] ZHUANG Pei-xian and DAI Sheng-kui. Improved Geese Swarm Optimization Algorithm Based on Gaussian Weighted Sum [J]. Computer Science, 2013, 40(Z6): 87-89.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.