Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (5): 371-379.doi: 10.11896/jsjkx.210200005

• Interdiscipline & Frontier • Previous Articles    

Participant Selection Strategies Based on Crowd Sensing for River Environmental Monitoring

LI Xiao-dong1, YU Zhi-yong1,2, HUANG Fang-wan1,2, ZHU Wei-ping1, TU Chun-yu1, ZHENG Wei-nan1   

  1. 1 College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108,China
    2 Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University),Fuzhou 350108,China
  • Received:2021-02-01 Revised:2021-05-27 Online:2022-05-15 Published:2022-05-06
  • About author:LI Xiao-dong,born in 1994,postgra-duate.His main research interests include crowd sensing and so on.
    HUANG Fang-wan,born in 1980,master,senior lecturer,is a member of China Computer Federation.Her main research interests include computational intelligence,machine learning and big data analysis.
  • Supported by:
    National Natural Science Foundation of China(61772136).

PDF (PC)

631

Abstract: The surrounding environment of rivers in city is often damaged and polluted.How to effectively monitor rivers has gradually attracted the attention of public,government and academia.At present,traditional monitoring methods are facing with high cost,insufficient coverage and other defects.With the increasing popularity of intelligent mobile devices,a new idea of using crowd sensing to efficiently monitor the river environment is proposed in this paper.The problem can be described as the assumption that each river reach contains c monitoring points,and then r users are selected according to the movement tracks of a large number of users to jointly complete the monitoring of all river reaches in s periods.It is stipulated that the smaller the number of users r,the less the monitoring cost.The stepwise-greedy strategy,the global-greedy strategy and the integer-programming stra-tegy are designed to solve this problem,that is,to select the least number of participants to achieve the monitoring goal of “s durations-c ranges-r users”.In this paper,the above strategies are applied to environmental monitoring of some rivers in Taijiang,Fuzhou.Experimental results show that the above strategies can obtain better solutions than the random strategy,and the integer-programming strategy has the best performance.However,with the increase of the scale of the problem,the implicit enumeration algorithm used to solve the small-scale integer programming will be unable to solve the situation.Motivated by this,this paper designs a discrete particle swarm optimization algorithm based on greedy initialization(GI-DPSO).Although this algorithm can solve large-scale integer programming,it is time-consuming.Considering the monitoring cost and computational cost comprehensively,it is suggested that the integer-programming strategy can be adopted for small-scale problems and the global-greedy strategy can be adopted for large-scale problems.

Key words: Crowd sensing, Discrete particle swarm optimization algorithm, Greedy strategy, Integer programming, River environmental monitoring

CLC Number: 

  • TP391
[1]GAO R W.Reshaping the relationship between inland rivers and people’s livelihood from the perspective of humanism:A study on the comprehensive accessibility of inland rivers in Fuzhou[J].Fujian Architecture,2020(8):1-9.
[2]Fuzhou Urban and Rural Construction Bureau.Measures for the Management of Urban Inland Rivers in Fuzhou[R].Fuzhou,2019.
[3]DUTTA J,CHOWDHURY C,ROY S,et al.Towards smartcity:sensing air quality in city based on opportunistic crowd-sensing[C]//Proceedings of the 18th International Conference on Distributed Computing and Networking.2017:1-6.
[4]GANTI R K,YE F,LEI H.Mobile crowdsensing:current state and future challenges[J].IEEE Communications Magazine,2011,49(11):32-39.
[5]RADU V,KRIARA L,MARINA M K.Pazl:A mobile crowd-sensing based indoor WiFi monitoring system[C]//Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013).IEEE,2013:75-83.
[6]CARDONE G,FOSCHINI L,BELLAVISTA P,et al.Fostering participaction in smart cities:a geo-social crowdsensing platform[J].IEEE Communications Magazine,2013,51(6):112-119.
[7]GUO W,ZHU W,YU Z,et al.A survey of task allocation:Contrastive perspectives from wireless sensor networks and mobile crowdsensing[J].IEEE Access,2019,7:78406-78420.
[8]LIU Y,GUO B,WU W L,et al.Research on the method of selecting multitask participants for mobile group intelligence perception[J].Chinese Journal of Computers,2017,40(8):1872-1887.
[9]LI H.Participant Selection and Task Assignment in MobileCrowd Sensing[D].Charlott:University of North Carolina at Charlotte,2018.
[10]LUDWIG T,REUTER C,PIPEK V.What you see is what I need:Mobile reporting practices in emergencies[C]//ECSCW 2013:Proceedings of the 13th European Conference on Compu-ter Supported Cooperative Work.London:Springer,2013:181-206.
[11]DUTTA J,GAZI F,ROY S,et al.AirSense:Opportunistic crowd-sensing based air quality monitoring system for smart city[C]//Sensors.IEEE,2017.
[12]QIN Z,ZHU Y.NoiseSense:A crowd sensing system for urban noise mapping service[C]//2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS).IEEE,2016:80-87.
[13]RAMBURN T,BADOREEA D,CHEERKOOT-JALIM S.Drive-MU:A Real-time Road-Traffic Monitoring Android Application for Mauritius[C]//2019 Conference on Next Generation Computing Applications (NextComp).IEEE,2019:1-8.
[14]EL KHAILI M,BAKKOURY J,KHIAT A,et al.Crowdsour-cing by IoT using LabVIEW for Measuring the Air Quality[C]//Proceedings of the 3rd International Conference on Smart City Applications.2018:1-8.
[15]LEE H P,GARG S,LIM K M.Crowdsourcing of environmental noise map using calibrated smartphones[J].Applied Acoustics,2020,160:107130.
[16]JING Y,GUO B,LIU Y,et al.CrowdTracker:object trackingusing mobile crowd sensing[C]//Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers.2017:85-88.
[17]KIM K,ZABIHI H,KIM H,et al.TrailSense:A crowdsensing system for detecting risky mountain trail segments with walking pattern analysis[J].Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies,2017,1(3):1-31.
[18]WU F,YANG S,ZHENG Z,et al.Fine Grained User Profiling for Personalized Task Matching in Mobile Crowdsensing[J].IEEE Transactions on Mobile Computing,2020,1(1):99-112.
[19]SONG Z,ZHANG B,LIU C H,et al.QoI-aware energy-efficient participant selection[C]//2014 Eleventh Annual IEEE International Conference on Sensing,Communication,and Networking (SECON).IEEE,2014:248-256.
[20]LI H,LI T,WANG Y.Dynamic participant recruitment of mobile crowd sensing for heterogeneous sensing tasks[C]//2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems.IEEE,2015:136-144.
[21]ZHANG D,XIONG H,WANG L,et al.CrowdRecruiter:selecting participants for piggyback crowdsensing under probabilistic coverage constraint[C]//Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing.2014:703-714.
[22]REDDY S,ESTRIN D,SRIVASTAVA M.Recruitment framework for participatory sensing data collections[C]//Interna-tional Conference on Pervasive Computing.Berlin:Springer,2010:138-155.
[23]YU Z,ZHOU J,GUO W,et al.Participant selection for t-sweep k-coverage crowd sensing tasks[J].World Wide Web,2018,21(3):741-758.
[24]LV Q,GU J Q,XU S,et al.Structure and characteristics of the automatic monitoring system for rivers in Suzhou city[J].Urban and Rural Construction,2015(4):82-84.
[25]CHEN Z Q.Research on Intelligent Video Monitoring System under “River Chief System”[D].North China University of Water Conservancy and Hydropower,2019.
[26]TANG X Y.Design and development of an intelligent waterquality monitoring platform for unmanned ships[D].Haikou:Hainan University,2018.
[27]KARP R M.Reducibility among combinatorial problems[M]//Complexity of Computer Computations.Boston:Springer,1972:85-103.
[28]JUN W,DUAN L I.A New Implicit Enumeration Method for Polynomial 0-1 Programming and Applications[J].Systems Engineering-Theory & Practice,2007 (3):2.
[29]LU S H,HU M H.Multi-airport GDP release strategy based on heuristic implicit enumeration algorithm[J].Journal of Wuhan Institute of Technology,2010,32(1):97-99.
[30]KENNEDY J,EBERHART R.Particle swarm optimization[C]//Proceedings of ICNN’95-International Conference on Neural Networks.IEEE,1995:1942-1948.
[31]EBERHART R,KENNEDY J.A new optimizer using particleswarm theory[C]//Proceedings of the Sixth International Symposium on Micro Machine and Human Science(MHS’95).IEEE,1995:39-43.
[32]SHEN L C,HUO X H,NIU Y F.Overview of the Research Status of Discrete Particle Swarm Optimization Algorithms[J].Systems Engineering and Electronics,2008(10):1986-1990.
[1] LIU Zhang-hui, ZHENG Hong-qiang, ZHANG Jian-shan, CHEN Zhe-yi. Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems [J]. Computer Science, 2022, 49(6A): 619-627.
[2] ZHANG Hong-ying, SHEN Rong-miao, LUO Qian. Study on Optimal Scheduling of Gate Based on Mixed Integer Programming [J]. Computer Science, 2020, 47(8): 278-283.
[3] LI Jian-Jun, WANG Xiao-ling, YANG Yu and FU Jia. Emergency Task Assignment Method Based on CQPSO Mobile Crowd Sensing [J]. Computer Science, 2020, 47(6A): 273-277.
[4] ZHOU Xin-yue, QIAN Li-ping, HUANG Yu-pin, WU Yuan. Optimization Method of Electric Vehicles Charging Scheduling Based on Ant Colony [J]. Computer Science, 2020, 47(11): 280-285.
[5] LIU Dan. Fog Computing and Self-assessment Based Clustering and Cooperative Perception for VANET [J]. Computer Science, 2020, 47(10): 55-62.
[6] 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.
[7] ZHENG Fei-feng, JIANG Juan, MEI Qi-huang. Study on Stowage Optimization in Minimum Container Transportation Cost [J]. Computer Science, 2019, 46(6): 239-245.
[8] ZHENG Xiang-ping, YU Zhi-yong, WEN Guang-bin. Community Discovery in Location Network [J]. Computer Science, 2018, 45(6): 46-50.
[9] ZHOU Jie, YU Zhi-yong, GUO Wen-zhong, GUO Long-kun and ZHU Wei-ping. Participant Selection Algorithm for t-Sweep k-Coverage Crowd Sensing Tasks [J]. Computer Science, 2018, 45(2): 157-164.
[10] ZENG Zi-yi, QIU Han, ZHU Jun-hu, ZHOU Tian-yang. Network Security Experiment Environment Multi-emulation Planning Based on Capability Measurement [J]. Computer Science, 2018, 45(11): 160-163.
[11] JIA Xin and ZHANG Shao-ping. Research on Wear Leveling Algorithm of NAND FLASH Memory Based on Greedy Strategy [J]. Computer Science, 2017, 44(Z11): 312-316.
[12] LIU Wei, ZHAO Yu and CHEN Rui. Zero-One Integer Programming Based Optimization Model and Two-phase Resource Optimization Algorithm for Wireless Ad hoc Networks [J]. Computer Science, 2017, 44(1): 103-108.
[13] HE Xin, LIU Tian-xu, DING Shuang and BAI Lin. Optimization Selection Mechanism for Service Nodes in Hybrid Crowd Sensing [J]. Computer Science, 2017, 44(1): 113-116.
[14] YANG Bei, ZHOU Lan-jiang, YU Zheng-tao and LIU Li-jia. Research on Semi-supervised Learning Based Approach for Lao Part of Speech Tagging [J]. Computer Science, 2016, 43(9): 103-106.
[15] 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.
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.