A Survey of Task Allocation: Contrastive Perspectives From Wireless Sensor Networks and Mobile Crowdsensing

被引:41
作者
Guo, Wenzhong [1 ,2 ,3 ]
Zhu, Weiping [1 ]
Yu, Zhiyong [1 ,2 ,3 ]
Wang, Jiangtao [4 ]
Guo, Bin [5 ]
机构
[1] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Fujian, Peoples R China
[2] Minist Educ, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350003, Fujian, Peoples R China
[3] Fujian Prov Key Lab Networking Comp & Intelligent, Fuzhou 350116, Fujian, Peoples R China
[4] Peking Univ, Sch Elect & Comp Sci, Beijing 100871, Peoples R China
[5] Northwestern Polytech Univ, Sch Comp Sci, Xian 710129, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Mobile crowdsensing (MCS); task allocation; wireless sensor networks (WSNs); LIFETIME MAXIMIZATION; PARTICIPANT SELECTION; TARGET COVERAGE; ASSIGNMENT; FRAMEWORK; PRIVACY;
D O I
10.1109/ACCESS.2019.2896226
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Wireless sensor networks (WSNs) and mobile crowdsensing (MCS) are two important paradigms in urban dynamic sensing. In both sensing paradigms, task allocation is a significant problem, which may affect the completion quality of sensing tasks. In this paper, we give a survey of task allocation in WSNs and MCS from the contrastive perspectives in terms of data quality and sensing cost, which help to better understand related objectives and strategies. We first analyze the different characteristics of two sensing paradigms, which may lead to difference in task allocation issues or strategies. Then, we present some common issues in task allocation with objectives in data quality and sensing cost. Furthermore, we provide reviews of unique task allocation issues in MCS according to its new characteristics. Finally, we identify some potential opportunities for the future research.
引用
收藏
页码:78406 / 78420
页数:15
相关论文
共 98 条
[11]   Towards Energy-Efficient Wireless Networking in the Big Data Era: A Survey [J].
Cao, Xianghui ;
Liu, Lu ;
Cheng, Yu ;
Shen, Xuemin .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2018, 20 (01) :303-332
[12]  
Capponi A., 2018, P IEEE 19 INT S WORL, P14, DOI [10.1109/WoWMoM.2018.8449764, DOI 10.1109/WOWMOM.2018.8449764]
[13]   On Energy-Efficient Trap Coverage in Wireless Sensor Networks [J].
Chen, Jiming ;
Li, Junkun ;
He, Shibo ;
He, Tian ;
Gu, Yu ;
Sun, Youxian .
ACM TRANSACTIONS ON SENSOR NETWORKS, 2013, 10 (01)
[14]   Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing [J].
Cheng, Peng ;
Lian, Xiang ;
Chen, Lei ;
Han, Jinsong ;
Zhao, Jizhong .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2016, 28 (08) :2201-2215
[15]  
Chou CM, 2012, 2012 12TH INTERNATIONAL CONFERENCE ON ITS TELECOMMUNICATIONS (ITST-2012), P573
[16]  
Duan LJ, 2012, IEEE INFOCOM SER, P1701, DOI 10.1109/INFCOM.2012.6195541
[17]  
Duckham M, 2005, LECT NOTES COMPUT SC, V3468, P152
[18]   A Survey on Security, Privacy, and Trust in Mobile Crowdsourcing [J].
Feng, Wei ;
Yan, Zheng ;
Zhang, Hengrun ;
Zeng, Kai ;
Xiao, Yu ;
Hou, Y. Thomas .
IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (04) :2971-2992
[19]  
Feng ZN, 2014, IEEE INFOCOM SER, P1231, DOI 10.1109/INFOCOM.2014.6848055
[20]   Task Allocation in Spatial Crowdsourcing: Current State and Future Directions [J].
Guo, Bin ;
Liu, Yan ;
Wang, Leye ;
Li, Victor O. K. ;
Lam, Jacqueline C. K. ;
Yu, Zhiwen .
IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (03) :1749-1764