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

被引：40

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

来源：

IEEE ACCESS | 2019年 / 7卷

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

页码：78406 / 78420

页数：15

共 98 条

[31] Steered Crowdsensing: Incentive Design towards Quality-Oriented Place-Centric Crowdsensing [J].

Kawajiri, Ryoma ;

Shimosaka, Masamichi ;

Kashima, Hisashi .

UBICOMP'14: PROCEEDINGS OF THE 2014 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING, 2014, :691-701

[32]

Khalili MM, 2015, IEEE CONF COMPUT, P498, DOI 10.1109/INFCOMW.2015.7179434

[33]

Kido H., 2005, Protection of Location Privacy using Dummies for Location-based Services, P1248

[34] Maximum Lifetime Combined Barrier-Coverage of Weak Static Sensors and Strong Mobile Sensors [J].

Kim, Donghyun ;

Wang, Wei ;

Son, Junggab ;

Wu, Weili ;

Lee, Wonjun ;

Tokuta, Alade O. .

IEEE TRANSACTIONS ON MOBILE COMPUTING, 2017, 16 (07) :1956-1966

[35] An Incentive-Based Framework for Vehicle-Based Mobile Sensing [J].

Lan, Kun-chan ;

Chou, Chien-Ming ;

Wang, Han-Yi .

ANT 2012 AND MOBIWIS 2012, 2012, 10 :1152-1157

[36]

Lane N.D., 2013, P 11 ACM C EMBEDDED, P7

[37] Low-Power 2.4 GHz Wake-Up Radio for Wireless Sensor Networks [J].

Le-Huy, Philippe ;

Roy, Sebastien .

2008 4TH IEEE INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB), 2008, :13-18

[38] Dynamic Participant Recruitment of Mobile Crowd Sensing for Heterogeneous Sensing Tasks [J].

Li, Hanshang ;

Li, Ting ;

Wang, Yu .

2015 IEEE 12TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SENSOR SYSTEMS (MASS), 2015, :136-144

[39] Energy-Aware Participant Selection for Smartphone-Enabled Mobile Crowd Sensing [J].

Liu, Chi Harold ;

Zhang, Bo ;

Su, Xin ;

Ma, Jian ;

Wang, Wendong ;

Leung, Kin K. .

IEEE SYSTEMS JOURNAL, 2017, 11 (03) :1435-1446

[40] FooDNet: Toward an Optimized Food Delivery Network Based on Spatial Crowdsourcing [J].

Liu, Yan ;

Guo, Bin ;

Chen, Chao ;

Du, He ;

Yu, Zhiwen ;

Zhang, Daqing ;

Ma, Huadong .

IEEE TRANSACTIONS ON MOBILE COMPUTING, 2019, 18 (06) :1288-1301

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