Toward Collaborative Mobile Crowdsourcing

被引:18
作者
Hamrouni A. [1 ]
Alelyani T. [2 ]
Ghazzai H. [1 ]
Massoud Y. [1 ]
机构
[1] School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ
[2] College of Computer Science and Information Systems, Najran University, Najran
来源
IEEE Internet of Things Magazine | 2021年 / 4卷 / 02期
关键词
Internet of things;
D O I
10.1109/IOTM.0001.2000185
中图分类号
学科分类号
摘要
Mobile crowdsourcing (MC) is an effective way of engaging large groups of smart devices to perform tasks remotely while exploiting their built-in features. It has drawn great attention in the areas of smart cities and urban computing communities to provide decentralized, fast, and flexible ubiquitous technological services. The vast majority of previous studies focused on non-cooperative MC schemes in Internet of Things (IoT) systems. Advanced collaboration strategies are expected to leverage the capability of MC services and enable the execution of more complicated crowdsourcing tasks. In this context, Collaborative Mobile Crowdsourcing (CMC) enables task requesters to hire groups of IoT devices' users that must communicate with each other and coordinate their operational activities in order to accomplish complex tasks. In this paper, we present and discuss the novel CMC paradigm in IoT. Then, we provide a detailed taxonomy to classify the different components forming CMC systems. Afterwards, we investigate the challenges in designing CMC tasks and discuss different team formation strategies involving the crowdsourcing platform and selected team leaders. We also analyze and compare the performances of certain proposed CMC recruitment algorithms. Finally, we shed light on open research directions to leverage CMC service design. © 2018 IEEE.
引用
收藏
页码:88 / 94
页数:6
相关论文
共 15 条
[1]  
Zhou Z., Et al., Robust mobile crowd sensing: When deep learning meets edge computing, IEEE Network, 32, 4, pp. 54-60, (2018)
[2]  
Chen M., Et al., Urban healthcare big data system based on crowdsourced and cloud-based air quality indicators, IEEE Commun. Mag., 56, 11, pp. 14-20, (2018)
[3]  
Pradhan M., Et al., Leveraging crowdsourcing and crowdsensing data for HADR operations in a smart city environment, IEEE Internet of Things Mag., 2, 2, pp. 26-31, (2019)
[4]  
Ruiz-Correa S., Et al., Sensecityvity: Mobile crowdsourcing, urban awareness, and collective action in Mexico, IEEE Pervasive Computing, 16, 2, pp. 44-53, (2017)
[5]  
Jiang H., Et al., Fly-navi: A novel indoor navigation system with on-The-fly map generation, IEEE Trans. Mobile Computing, pp. 1-1, (2020)
[6]  
Hamrouni A., Et al., A spatial mobile crowdsourcing framework for event reporting, IEEE Trans. Computational Social Systems, 7, 2, pp. 477-491, (2020)
[7]  
Catherwood P.A., Et al., A community-based IoT personalized wireless healthcare solution trial, IEEE J. Translational Engineering in Health and Medicine, 6, pp. 1-13, (2018)
[8]  
Awaisi K.S., Et al., Leveraging IoT and fog computing in healthcare systems, IEEE Internet of Things Mag., 3, 2, pp. 52-56, (2020)
[9]  
Roy C., Misra S., Pal S., Blockchain-enabled safety-as-A-service for industrial IoT applications, IEEE Internet of Things Mag., 3, 2, pp. 19-23, (2020)
[10]  
Jain A., Et al., Understanding workers, developing effective tasks, and enhancing marketplace dynamics: A study of a large crowdsourcing marketplace, Proc. VLDB Endow., 10, 7, pp. 829-840, (2017)