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Mobile Crowd Sensing

The proliferation of smartphones with rich embedded sensors has led to the new revolutionary mobile crowd-sensing (MCS) paradigm, where individuals use their mobile devices to collectively sense and share information related to a certain phenomenon of interest. Due to the low deploying cost and high sensing coverage, MCS becomes increasingly popular for a wide range of applications in environment, infrastructure, and community monitoring, such as air quality monitoring, wireless signal strength measurement, traffic jam alerts, wireless indoor localization, and urban tomography. Meanwhile, the large scale deployment of MCS has encountered several key challenges, such as uneven and biased coverage, large variations of sensing quality, user concerns on privacy, and potential excessive consumption of user device resources. Resolving these challenges requires innovative technology solutions and algorithm designs, and in many cases, effective economic mechanisms that incentivize mobile users to actively engage in high quality data sensing and timely data reporting.

In this project, we plan to address the following four key questions on designing economic mechanisms for MCS: (1) How to cope with incomplete network information and encourage users to truthfully share their private information? (2) How to retain the most effective users in the system and maintain their long-tern participation incentives? (3) How to explore the network dynamics to make the data reporting more cost effective? (4) How to exploit the competition and cooperation among users to achieve good sensing results?

 

 

 
 
Project Team

NCEL Members: Man Hon Cheung, Lingjie Duan, Lin Gao, Yuan Luo, Fen Hou,  Jianwei Huang, and Changkun Jiang
Collaborators: Jean Walrand (UC Berkeley)

 
 
 
References
In Press
Cheung, Man Hon, Fen Hou, and Jianwei Huang. "Delay-Sensitive Mobile Crowdsensing: Algorithm Design and Economics." IEEE Transactions on Mobile Computing (In Press). Download: 08315500.pdf (385.83 KB)
2017
Cheung, Man Hon, Fen Hou, and Jianwei Huang Make a Difference: Diversity-Driven Social Mobile Crowdsensing. IEEE International Conference on Computer Communications (INFOCOM). Atlanta, USA, 2017. Download: Michael_INFOCOM17.pdf (202.37 KB)
Luo, Tie, et al. "Sustainable Incentives for Mobile Crowdsensing: Auctions, Lotteries, and Trust and Reputation Systems." IEEE Communications Magazine. 55.3 (2017): 68-74. Download: 07876960.pdf (862.82 KB)
2016
Jiang, Changkun, et al. Exploiting data reuse in mobile crowdsensing. IEEE Global Communications Conference (GLOBECOM). IEEE, 2016. Download: 07841828.pdf (238.91 KB)
2015
Cheung, Man Hon, et al. Distributed Time-Sensitive Task Selection in Mobile Crowdsensing. ACM Mobihoc. Hangzhou, China, 2015. Download: Mobihoc2015.pdf (343.2 KB)
Jiang, Changkun, et al. Economics of Peer-to-Peer Mobile Crowdsensing. IEEE Global Communications Conference (GLOBECOM). San Diego, CA, USA, 2015. Download: 07417152.pdf (207.37 KB)
Gao, Lin, Fen Hou, and Jianwei Huang Providing Long-Term Participation Incentive in Participatory Sensing. IEEE INFOCOM. Hong Kong, 2015. Download: p2803-gao.pdf (1.6 MB)
2014
Duan, Lingjie, et al. "Motivating Smartphone Collaboration in Data Acquisition and Distributed Computing." IEEE Transactions on Mobile Computing. 13.10 (2014): 2320-2333. Download: TMC_SmartPhoneCollabration.pdf (789.75 KB)
Cheung, Man Hon, Fen Hou, and Jianwei Huang Participation and Reporting in Multimedia Participatory Sensing. IEEE WiOpt. Hammamet, Tunisia, 2014. Download: partsense_final.pdf (265.49 KB)
2012

 

 

 

 

 



story | by Dr. Radut