A Context-Aware Incentive Mechanism for Mobile Crowd Sensing to Increase Participation of Users

Document Type : Original Article

Authors

Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

Abstract

Mobile crowd sensing is a new paradigm that uses the data sensed by mobile users to extract population data and then provides people-centered services. However, most users do not tend to participate voluntarily in this process. Therefore, availability of incentive mechanisms is mandatory to encourage users’ participation. In this paper, based on users-submitted data, a score-based incentive mechanism is proposed. This mechanism measures data quality via obtained contextual information from user data, and accordingly, gives score to the user. The fuzzy inference system is used in the center of the incentive mechanism to calculate the score. The proposed mechanism is evaluated via simulation and the effect of each parameter on the score acquired by user is shown. Simulation results show that this mechanism could be helpful in solving the problem of price imbalance in different areas of measurement and also is able to collect high-quality data at each measurement point with few number of participants.

Keywords

References

[1]      M. Gustarini, K. Wac, and A. K. Dey, “Anonymous smartphone data collection: factors influencing the users' acceptance in mobile crowd sensing,” Personal and Ubiquitous Computing, vol. 20, no. 1, pp. 65-82, 2016.
[2]      X. Zhang, Z. Yang, W. Sun, Y. Liu, S. Tang, K. Xing, et al., “Incentives for mobile crowd sensing: A survey,” IEEE Communications Surveys & Tutorials, vol. 18, no.1, pp. 54-67, 2016.
[3]      R. K. Ganti, F. Ye, and H. Lei, “Mobile crowdsensing: current state and future challenges,” Communications Magazine, vol. 49, no. 11, pp. 32-39, 2011.
[4]      سیمین قاسمی فلاورجانی, محمدعلی نعمت بخش و بهروز شاهقلی قهفرخی, «تخصیص وظایف چندهدفه در واگذاری به ابر سیار», مجله مهندسی برق دانشگاه تبریز, جلد 46، شماره 4، صفحات 217-232، 1395.
[5]      M. Riahi, R. Rahman, and K. Aberer, Privacy, Trust and Incentives in Participatory Sensing, Participatory Sensing, Opinions and Collective Awareness, Springer, 2017.
[6]      X. Li and Q. Zhu, "Social Incentive Mechanism Based Multi-User Sensing Time Optimization in Co-Operative Spectrum Sensing with Mobile Crowd Sensing," Sensors, vol. 18,no.1, p. 250, 2018.
[7]      Z. Shaolin, N. Abrar, and A. Iqbal, “Incentive model design for participatory sensing: technologies and challenges,” International Conference on Networking Systems and Security, Bangladesh, pp. 1-6, 2015.
[8]      S. Reddy, D. Estrin, M. Hansen, and M. Srivastava, “Examining micro-payments for participatory sensing data collections,” International conference on Ubiquitous computing, Denmark, pp. 33-36, 2010.
[9]      J.-S. Lee and B. Hoh, “Sell your experiences: a market mechanism based incentive for participatory sensing,” International Conference on Pervasive Computing and Communications, Germany, pp. 60-68, 2010.
[10]      J.-S. Lee and B. Hoh, “Dynamic pricing incentive for participatory sensing,” Pervasive and Mobile Computing, vol. 6, no. 16, pp. 693-708, 2010.
[11]      L. Deng and L. P. Cox, “Livecompare: grocery bargain hunting through participatory sensing,” workshop on Mobile Computing Systems and Applications, California, pp. 4-10, 2009.
[12]      D. Mendez, A. J. Perez, M. Labrador, and J. J. Marron, “P-sense: A participatory sensing system for air pollution monitoring and control,” International Conference on Pervasive Computing and Communications Workshops, USA, pp. 344-347, 2011.
[13]      S. Anawar and S. Yahya, “Empowering health behaviour intervention through computational approach for intrinsic incentives in participatory sensing application,” International Conference on Research and Innovation in Information Systems, Malaysia, pp. 281-285, 2013.
[14]      T. Dimitriou and I. Krontiris, “Privacy-Respecting auctions as incentive mechanisms in mobile crowd sensing,” International Conference on Information Security theory and Practice, ,Greece, pp. 20-35, 2015.
[15]      J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing; A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1997.
[16]      فرناز صباحی و محمدرضا اکبرزاده توتونچی, «شناسایی سیستم‌های غیرخطی بر اساس منطق فازی توسعه یافته»، مجله مهندسی برق دانشگاه تبریز, جلد 44، شماره 1، صفحات 23-32، 1392.
[17]      H. Gao, C. H. Liu, W. Wang, J. Zhao, Z. Song, X. Su, et al., “A survey of incentive mechanisms for participatory sensing,” IEEE Communications Surveys & Tutorials, vol. 17, no. 2,  pp. 918-943, 2015.
[18]      T. Luo, S. S. Kanhere, J. Huang, S. K. Das, and F. Wu, "Sustainable incentives for mobile crowdsensing: Auctions, lotteries, and trust and reputation systems," IEEE Communications Magazine,vol. 55, no.3, pp. 68-74, 2017.
[19]      I. Schweizer, C. Meurisch, J. Gedeon, R. Bärtl, and M. Mühlhäuser, “Noisemap: multi-tier incentive mechanisms for participative urban sensing,” Third International Workshop on Sensing Applications on Mobile Phones,Canada, pp. 9-14, 2012.
[20]      L. G. Jaimes, I. Vergara-Laurens, and A. Chakeri, “SPREAD, a crowd sensing incentive mechanism to acquire better representative samples,” International Conference on Pervasive Computing and Communications Workshops,Hungary, pp. 92-97, 2014.
[21]      B. O. Holzbauer, B. K. Szymanski, and E. Bulut, “Socially-aware market mechanism for participatory sensing,” International workshop on Mission-oriented wireless sensor networking, Turkey, pp. 9-14, 2012.
[22]      I. Krontiris and A. Albers, “Monetary incentives in participatory sensing using multi-attributive auctions,” International Journal of Parallel, Emergent and Distributed Systems,vol. 27, no. 4, pp. 317-336, 2012.
[23]      M. Shin, C. Cornelius, D. Peebles, A. Kapadia, D. Kotz, and N. Triandopoulos, “AnonySense: A system for anonymous opportunistic sensing,” Pervasive and Mobile Computing, vol. 7, no.1,pp. 16-30, 2011.
[24]      T. Das, P. Mohan, V. N. Padmanabhan, R. Ramjee, and A. Sharma, “PRISM: platform for remote sensing using smartphones,” International conference on Mobile systems, applications, and services, USA, pp. 63-76, 2010.
[25]      A. K. Dey, “Understanding and using context,” Personal and ubiquitous computing, vol. 5, no.1, pp. 4-7, 2001.
[26]      H. Vahdat-Nejad, Context-Aware Middleware: A Review, Context in Computing, Springer, 2014.
[27]      T. Tsujimori, N. Thepvilojanapong, Y. Ohta, Y. Zhao, and Y. Tobe, “History-based incentive for crowd sensing,” International Workshop on Web Intelligence and Smart Sensing, France, pp. 1-6, 2014.
[28]      H. Amintoosi and S. S. Kanhere, “A reputation framework for social participatory sensing systems,” Mobile Networks and Applications,vol. 19, no.1, pp. 88-100, 2014.
[29]      S. Chabridon, R. Laborde, T. Desprats, A. Oglaza, P. Marie, and S. M. Marquez, “A survey on addressing privacy together with quality of context for context management in the internet of things,” annals of telecommunications-annales des télécommunications, vol. 69, no.1, pp. 47-62, 2014.
[30]      A. Manzoor, H.-L. Truong, and S. Dustdar, “On the evaluation of quality of context,” European Conference on Smart Sensing and Context, Switzerland, pp. 140-153, 2008.
[31]      S. Dustdar and A. Manzoor, Quality of Context in Pervasive Systems: Models, Techniques, and Applications, PhD thesis, School of Computer Science, Wien TU, 2010.
[32]      W. Van Leekwijck and E. E. Kerre, “Defuzzification: criteria and classification,” Fuzzy sets and systems, vol. 108, no. 2, pp. 159-178, 1999.
[33]      L. G. Jaimes, I. J. Vergara-Laurens, and A. Raij, “A survey of incentive techniques for mobile crowd sensing,” IEEE Internet of Things Journal,vol. 2, no. 5,  pp. 370-380, 2015.
TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Volume 49, Issue 4 - Serial Number 90
February 2020
Pages 1451-1461

Files

Share

How to cite

Statistics