Survey of Crowdsensing: Architecture, Classification and Security Challenges

International Journal of Electronics and Communication Engineering
© 2019 by SSRG - IJECE Journal
Volume 6 Issue 10
Year of Publication : 2019
Authors : Abdul Razaque
pdf
How to Cite?

Abdul Razaque, "Survey of Crowdsensing: Architecture, Classification and Security Challenges," SSRG International Journal of Electronics and Communication Engineering, vol. 6,  no. 10, pp. 1-15, 2019. Crossref, https://doi.org/10.14445/23488549/IJECE-V6I10P101

Abstract:

With the rapid popularization of wireless mobile devices, mobile sensing and crowdsourcing are combined into a new technology called crowdsensing. Crowdsensing is an emerging technology that is used within a large group of applications having mobile devices equipped with embedded sensors. The researcher/industry still has a long way to fully investigate and show the big benefit of using crowdsensing in many life-critical applications. For example, using crowdsensing, a huge amount of data can be used for analysis when it is collectively extracted from different parts of the cloud. However, when developing crowdsensing techniques, a series of challenges and security problems have come to exist. This survey paper focuses on the challenges encountered when developing crowdsensing techniques for several applications. Mainly, the paper assesses the structure and basic classifications of crowdsensing, including platform, incentive mechanism, and typical applications and its applications and security issues of crowdsensing.

Keywords:

Crowdsensing architecture; Crowdsensing classifications, Crowdsensing security Challenges.

References:

[1] An Jian, GUI Xiao-lin, He Chang-qi, Wu Ruo-biao.―Crowdsourcing Assignment Mechanism Based on AHP in Mobile Sensing‖ Journal of Beijing University of Posts and Telecommunications. 2015, 38(5): 37-41
[2] Kazemi, Leyla, and C. Shahabi. "GeoCrowd:enabling query answering with spatial crowdsourcing." International Conference on Advances in Geographic Information Syst. ACM, 2012:189-198.
[3] Yan Fengya, ―Research and Realization of Crowdsensing Technology Based on Crowdsourcing‖, Journal of National University of Defense Science and technology 2014
[4] Gao Mengchao, Hu Qingbao, Cheng Yaodong, et al.―Design and Implementation of Crowdsourcing-based Social Network Data Collection Model‖. Computer Engineering, 2015, 41(4); 36-40
[5] Nan Wenqian, Guo Bin, Chen Huihui, Yu Zhiwen, et al. ―A Crowd-Space, Muti-Interaction-Based Dynamic Incentive Mechanism for Mobile Crowd Sensing‖ Chinese Journal of computers 2015,38(12): 2413-2423
[6] Qiu, C.; Mutka, M. W. "AirLoc: Mobile Robots Assisted Indoor Localization". 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor
Systems(MASS): 407–415. doi:10.1109/MASS.2015.10.ISBN 978-1-4673-9101-6.
[7] Qiu, C.; Mutka, M. W. (2016). "iFrame: Dynamic indoor map construction through automatic mobile sensing". IEEE International Conference on Pervasive Computing and Communications.
[8] P. Bahl and V. N. Padmanabhan, ―RADAR: an in-building RF-based user location and tracking system,‖ Proceedings of 19th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM ’00), vol. 2, pp. 775–784, Tel Aviv.Israel, March 2000.
[9] Chen and H. Kobayashi, ―Signal strength based indoor geolocation,‖ Proceedings of the IEEE International Conference on Communications (ICC ’02), vol. 1, pp. 436–439, New York, NY, USA, April–May 2002.
[10] Youssef, M. A.; Agrawala, A.; Shankar, A. Udaya. "WLAN location determination via clustering and probability distributions". Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003): 143–150.doi:10.1109/PERCOM.2003.1192736.
[11] Wu Y, Zheng JR, Peng H, Chen H, Li CP. ―Survey on incentive mechanisms for crowd sensing‖ Ruan Jian Xue Bao, 2016, 27(8):2025-2047
[12] Yang, Dejun, et al. "Incentive mechanisms for crowdsensing: crowdsourcing with smartphones." IEEE/ACM Transactions on Networking 24.3(2016):1732-1744.
[13] Lin, Jian, et al. "BidGuard: A framework for privacy-preserving crowdsensing incentive mechanisms." Communications and Network Security IEEE,
2017:145-153.
[14] Wen, Yutian, et al. "Quality-Driven Auction-Based Incentive Mechanism for Mobile Crowd Sensing." IEEE Transactions on Vehicular Technology
64.9(2015):4203-4214.
[15] Wang, Jing, et al. "Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing." IEEE, International Conference on Distributed Computing Systems IEEE, 2016:354-363.
[16] Xu, Jia, J. Xiang, and D. Yang. "Incentive Mechanisms for Time Window Dependent Tasks in Mobile Crowdsensing." IEEE Transactions on Wireless Communications 14.11(2015):6353-6364.
[17] Nan, Wenqian, et al. "A Cross-Space, Multi-interaction-Based Dynamic Incentive Mechanism for Mobile Crowd Sensing." Chinese Journal of Computers (2015):179-186.
[18] Sun Yong, Nakata K, ―An agent-based architecture for participatory sensing platform‖. Proc of the 4th International Universal Communication Symposium. 2010:392 – 400.
[19] Niwat T, Shinichi K, Yoshito T, ―Opportunistic collaboration in participatory sensing environments‖. Proc of 5th ACM International Workshop on Mobility in the Evolving Internet Architecture. New York: ACM Press, 2010:39-44.
[20] Cui Qianqian. ―An Intelligent Traffic Guidance Data Model and Scheme Based on Crowdsourcing Map‖. Journal of Beijing University of Posts and
Telecommunications, 2015.
[21] ZigBee Cluster Library Specification, ZigBee Alliance (May 31, 2012)
[22] Shuo, S., Hao, S., Yang, S. ―Design of An Experimental Indoor Position System Based on RSSI‖. Proceedings of the 2nd International Conference on Information Science and Engineering, ICISE 2010, Hangzhou, China, December 4-6, pp. 1989–1992 (2010)
[23] Lau, E.-E.-L., Lee, B.-G., Lee, S.-C. ―Enhanced RSSI-based High Accuracy Real-Time User Location Tracking System for Indoor and Outdoor Environments‖. International Journal on Smart Sensing and Intelligent Systems 1(2), 534–548 (2008)
[24] Zhang, Z., Wan, G., Jiang, M., Yang, G. ―Research of An Adjacent Correction Positioning Algorithm Based on RSSI-Distance Measurement‖. Proceedings of the Eighth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Shanghai, China, July 26-28, vol. 4, pp. 2319–2323 (2011)
[25] Barsocchi, P., Lenzi, S., Chessa, S., Giunta, G. ―A Novel Approach to Indoor RSSI Localization by Automatic Calibration of the Wireless Propagation Model‖. IEEE 69th Vehicular Technology Conference, VTC 2009, Barcelona, Spain, April 26-29 (2009)
[26] Barsocchi, P., Lenzi, S., Chessa, S., Giunta, G. ―Virtual Calibration for RSSI-based Indoor Localization with IEEE 802.15.4‖. IEEE International Conference on Communications, ICC 2009, Dresden, Germany, June 14-18 (2009)
[27] Borrelli, A., Monti, C., Vari, M., Mazzenga, F. ―Channel Models for IEEE 802.11b Indoor System Design‖. Proceedings of the IEEE International Conference on Communications, ICC 2004, Paris, France, June 20-24, vol. 6, pp. 3701–3705 (2004)
[28] Green, E., Hata, M. ―Microcellular Propagation Measurements in An Urban Environment‖. Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 1991, pp. 324–328 (1991)Google Scholar
[29] Ren, Z., Huang, Y., Chen, Q., Li, H. ―Modeling and Simulation of Fading, Pathloss, and Shadowing in Wireless Networks‖. Proceedings of the IEEE International Conference on Communications Technology and Application, ICCTA 2009, Alexandria, Egypt, October 17-19, pp. 335–343 (2009)
[30] Mehra, R., Singh, A. ―Real TIme RSSI Error Reduction in Distance Estimation Using RLS Algorithm‖. Proceedings of the IEEE 3rd International Advance Computing Conference, IACC 2013, Ghaziabad, India, February 22-23, pp. 661–665 (2013)
[31] Li Jinglin, Yuan Quan, Yang Fangchun, ―Crowd Sensing and Service in Internet of Vehicles‖, ZTE Technical Journal, 2015
[32] Sensorly. https://www.sensorly.com/
[33] Eriksson, Jakob, et al. "The pothole patrol:using a mobile sensor network for road surface monitoring." International Conference on Mobile Systems, Applications, and Services DBLP, 2008:29-39.
[34] Mohan, Prashanth, V. N. Padmanabhan, and R. Ramjee. "Nericell: using mobile smartphones for rich monitoring of road and traffic conditions." Proc of AcmSensys (2008):357-358.
[35] Mathur, Suhas, et al. "ParkNet:drive-by sensing of road-side parking statistics." International Conference on Mobile Systems, Applications, and Services 2010:123-136.
[36] Eisenman, S. B., et al. "The BikeNet mobile sensing system for cyclist experience mapping." ACM Conference on Embedded Networked Sensor Systems 2007:87-101.
[37] Hyman, Josh, et al. "Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype." The Workshop on Embedded Networked Sensors ACM, 2007:13-17.
[38] Hull B, Bychkovsky V, Zhang Y.et al. ―CarTel: a distributed mobile sensor computing system‖. ACM SenSys, 2006: 125-138
[39] Thiagarajan A, Ravindranath L, LaCurts K.et al. ―VTrack: accurate, energy-aware road traffic delay estimation using mobile phones‖ ACM SenSys. 2009: 85-98
[40] Koukoumidis E, Peh L-S, Martonosi M R.SignalGuru. ―leveraging mobile phones for collaborative traffic signal schedule advisory ‖ ACM MobiSys, 2011: 127-140
[41] GantiR,PhamN,Ahmadi H.et al. ―GreenGPS: A participatory sensing fuel-efficient maps application‖ ACM MobiSys, 2010: 151-164
[42] Dutta, Prabal, et al. "Common Sense:participatory urban sensing using a network of handheld air quality monitors." International Conference on Embedded Networked Sensor Systems, SENSYS 2009, Berkeley, California, Usa, November DBLP, 2009:349-350.
[43] Stevens, Matthias, and E. D. '. Hondt. "Crowdsourcing of Pollution Data using Smartphones." Crowdsourcing of Pollution Data Using Smartphones (2010).
[44] Rana, Rajib Kumar, et al. "Ear-phone:an end-to-end participatory urban noise mapping system." ACM/IEEE International Conference on Information Processing in Sensor Networks ACM, 2010:105-116.
[45] Kim, Sunyoung, et al. "Creek watch: pairing usefulness and usability for successful citizen science." Sigchi Conference on Human Factors in Computing Systems ACM, 2011:2125-2134.
[46] Weppner, Jens, and P. Lukowicz. "Bluetooth based collaborative crowd density estimation with mobile phones." IEEE International Conference on Pervasive Computing and Communications IEEE, 2013:193-200.
[47] Ra, Moo Ryong, et al. "Medusa:a programming framework for crowd-sensing applications." International Conference on Mobile Systems, Applications, and Services ACM, 2012:337-350.
[48] Simoens, Pieter, et al. "Scalable crowd-sourcing of video from mobile devices." Proceeding of the, International Conference on Mobile Systems, Applications, and Services 2013:139-152.
[49] WANG Zhengqi. ―Security challenges for the mobile crowdsensing network.‖ Science and Technology Review 33.24(2015):114-117.
[50] Ganti, Raghu K., F. Ye, and H. Lei. "Mobile crowdsensing: current state and future challenges." IEEE Communications Magazine 49.11(2011):32-39.
[51] Wang, Leye, et al. "Sparse mobile crowdsensing: challenges and opportunities." IEEE Communications Magazine 54.7(2016):161-167.
[52] Pournajaf, Layla, et al. "Participant Privacy in Mobile Crowd Sensing Task Management: A Survey of Methods and Challenges." AcmSigmod Record 44.4(2016):23-34.
[53] Guo, Bin, et al. "The Emergence of Visual Crowdsensing: Challenges and Opportunities." IEEE Communications Surveys & Tutorials PP.99 (2017):1-1.
[54] Song, Xintong, et al. "Holistic Reality Examination on Practical Challenges in a Mobile CrowdSensing Application." Global Communications Conference IEEE, 2017:1-6.
[55] Restuccia, Francesco, et al. "Quality of Information in Mobile Crowdsensing: Survey and Research Challenges." Acm Transactions on Sensor Networks 13.4(2017).
[56] Sun, Jiajun. "Incentive Mechanisms for Mobile Crowd Sensing: Current States and Challenges of Work." Computer Science 12.2(2013).
[57] Noureen, Javeria, and M. Asif. "Crowdsensing: Socio-Technical Challenges and Opportunities." International Journal of Advanced Computer Science & Applications 8.3(2017).
[58] Siegfried, Tobias. "Potential and Challenges of Low-Cost and High-Tech Crowd-sensing Approaches in Hydrometeorology for Better Water Resources
Management - Insights and Learnings from the Global iMoMo Initiative." EGU General Assembly Conference EGU General Assembly Conference Abstracts, 2016.
[59] Bellavista, P., et al. "Crowdsensing in smart cities: Technical challenges, open issues, and emerging solution guidelines." (2015).
[60] Gallacher, Sarah, et al. "Investigating the Challenges of Crowd Sensing: Lessons from Zurich." Workshop structures of Knowledge Co-Creation Between Organisations and the Public ACM, 2014.
[61] Zhao, Dong, and H. Ma. "Development and Challenges of Crowd Sensing Networks." Information & Communications Technologies (2014).
[62] He Hong, Xiang Chaocan, et al. ―Survey on Crowd-Sensing Networks‖ Journal of Jilin University 34(3) 2016: 374-383