Abstract
Participatory sensing is a promising sensing paradigm that enables collection, processing, dissemination and analysis of the phenomena of interest by ordinary citizens through their handheld sensing devices. Participatory sensing has huge potential in many applications, such as smart transportation and air quality monitoring. However, participants may submit low-quality, misleading, inaccurate, or even malicious data if a participatory sensing campaign is not launched effectively. Therefore, it has become a significant issue to establish an efficient participatory sensing campaign for improving the data quality. This article proposes a novel five-tier framework of participatory sensing and addresses several technical challenges in this proposed framework including: (1) optimized deployment of data collection points (DC-points); and (2) efficient recruitment strategy of participants. Toward this end, the deployment of DC-points is formulated as an optimization problem with maximum utilization of sensor and then a Wise-Dynamic DC-points Deployment (WD3) algorithm is designed for high-quality sensing. Furthermore, to guarantee the reliable sensing data collection and communication, a trajectory-based strategy for participant recruitment is proposed to enable campaign organizers to identify well-suited participants for data sensing based on a joint consideration of temporal availability, trust, and energy. Extensive experiments and performance analysis of the proposed framework and associated algorithms are conducted. The results demonstrate that the proposed algorithm can achieve a good sensing coverage with a smaller number of DC-points, and the participants that are termed as social sensors are easily selected, to evaluate the feasibility and extensibility of the proposed recruitment strategies.
- Hossein Ahmadi, Nam Pham, Raghu K. Ganti, Tarek F. Abdelzaher, Suman Nath, and Jiawei Han. 2010. Privacy-aware regression modeling of participatory sensing data. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems. ACM, New York, 99--112. Google Scholar
Digital Library
- Haleh Amintoosi and Salil S. Kanhere. 2014. A reputation framework for social participatory sensing systems. Mobile Netw. Appl. 19, 1, 88--100. Google Scholar
Digital Library
- Haleh Amintoosi and Salil S. Kanhere. 2013. A trust-based recruitment framework for multi-hop social participatory sensing. In Proceedings of the 9th IEEE International Conference on Distributed Computing in Sensor Systems. IEEE, 266--273. Google Scholar
Digital Library
- Habib M. Ammari and Sajal K. Das. 2012. Centralized and clustered k-coverage protocols for wireless sensor networks. IEEE Trans. Comput. 61, 1, 118--133. Google Scholar
Digital Library
- Yi F. Dong, S. Kanhere, Chun Tung Chou, and Nirupama Bulusu. 2008. Automatic collection of fuel prices from a network of mobile cameras. In Proceedings of the 4th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS'08). Springer-Verlag, Berlin, Heidelberg, 140--156. Google Scholar
Digital Library
- Shane B. Eisenman, Emiliano Miluzzo, Nicholas D. Lane, Ronald A. Peterson, Gahng-Seop Ahn, and Andrew T. Campbell. 2007. The BikeNet mobile sensing system for cyclist experience mapping. In Proceedings of the 5th International Conference on Embedded Networked Sensor Systems (SenSys'07). ACM, New York, 87--101. Google Scholar
Digital Library
- Shravan Gaonkar, Jack Li, Romit Roy Choudhury, Landon Cox, and Al Schmidt. 2008. Micro-Blog: Sharing and querying content through mobile phones and social participation. In Proceedings of the 6th International Conference on Mobile Systems, Applications, and Services. ACM, New York, 174--186. Google Scholar
Digital Library
- Fei Hao, Mingjie Jiao, Geyong Min, and Laurence T. Yang. 2014. A trajectory-based recruitment strategy of social sensors for participatory sensing. IEEE Commun. Mag. 52, 12, 41--47.Google Scholar
Cross Ref
- Antonio J. Jara, Miguel A. Zamora, and Antonio F. G. Skarmeta. 2010. An architecture based on internet of things to support mobility and security in medical environments. In Proceedings of the 7th IEEE Conference on Consumer Communications and Networking Conference. IEEE, 1--5. Google Scholar
Digital Library
- Salil S. Kanhere. 2011. Participatory sensing: Crowdsourcing data from mobile smartphones in urban spaces. In Proceeding of the 12th International Conference on Mobile Data Management. IEEE, 3--6. Google Scholar
Digital Library
- Merkouris Karaliopoulos, Orestis Telelis, and Iordanis Koutsopoulos. 2015. User recruitment for mobile crowdsensing over opportunistic networks. In Proceedings of IEEE INFOCOM'15. IEEE.Google Scholar
Cross Ref
- Tamara G. Kolda and Brett W. Bader. 2009. Tensor decompositions and applications. SIAM Review 51, 3, 455--500. Google Scholar
Digital Library
- Ville Kotovirta, Timo Toivanen, Renne Tergujeff, and Markku Huttunen. 2012. Participatory sensing in environmental monitoring -- experiences. In Proceedings of International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. IEEE, Los Alamitos, CA, 155--162. Google Scholar
Digital Library
- Lei Li, Baoxian Zhang, and Jun Zheng. 2013. A study on one-dimensional phk-coverage problem in wireless sensor networks. Wireless Commun. Mobile Comput. 13, 1--11.Google Scholar
Cross Ref
- Wen-Hwa Liao, Ssu-Chi Kuai, and Mon-Shin Lin. 2015. An energy-efficient sensor deployment scheme for wireless sensor networks using ant colony optimization algorithm. Wireless Pers. Commun. 82, 4, 2135--2153. Google Scholar
Digital Library
- Man Lin, Yongwen Pan, Laurence T. Yang, Minyi Guo, and Nenggan Zheng. 2013. Scheduling co-design for reliability and energy in cyber-physical systems. IEEE Trans. Emerg. Topics Comput. 1, 2, 353--365.Google Scholar
Cross Ref
- Tie Luo, Hwee-Pink Tan, and Lirong Xia. 2014. Profit-maximizing incentive for participatory sensing. In Proceedings of the 33rd IEEE International Conference on Computer Communications (INFOCOM'14). IEEE, 127--135.Google Scholar
Cross Ref
- Antti P. Miettinen and Jukka K. Nurminen. 2010. Energy efficiency of mobile clients in cloud computing. In Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing. USENIX Association, 4--4. Google Scholar
Digital Library
- S. Mini, Siba K. Udgata, and Samrat L. Sabat. 2014. Sensor deployment and scheduling for target coverage problem in wireless sensor networks. IEEE Sensors J. 14, 3, 636--644.Google Scholar
Cross Ref
- Amjad Osmani, Mehdi Dehghan, H. Pourakbar, and Payam Emdadi. 2009. Fuzzy-based movement-assisted sensor deployment method in wireless sensor networks. In Proceedings of the 1st International Conference on Computational Intelligence, Communication Systems and Networks (CICSYN'09). IEEE Computer Society, Los Alimatos, CA, 90--95. Google Scholar
Digital Library
- Rajib Kumar Rana, Chun Tung Chou, Salil S. Kanhere, Nirupama Bulusu, and Wen Hu. 2010. Ear-phone: An end-to-end participatory urban noise mapping system. In Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN'10). ACM, New York, 105--116. Google Scholar
Digital Library
- Sasank Reddy, Deborah Estrin, and Mani Srivastava. 2010a. Recruitment framework for participatory sensing data collections. In Proceedings of the 8th International Conference on Pervasive Computing (Pervasive'10). Springer-Verlag, Berlin, Heidelberg, 138--155. Google Scholar
Digital Library
- Sasank Reddy, Katie Shilton, Gleb Denisov, Christian Cenizal, Deborah Estrin, and Mani Srivastava. 2010b. Biketastic: Sensing and mapping for better biking. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI'10). ACM, New York, 1817--1820. Google Scholar
Digital Library
- Jimeng Sun, Dacheng Tao, and Christos Faloutsos. 2006. Beyond streams and graphs: Dynamic tensor analysis. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06). ACM, New York, 374--383. Google Scholar
Digital Library
- Jimeng Sun, Dacheng Tao, Spiros Papadimitriou, Philip S. Yu, and Christos Faloutsos. 2008. Incremental tensor analysis: Theory and applications. ACM Trans. Knowl. Disc. Data 2, 3, 11:1--11:37. Google Scholar
Digital Library
- Guliz Seray Tuncay, Giacomo Benincasa, and Ahmed Helmy. 2012. Autonomous and distributed recruitment and data collection framework for opportunistic sensing. In Proceedings of the 18th Annual International Conference on Mobile Computing and Networking (Mobicom'12). ACM, New York, 407--410. Google Scholar
Digital Library
- Bang Wang. 2011. Coverage problems in sensor networks: A survey. ACM Comput. Surv. 43, 4, 32:1--32:53. Google Scholar
Digital Library
- Guiling Wang, Guohong Cao, and Thomas F. La Porta. 2006. Movement-assisted sensor deployment. IEEE Trans. Mobile Comput. 5, 6, 640--652. Google Scholar
Digital Library
- Xinlei Oscar Wang, Wei Cheng, Prasant Mohapatra, and Tarek F. Abdelzaher. 2013. ARTSense: Anonymous reputation and trust in participatory sensing. In Proceedings of the 32nd Annual IEEE International Conference on Computer Communications. IEEE, 2517--2525.Google Scholar
- Harald Weinschrott, Frank Durr, and Kurt Rothermel. 2010. StreamShaper: Coordination algorithms for participatory mobile urban sensing. In Proceeding of the 7th International Conference on Mobile Adhoc and Sensor Systems. IEEE, 195--204.Google Scholar
Cross Ref
- Ya Xu, John Heidemann, and Deborah Estrin. 2001. Geography-informed energy conservation for ad Hoc routing. In Proceedings of the 7th Annual International Conference on Mobile Computing and Networking (MobiCom'01). ACM, New York, 70--84. Google Scholar
Digital Library
- Liu Yang, Man Lin, and Laurence T. Yang. 2012. Multi-core fixed priority dvs scheduling. In Algorithms and Architectures for Parallel Processing, Springer, 517--530. Google Scholar
Digital Library
- Yu Zheng, Furui Liu, and Hsun-Ping Hsieh. 2013. U-Air: When urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'13). ACM, New York, 1436--1444. Google Scholar
Digital Library
- Yi Zou and Krishnendu Chakrabarty. 2004. Sensor deployment and target localization in distributed sensor networks. ACM Trans. Embed. Comput. Syst. 3, 1, 61--91. Google Scholar
Digital Library
Index Terms
Launching an Efficient Participatory Sensing Campaign: A Smart Mobile Device-Based Approach
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