Participatory sensing in Kenya :
Preconditions for successful implementation
DOI:
https://doi.org/10.58506/ajstss.v2i2.151Keywords:
Location based services, participatory sensing, technology adoption, preconditions, mobile sensing applications, mobile usersAbstract
An increasing number of participatory sensing applications have been developed in recent years. Since these applications can be used for personal and community levels to address real world problems, the players of location based services (LBS) are already exploring their environment. One approach could be to especially address these users’ necessary preconditions for successful implementation in order to increase the Kenyan user base of participatory sensing applications. To achieve this objective, a number of earlier related studies were reviewed with a view of identifying factors affecting successful implementations in Kenya for use in the study. To this end, we conduct a questionnaire-based study involving 100 participants to investigate the possible key preconditions necessary for successful implementation of LBS. In particular, we analyze the potential interests of our participants in sensing tasks based on their demographics and interaction with sensing applications. It results to proposed preconditions of successful implementation in Kenya. The identified preconditions are tested statistically using correlation and regression analysis. The findings based on Pearson correlation analysis (coefficients above 0.8) indicate the preconditions have strong linear relationship and recorded p-values of less than 0.05 meaning that their contribution is significant to successful implementation in Kenya.
References
Ahmadi, H., Pham, N., Ganti, R., Abdelzaher, T., Nath, S., & Han, J. (2010). Privacy-aware regression modeling of participatory sensing data. Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, 99–112. https://doi.org/10.1145/1869983.1869994
Amintoosi, H., Kanhere, S. S., & Allahbakhsh, M. (2015). Trust-based privacy-aware participant selection in social participatory sensing. Journal of Information Security and Applications, 20, 11–25. https://doi.org/10.1016/j.jisa.2014.10.003
Benisch, M., Gage, P., Sadeh, N., & Faith, L. (2011). Capturing location-privacy preferences : quantifying accuracy and user-burden tradeoffs, 679–694. https://doi.org/10.1007/s00779-010-0346-0
Bennett, V., Abdoun, T., Zeghal, M., Koelewijn, A., Barendse, M., & Dobry, R. (2011). Real-time monitoring system and advanced characterization technique for civil infrastructure health monitoring. Advances in Civil Engineering, 2011. https://doi.org/10.1155/2011/870383
Bhattacharjee, J., Pal, A., Mukhopadhyay, S., & Singh, V. K. (2015). Participatory Sensing System in Presence of Multiple Buyers. Procedia Computer Science, 54, 237–246. https://doi.org/10.1016/j.procs.2015.06.028
CAK. (2017). Second Quarter Sector Statistics Report for the Financial Year 2016 / 2017, 2017(December 2016), 1–31.
Cheng, L., Niu, J., Kong, L., Luo, C., Gu, Y., He, W., & Das, S. K. (2016). Compressive Sensing based Data Quality Improvement for Crowd-Sensing Applications. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2016.10.004
Chessa, S., Girolami, M., Foschini, L., Ianniello, R., Corradi, A., & Bellavista, P. (2016). Mobile crowd sensing management with the ParticipAct living lab. Pervasive and Mobile Computing. https://doi.org/10.1016/j.pmcj.2016.09.005
Christin, D. (2016). Privacy in mobile participatory sensing : Current trends and future challenges. The Journal of Systems & Software, 116, 57–68. https://doi.org/10.1016/j.jss.2015.03.067
Christin, D., Buchner, C., & Leibecke, N. (2013). What’s the value of your privacy? Exploring factors that influence privacy-sensitive contributions to participatory sensing applications. 38th Annual IEEE Conference on Local Computer Networks - Workshops, 918–923. https://doi.org/10.1109/LCNW.2013.6758532
Christin, D., Pons-Sorolla, D. R., Hollick, M., & Kanhere, S. S. (2014). TrustMeter: A trust assessment scheme for collaborative privacy mechanisms in participatory sensing applications. IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings, (April), 21–24. https://doi.org/10.1109/ISSNIP.2014.6827614
Christin, D., Reinhardt, A., Kanhere, S. S., & Hollick, M. (2011). A survey on privacy in mobile participatory sensing applications. Journal of Systems and Software, 84(11), 1928–1946. https://doi.org/10.1016/j.jss.2011.06.073
Clarke, A., & Steele, R. (2014). Local Processing to Achieve Anonymity in a Participatory Health e-Research System. Procedia - Social and Behavioral Sciences, 147, 284–292. https://doi.org/10.1016/j.sbspro.2014.07.172
Cook, D. J., & Das, S. K. (2012). Pervasive computing at scale : Transforming the state of the art. Pervasive and Mobile Computing, 8(1), 22–35. https://doi.org/10.1016/j.pmcj.2011.10.004
Dahunsi, F., & Dwolatzky, B. (2012). Towards the deployment and adoption of Location-based services for optimal mobile communication operations in Africa. The African Journal of Information Systems, 4(2). Retrieved from http://digitalcommons.kennesaw.edu/cgi/viewcontent.cgi?article=1068&context=ajis
Di, B,. Wang, T,. Song, L,. Han, Z. (2013). Incentive mechanism for collaborative smartphone sensing using overlapping coalition formation games. In Proc. IEEE Glob. Commun. Conf. GLOBECOM (pp. 1705–1710).
Geoffrey R. Marczyk, David DeMatteo, D. F. (2005). Essentials of Research Design and Methodology. 2005. New Jersey.: John Wiley & Sons.
Gisdakis, S., Giannetsos, T., & Papadimitratos, P. (2016). Security , Privacy , and Incentive Provision for Mobile Crowd Sensing Systems, 3(5), 839–853.
Gunasekaran, S., & Rathnamala, J. (2015). Review on Various Architectural Models in Mobile Crowdsensing, 4(2), 1412–1418.
Guo, B., Chen, H., Yu, Z., Nan, W., Xie, X., Zhang, D., & Zhou, X. (2016). TaskMe: Toward a Dynamic and Quality-Enhanced Incentive Mechanism for Mobile Crowd Sensing. International Journal of Human-Computer Studies. https://doi.org/10.1016/j.ijhcs.2016.09.002
Huang, K. L., Kanhere, S. S., & Hu, W. (2010). Preserving privacy in participatory sensing systems. Computer Communications, 33(11), 1266–1280. https://doi.org/10.1016/j.comcom.2009.08.012
Jaimes, L. G., Member, G. S., Vergara-laurens, I. J., & Raij, A. (2015). A Survey of Incentive Techniques for Mobile Crowd Sensing, 2(5), 370–380.
Jun, N., Chin, & Siau, K. L. (2012). Critical Success Factors of Location-Based Services by. Management Information Systems Commons, 27, 1–97. Retrieved from http://digitalcommons.unl.edu/businessdiss/27
Kalui, D. (2016). Trust Assurance Privacy Preserving Framework for Moving Objects in Participatory Sensing. In Proc. Int. IEEE Conference on Big Data Analysis, 246–251. https://doi.org/10.1109/ICBDA.2016.7509833
Kalui, D. M., Guo, X., Zhang, D., Xie, Y., & Yang, Z. (2015). Personalized privacy aware framework for moving objects in participatory sensing. Proceedings - 15th IEEE International Conference on Computer and Information Technology, CIT 2015, 14th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2015, 13th IEEE International Conference on Dependable, Autonomic and Se, 191–198. https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.28
Kazemi, L., & Shahabi, C. (2013). TAPAS: Trustworthy privacy-aware participatory sensing. Knowledge and Information Systems, 37(1), 105–128. https://doi.org/10.1007/s10115-012-0573-y
Kieyah, jd. THE IMPACT OF COMPETITION IN THE MOBILE VOICE MARKET ON TELECOMMUNICATIONS SECTOR AND MACRO ECONOMY (2012).
Koutsopoulos, I. (2013). Optimal incentive-driven design of participatory sensing systems. In Proc. IEEE INFOCOM (p. 1402–1410.).
Lee, J., & Hoh, B. (2010). Dynamic pricing incentive for participatory sensing. In Pervasive and Mobile Computing (Vol. 6, pp. 693–708). Elsevier B.V. https://doi.org/10.1016/j.pmcj.2010.08.006
Li, Q., & Cao, G. (2016). Providing Privacy-Aware Incentives in Mobile Sensing Systems, 15(6), 1485–1498.
Lin, J., Benisch, M., Sadeh, N., Niu, J., Hong, J., Lu, B., & Guo, S. (2012). A Comparative Study of Location-sharing Privacy Preferences in the U . S . and China, (60873241), 1–24.
Luo, T., Tan, H., & Xia, L. (2014). Profit-Maximizing Incentive for Participatory Sensing, 127–135.
Macias, E., Suarez, A., & Lloret, J. (2013). Mobile Sensing Systems. Sensors, 13(12), 17292–17321. https://doi.org/10.3390/s131217292
Manzoor, A., Patsakis, C., McCarthy, J., Mullarkey, G., Clarke, S., Cahill, V., & Bouroche, M. (2013). Data sensing and dissemination framework for smart cities. Proceedings - 2013 International Conference on MOBILe Wireless MiddleWARE, Operating Systems and Applications, Mobilware 2013, 156–165. https://doi.org/10.1109/Mobilware.2013.19
Marusic, S., Gubbi, J., Sullivan, H., Law, Y. E. E. W. E. I., & Palaniswami, M. (2014). Participatory Sensing, Privacy, and Trust Management for Interactive Local Government, (September).
Mucheru Joe, 25, A. 2017. (n.d.). Internet Security, standard newspaper. https://doi.org/Mucheru, Joe, 25 April, 2017. Https://www.standardmedia.co.ke/business/article/2001237611/
Niwa, J., Okada, K., Okuda, T., & Yamaguchi, S. (2013). MPSDataStore : A Sensor Data Repository System for Mobile Participatory Sensing Categories and Subject Descriptors, 3–8.
Pallat, J. (2003). A Step by Step Guide to Data Analysis Using SPSS, 229–240.
Pérez-torres, R., Torres-huitzil, C., & Galeana-zapién, H. (2016). Power management techniques in smartphone-based mobility sensing systems : A survey. Pervasive and Mobile Computing, 31, 1–21. https://doi.org/10.1016/j.pmcj.2016.01.010
Reddy, S., Estrin, D., Hansen, M., Srivastava, M. (2010). Examining micro-payments for participatory sensing data collections. In Proceedings of the 12 th ACM International Conference on Ubiquitous Computing.ACM (p. 33–36.).
Reddy, S., Samanta, V., Burke, J., Estrin, D., Hansen, M., & Srivastava, M. (2009). Mobisense - mobile network services for coordinated participatory sensing. Proceedings - 2009 International Symposium on Autonomous Decentralized Systems, ISADS 2009, 231–236. https://doi.org/10.1109/ISADS.2009.5207328
Reinhardt, D., & Heinig, C. (2014). Survey-based exploration of attitudes to participatory sensing tasks in location-based gaming communities. Pervasive and Mobile Computing, 27, 27–36. https://doi.org/10.1016/j.pmcj.2016.01.001
Roos, M., & Coetzee, L. (2015). The obstacles faced by location-based services in South Africa, 50–54.
Sathe, S., Melamed, R., Bak, P., & Kalyanaraman, S. (2014). Enabling location-based services 2.0: Challenges and opportunities. Proceedings - IEEE International Conference on Mobile Data Management, 1, 313–316. https://doi.org/10.1109/MDM.2014.45
Shilton, K., Ramanathan, N., Reddy, S., Samanta, V., Burke, J. A., Estrin, D., … Srivastava, M. B. (2006). Participatory Design of Sensing Networks: Strengths and Challenges. Communications of the ACM, 47(6), 34–40. https://doi.org/10.1111/j.1469-8137.2009.02811.x
standard newspaper ,may 2, 2017. (n.d.). Alliance for Affordable Internet 2017.
Sun, X., Hu, S., Su, L., Abdelzaher, T. F., Hui, P., Zheng, W., … Stankovic, J. A. (2016). Sharing : Automatic Phone-to-Phone Communication in Vehicles, 15(10), 2550–2563.
Tarquini, M., & Morgano, M. (2013). Ethical challenges of participatory sensing for crisis information management. ISCRAM 2013 Conference Proceedings - 10th International Conference on Information Systems for Crisis Response and Management, (May), 421–425. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84905671113&partnerID=tZOtx3y1
Tilak, S. (2013). Real-World Deployments of Participatory Sensing Applications :, 2013.
Vergara-Laurens, I. J., Mendez, D., Jaimes, L. G., & Labrador, M. (2016). A-PIE: An algorithm for preserving privacy, quality of information, and energy consumption in Participatory Sensing Systems. Pervasive and Mobile Computing. https://doi.org/10.1016/j.pmcj.2016.06.020
Wang, X., Cheng, W., Mohapatra, P., & Abdelzaher, T. (2013). ARTSense: Anonymous reputation and trust in participatory sensing. Proceedings - IEEE INFOCOM, 2517–2525. https://doi.org/10.1109/INFCOM.2013.6567058
Yang, Mu; Sassone, Vladimiro and O’Hara, K. (n.d.). examples of some key anonymisation techniques 2012. Retrieved from http://ico.org.uk/
Zhang, B., Song, Z., Liu, C. H., Ma, J., & Wang, W. (2015). An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints. ACM Trans. Intell. Syst. Technol., 6(3), 42:1--42:19. https://doi.org/10.1145/2630074
Zhang, X., Yang, Z., Zhou, Z., Cai, H., Chen, L., & Li, X. (2014). Free market of crowdsourcing: Incentive mechanism design for mobile sensing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3190–3200. https://doi.org/10.1109/TPDS.2013.2297112
