Abstract
The increasing popularity of smartphones, associated with their capability to sense the environment, has allowed the creation of an increasing range of data-driven applications. In general, this type of application collects data from the environment using edge devices and sends them to a remote cloud to be processed. In this setting, the governance of the application and its data is, usually, unilaterally defined by the cloud-based application provider. We propose an architectural model which allows this kind of application to be governed solely by the community of users, instead. We consider members of a community who have some common problem to solve, and eliminate the dependence on an external cloud-based application provider by leveraging the capabilities of the devices sitting on the edge of the network. We combine the concepts of Participatory Sensing, Mobile Social Networks and Edge Computing, which allows data processing to be done closer to data sources. We define our model and then present a case study that aims to evaluate the feasibility of our proposal, and how its performance compares to that of other existing solutions (e.g. cloud-based architecture). The case study uses simulation experiments fed with real data from the public transport system of Curitiba city, in Brazil. The results show that the proposed approach is feasible, and can aggregate as much data as current approaches that use remote dedicated servers. Differently from the all-or-nothing sharing policy of current approaches, the approach proposed allows users to autonomously configure the trade-off between the sharing of private data, and the performance that the application can achieve.
This work was supported by the Innovation Center, Ericsson Telecomunicacoes S.A., Brazil and by EMBRAPII-CEEI.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
There are more attributes in the original data, but we just cite the data we use in our model.
References
Aazam, M., Huh, E.: Fog computing and smart gateway based communication for cloud of things. In: 2014 International Conference on Future Internet of Things and Cloud, pp. 464–470, August 2014. https://doi.org/10.1109/FiCloud.2014.83
Bellavista, P., Chessa, S., Foschini, L., Gioia, L., Girolami, M.: Human-enabled edge computing: exploiting the crowd as a dynamic extension of mobile edge computing. IEEE Commun. Mag. 56(1), 145–155 (2018). https://doi.org/10.1109/MCOM.2017.1700385
Bonawitz, K., et al.: Towards federated learning at scale: system design. CoRR abs/1902.01046 (2019). http://arxiv.org/abs/1902.01046
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the Internet of Things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, MCC 2012, pp. 13–16. ACM, New York (2012). https://doi.org/10.1145/2342509.2342513, http://doi.acm.org/10.1145/2342509.2342513
Burke, J., et al.: Participatory sensing. In: Workshop on World-Sensor-Web (WSW 2006): Mobile Device Centric Sensor Networks and Applications, pp. 117–134 (2006)
Ganti, R.K., Pham, N., Ahmadi, H., Nangia, S., Abdelzaher, T.F.: GreenGPS: a participatory sensing fuel-efficient maps application. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, MobiSys 2010, pp. 151–164. ACM, New York (2010). https://doi.org/10.1145/1814433.1814450, http://doi.acm.org/10.1145/1814433.1814450
Guo, B., Yu, Z., Zhou, X., Zhang, D.: From participatory sensing to mobile crowd sensing. In: 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), pp. 593–598, March 2014. https://doi.org/10.1109/PerComW.2014.6815273
Hartigan, J.A., Wong, M.A.: Algorithm as 136: a k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979). http://www.jstor.org/stable/2346830
Hartigan, J.A.: Clustering Algorithms, 99th edn. Wiley, New York (1975)
Kuendig, S.J., Rolim, J., Angelopoulos, K.M., Hosseini, M.: Crowdsourced edge: a novel networking paradigm for the collaborative community. Technical report (2019). https://archive-ouverte.unige.ch/unige:114607. ID: unige:114607; Paper submitted for publication at the Global IoT Summit 2019
Lohmar, T., Zaidi, A., Olofsson, H., Boberg, C.: Driving transformation in the automotive and road transport ecosystem with 5G. Ericsson Technology Review (2019)
Luan, T.H., Gao, L., Li, Z., Xiang, Y., Sun, L.: Fog computing: focusing on mobile users at the edge. CoRR abs/1502.01815 (2015), http://arxiv.org/abs/1502.01815
Ludwig, T., Reuter, C., Siebigteroth, T., Pipek, V.: CrowdMonitor: mobile crowd sensing for assessing physical and digital activities of citizens during emergencies. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, pp. 4083–4092. ACM, New York (2015). https://doi.org/10.1145/2702123.2702265, http://doi.acm.org/10.1145/2702123.2702265
Mafra, J., Brasileiro, F.V., Lopes, R.V.: Community-governed services on the edge. In: Ferguson, D., Helfert, M., Pahl, C. (eds.) Proceedings of the 10th International Conference on Cloud Computing and Services Science, CLOSER 2020, Prague, Czech Republic, 7–9 May 2020, pp. 498–505. SCITEPRESS (2020). https://doi.org/10.5220/0009765804980505
Mao, Y., You, C., Zhang, J., Huang, K., Letaief, K.B.: A survey on mobile edge computing: the communication perspective. IEEE Commun. Surv. Tutorials 19(4), 2322–2358 (2017). https://doi.org/10.1109/COMST.2017.2745201. Fourthquarter
Miluzzo, E., et al.: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, SenSys 2008, pp. 337–350. ACM, New York (2008). https://doi.org/10.1145/1460412.1460445, http://doi.acm.org/10.1145/1460412.1460445
de Oliveira Filho, T.B.: Inferring passenger-level bus trip traces from schedule, positioning and ticketing data: methods and applications. Master dissertation, Universidade Federal de Campina Grande, Paraíba, Brasil (2019)
Predić, B., Yan, Z., Eberle, J., Stojanovic, D., Aberer, K.: ExposureSense: integrating daily activities with air quality using mobile participatory sensing. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 303–305, March 2013. https://doi.org/10.1109/PerComW.2013.6529500
Reddy, S., Shilton, K., Denisov, G., Cenizal, C., Estrin, D., Srivastava, M.: Biketastic: sensing and mapping for better biking. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2010, pp. 1817–1820. ACM, New York (2010). https://doi.org/10.1145/1753326.1753598, http://doi.acm.org/10.1145/1753326.1753598
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7, http://www.sciencedirect.com/science/article/pii/0377042787901257
Ruge, L., Altakrouri, B., Schrader, A.: SoundOfTheCity - continuous noise monitoring for a healthy city. In: 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 670–675, March 2013. https://doi.org/10.1109/PerComW.2013.6529577
Satyanarayanan, M., Bahl, V., Caceres, R., Davies, N.: The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8, 14–23 (2009)
Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016). https://doi.org/10.1109/JIOT.2016.2579198
Traud, A.L., Mucha, P.J., Porter, M.A.: Social structure of Facebook networks. Phys. A 391(16), 4165–4180 (2012)
Tsai, F.S., Han, W., Xu, J., Chua, H.C.: Design and development of a mobile peer-to-peer social networking application. Expert Syst. Appl. 36(8), 11077–11087 (2009). https://doi.org/10.1016/j.eswa.2009.02.093, http://www.sciencedirect.com/science/article/pii/S0957417409002498
Zhou, P., Zheng, Y., Li, M.: How long to wait? Predicting bus arrival time with mobile phone based participatory sensing. IEEE Trans. Mob. Comput. 13(6), 1228–1241 (2014). https://doi.org/10.1109/TMC.2013.136
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Mafra, J., Brasileiro, F., Lopes, R. (2021). A Case for User-Defined Governance of Pure Edge Data-Driven Applications. In: Ferguson, D., Pahl, C., Helfert, M. (eds) Cloud Computing and Services Science. CLOSER 2020. Communications in Computer and Information Science, vol 1399. Springer, Cham. https://doi.org/10.1007/978-3-030-72369-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-72369-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72368-2
Online ISBN: 978-3-030-72369-9
eBook Packages: Computer ScienceComputer Science (R0)