AN INTELLIGENT EDGE PROCESSING FRAMEWORK USING SITUATION CALCULUS FOR IOT BASED SMART CITY

Authors

  • SK Alamgir Hossain Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
  • Md. Anisur Rahman Computer Science and Engineering Discipline, Khulna University, Khulna9208, Bangladesh
  • M. Anwar Hossain Department of Software Engineering, CCIS, King Saud University, KSA

DOI:

https://doi.org/10.53808/KUS.2022.ICSTEM4IR.0087-se

Keywords:

Edge Computing, Smart City, Situation Calculus, Internet of Things

Abstract

With advancements in IoT (Internet of Things), the number of applications and services for Smart Cities have increased in recent years, ranging from public services and safety to smart traffic and smart audio/video surveillance. As mobile and cloud computing have advanced, a large number of IoT devices have been connected to the Internet. This connected devices generates billions of bytes of data at the network edge. In order to fully realize the potential of this edge data there is a strong need to extend AI’s frontiers to the network edge. Edge computing, a new paradigm that shifts computing workloads and services from the network core to the network edge, has been identified as a viable approach to satisfy that need. The concept of Edge Intelligence (EI) in Smart Cities is gaining a lot of attraction. However, EI research is still in its early stages, and research groups would welcome a dedicated platform for communicating recent breakthroughs in EI in Smart Cities. To this end, we introduced a new technique of intelligent edge processing framework using Situation Calculus for IoT based Smart Cities. In this paper, we provide an overviewof our architecture, framework, and key techniques.

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References

Rahman, N. H. A., Cahyani, N. D. W. , and Choo, K. K. R. (2017). Cloud incident handling and forensic-by-design: cloud storage as a case study. Concurrency and Computation: Practice and Experience, 29(14).

Alonso, R. S., Candanedo’, I. S., Garcı´a, O., Prieto, J., and Gonza´lez, S. R. (2020). An intelligent edge-iot platform for monitoring livestock and crops in a dairy farming scenario. Ad Hoc Networks, 98:102047.

Atzori, L., Iera, A., and Morabito, G. (2010). The internet of things: A survey. Computer networks,54(15):2787–2805.

Bandyopadhyay, M., Singh, M. P., and Singh, V. (2013). Formalization and development of logic based emergency response systems using situation calculus. In 2013 International Conference on Machine Intelligence and Research Advancement, pages 504–508. IEEE, 2013.

Banihashemi, B., Giacomo, G. D., and Lespera´nce, Y. (2017). Abstraction in situation calculus action theories. In Thirty-First AAAI Conference on Artificial Intelligence.

Chang, C., Srirama, S. N., and Buyya, R. (2017). Indie fog: An efficient fog-computing infrastructure for the internet of things. Computer, 50(9):92–98.

Chouhan, P. K., Chen, L., Hussain, T., and Beard, A. (2021). A situation calculus based approach to cognitive modelling for responding to iot cyberattacks. In 2021 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI),pages 219–225. IEEE.

Cuff, D., Hansen, M., and Kang, J. (2008). Urban sensing: out of the woods. Communications of the ACM, 51(3):24–33.

Duan, Y., Lu, Z., Zhou, Z., Sun, X., and Wu, J. (2019). Data privacy protection for edge computing of smart city in a dikw architecture. Engineering Applications of Artificial Intelligence, 81:323–335.

Endsley, M. R. (1988). Design and evaluation for situation awareness enhancement. In Proceedings of the Human Factors Society annual meeting, volume 32, pages 97–101. SAGE Publications Sage CA: Los Angeles, CA.

Ganz, F., Barnaghi, P., and Carrez, F. (2014). Automated semantic knowledge acquisition from sensor data. IEEE Systems Journal, 10(3):1214–1225.

Habak, K., Ammar, M., Harras, K. A., and Zegura, E. (2015). Femto clouds: Leveraging mobile devices to provide cloud service at the edge. In Cloud Computing (CLOUD), 2015 IEEE 8th International Conference on, pages 9–16. IEEE, 2015.

Hossain, S. A., Rahman, M. A., and Hossain, M. A. (2018). Edge computing framework for enabling situation awareness in IoT based smart city. Journal of Parallel and Distributed Computing, 122:226–237.

Huang, J., Fox, M. S., and Gruninger, M.. Distributed trust reasoning in cyber-physical-social smart systems–a formalism in situation calculus.

Laya, A., Bratu, V. L., and Markendahl, J. (2013). Who is investing in machineto-machine communications? In 24th European Regional ITS Conference, Florence 2013, number 88475. International Telecommunications Society (ITS).

Levesque, H., Pirri, F., and Reiter, R.. Foundations for the situation calculus. 1998.

Luan, T. H., Gao, L., Li, Z., Xiang, Y., Wei, G., and Sun, L. (2015). Fog computing: Focusing on mobile users at the edge. arXiv preprint arXiv:1502.01815.

Lv, Z., Chen, D., Lou, R., and Wang, Q. (2021). Intelligent edge computing based on machine learning for smart city. Future Generation Computer Systems, 115:90–99.

Nilsson, N. J. (1982). Principles of artificial intelligence. Springer Science Business Media. City of Chicago. City of chicago open data. Technical report, https://data.cityofchicago.org/. Last accessed date: 26/05/2018.

Puiu, D., Barnaghi, P., Tonjes, R., Kumper, D., Ali, M. I., Mileo, A., Parreira, J. X., Fischer, M., Kolozali, S., Farajidavar, N., et al. (2016). Citypulse: Large scale data analytics framework for smart cities. IEEE Access, 4:1086–1108.

Rausch, T., Hummer, W., Muthusamy, V., Rashed, A., and Dustdar, S. (2019). Towards a serverless platform for edge AI. In 2nd USENIX Workshop on Hot Topics in Edge Computing (HotEdge 19).

Roman, R., Lopez, J., and Mambo, M. (2018). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78:680–698.

Smartsantander (2016). Future internet research and experimentation. Technical report, http://www.smartsantander.eu/.

Sokiyna, M. Y., Aqel, M. J., and Naqshbandi, O. A (2020). Cloud computing technology algorithms capabilities in managing and processing big data in business organizations: Mapreduce, hadoop, parallel programming. Journal of Information Technology Management, pages 113–126.

Stojanovic, L., Dinic, M, Stojanovic, N., and Stojadinovic, A. (2016). Big-data-driven anomaly detection in industry (4.0): An approach and a case study. In 2016 IEEE international conference on big data (big data), pages 1647–1652. IEEE.

Vanelli, B., Silva, M. P. D., Manerichi, G., Pinto, A. S. R., Dantas, M. A. R., Ferrandin, M., and Boava, A. (2017). Internet of things data storage infrastructure in the cloud using nosql databases. IEEE Latin America Transactions, 15(4):737–743.

Zanella, A., Bui, N., Castellani, A., Vangelista, L., and Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet of Things Journal, 1(1):22–32.

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Published

20-11-2022

How to Cite

[1]
S. A. . Hossain, M. A. . Rahman, and M. A. . Hossain, “AN INTELLIGENT EDGE PROCESSING FRAMEWORK USING SITUATION CALCULUS FOR IOT BASED SMART CITY”, Khulna Univ. Stud., pp. 513–530, Nov. 2022.

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