Edge Computing for Low-Latency loT Applications in Smart Cities
DOI:
https://doi.org/10.22105/siot.v1i4.251Keywords:
Edge computing, Internet of things, Smart cities, Low latency, Real-time processingAbstract
The swift advancement of Internet of Things (IoT) technologies is transforming traditional urban landscapes into intelligent cities, generating substantial volumes of data from interconnected devices. Nonetheless, the latency associated with cloud computing solutions creates obstacles for time-critical applications, including real-time traffic control, emergency response systems, and environmental monitoring. This paper examines the utilization of edge computing as a practical solution to reduce latency and enhance the effectiveness of IoT applications within smart cities. By processing data nearer to its origin—such as IoT sensors and devices—edge computing significantly diminishes the duration required for data transfer and analysis. This localized data processing enhances response times, optimizes bandwidth utilization, and alleviates the stress on centralized cloud infrastructures. Through case studies and empirical evaluations, we assess the efficacy of edge computing in diverse smart city situations, underscoring its capacity to facilitate real-time decision-making and boost overall urban efficiency. Additionally, the paper addresses architectural considerations, security issues, and prospective trends in the integration of edge computing within smart city frameworks. The results indicate that adopting edge computing can cultivate more resilient, adaptive, and effective smart city ecosystems, ultimately improving the quality of life for urban inhabitants.
References
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things. Proceedings of the first edition of the MCC workshop on Mobile cloud computing (pp. 13-16).. https://doi.org/10.1145/2342509.2342513
Arivazhagan, C., & Natarajan, V. (2020). A survey on fog computing paradigms, Challenges and Opportunities in IoT. 2020 international conference on communication and signal processing (ICCSP) (pp. 0385-0389). IEEE.. https://doi.org/10.1109/ICCSP48568.2020.9182229
Yi, S., Hao, Z., Qin, Z., & Li, Q. (2015). Fog computing: platform and applications. 2015 Third IEEE workshop on hot topics in web systems and technologies (HotWeb) (pp. 73-78). IEEE. https://doi.org/10.1109/HotWeb.2015.22
Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE internet of things journal, 3(6), 854–864. https://doi.org/10.1109/JIOT.2016.2584538
Dastjerdi, A. V., & Buyya, R. (2016). Fog computing: Helping the Internet of Things realize its potential. Computer, 49(8), 112–116. https://doi.org/10.1109/MC.2016.245
Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., & Koldehofe, B. (2013). Mobile fog: A programming model for large-scale applications on the internet of things. Proceedings of the second ACM SIGCOMM workshop on Mobile cloud computing (pp. 15-20). https://doi.org/10.1145/2491266.2491270
Mukherjee, M., Shu, L., & Wang, D. (2018). Survey of fog computing: Fundamental, network applications, and research challenges. IEEE communications surveys & tutorials, 20(3), 1826–1857. https://doi.org/10.1109/COMST.2018.2814571
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., … Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of systems architecture, 98, 289–330. https://doi.org/10.1016/j.sysarc.2019.02.009
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE communications surveys & tutorials, 17(4), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095
Khan, W. Z., Rehman, M. H., Zangoti, H. M., Afzal, M. K., Armi, N., & Salah, K. (2020). Industrial internet of things: Recent advances, enabling technologies and open challenges. Computers & electrical engineering, 81, 106522. https://doi.org/10.1016/j.compeleceng.2019.106522
Stojmenovic, I., & Wen, S. (2014). The fog computing paradigm: Scenarios and security issues. 2014 federated conference on computer science and information systems (pp. 1-8). IEEE. https://doi.org/10.15439/2014F503
Mahmud, R., Kotagiri, R., & Buyya, R. (2018). Fog computing: A taxonomy, survey and future directions. Internet of everything: algorithms, methodologies, technologies and perspectives, 103–130. https://doi.org/10.1007/978-981-10-5861-5_5
Songhorabadi, M., Rahimi, M., Farid, A. M. M., & Kashani, M. H. (2020). Fog computing approaches in smart cities: a state-of-the-art review. ArXiv preprint arxiv:2011.14732. https://doi.org/10.48550/arXiv.2011.14732
Mohapatra, H., & Rath, A. K. (2021). An IoT based efficient multi-objective real-time smart parking system. International journal of sensor networks, 37(4), 219–232. https://doi.org/10.1504/IJSNET.2021.119483
Mohapatra, H. (2021). Socio-technical challenges in the implementation of smart city. 2021 international conference on innovation and intelligence for informatics, computing, and technologies (3ICT) (pp. 57-62). IEEE. https://doi.org/10.1109/3ICT53449.2021.9581905