Edge Computing: Applications & Challenges: A Short Review
Keywords:
Edge computing, Cloud Computing and IoT
Abstract
Edge computing is distributed computing system in which Edge devices are used to process data locally, at the point of generation. Compared to alternative computer architectures, it offers higher latency as data processing and analysis are completed on the premises. Input-output ports, memory, storage, CPUs, and other components are integrated into edge computing tools. On these devices, data processing and analysis programs are installed at the location of data creation. A distributed computing paradigm known as "edge computing" improves reaction times and conserves bandwidth by bringing processing and data storage closer to the point of demand. Low latency, real-time processing, and the capacity to handle data at the edge of the network are important features. Edge computing is useful for many applications, including Internet of Things (IoT), virtual and augmented reality, smart cities, and driverless cars. Local data processing lowers latency, making it appropriate for applications where timely decision-making is essential. This study is to focus applications and challenges of edge computing along with tools which imparts key part of the edge computing. These tools and techniques can lead a better way to improve edge computing challenges. Hence this study recommends few research direction for research with regard latest development. This paper is review in the field edge computing.
References
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[2] O. A. Alzubi, J. A. Alzubi, M. Alazab, A. Alrabea, A. Awajan, and I. Qiqieh, “Optimized Machine Learning-Based Intrusion Detection System for Fog and Edge Computing Environment,” Electron., vol. 11, no. 19, pp. 1–16, 2022, doi: 10.3390/electronics11193007.
[3] Xu, W., Yang, Z., Ng, D. W. K., Levorato, M., Eldar, Y. C., & Debbah, M. (2023). Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing. IEEE journal of selected topics in signal processing, 17(1), 9-39.
[4] J. Xu, B. Gu, and G. Tian, “Review of agricultural IoT technology,” Artif. Intell. Agric., vol. 6, pp. 10–22, 2022, doi: 10.1016/j.aiia.2022.01.001.
[5] M. Haghi Kashani, M. Madanipour, M. Nikravan, P. Asghari, and E. Mahdipour, “A systematic review of IoT in healthcare: Applications, techniques, and trends,” J. Netw. Comput. Appl., vol. 192, no. May, p. 103164, 2021, doi: 10.1016/j.jnca.2021.103164.
[6] A. Ahamad, C. C. Sun, and W. K. Kuo, “Quantized Semantic Segmentation Deep Architecture for Deployment on an Edge Computing Device for Image Segmentation,” Electron., vol. 11, no. 21, 2022, doi: 10.3390/electronics11213561.
[7] M. Hartmann, U. S. Hashmi, and A. Imran, “Edge computing in smart health care systems: Review, challenges, and research directions,” Trans. Emerg. Telecommun. Technol., vol. 33, no. 3, pp. 1–25, 2022, doi: 10.1002/ett.3710.
[8] X. Hou, Z. Ren, J. Wang, S. Zheng, W. Cheng, and H. Zhang, “Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones,” IEEE Access, vol. 8, pp. 7117–7130, 2020, doi: 10.1109/ACCESS.2020.2964073.
[9] M. M. Kamruzzaman, B. Yan, M. N. I. Sarker, O. Alruwaili, M. Wu, and I. Alrashdi, “Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities,” J. Healthc. Eng., vol. 2022, 2022, doi: 10.1155/2022/9957888.
[10] M. Maray and J. Shuja, “Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues,” Mob. Inf. Syst., vol. 2022, 2022, doi: 10.1155/2022/1121822.
[11] R. Das and M. M. Inuwa, “A review on fog computing: Issues, characteristics, challenges, and potential applications,” Telemat. Informatics Reports, vol. 10, no. July 2022, 2023, doi: 10.1016/j.teler.2023.100049.
[12] R. Akhter and S. A. Sofi, “Precision agriculture using IoT data analytics and machine learning,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5602–5618, 2022, doi: 10.1016/j.jksuci.2021.05.013.
[13] H. Hua, Y. Li, T. Wang, N. Dong, W. Li, and J. Cao, “Edge Computing with Artificial Intelligence: A Machine Learning Perspective,” ACM Comput. Surv., vol. 55, no. 9, 2023, doi: 10.1145/3555802.
[14] L. A. Haibeh, M. C. E. Yagoub, and A. Jarray, “A Survey on Mobile Edge Computing Infrastructure: Design, Resource Management, and Optimization Approaches,” IEEE Access, vol. 10, pp. 27591–27610, 2022, doi: 10.1109/ACCESS.2022.3152787.
[15] R. Singh, A. Gehlot, S. Vaseem Akram, A. Kumar Thakur, D. Buddhi, and P. Kumar Das, “Forest 4.0: Digitalization of forest using the Internet of Things (IoT),” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5587–5601, 2022, doi: 10.1016/j.jksuci.2021.02.009.
[16] R. Shukla et al., “Detecting crop health using machine learning techniques in smart agriculture system,” J. Sci. Ind. Res. (India)., vol. 80, no. 8, pp. 699–706, 2021.
[17] S. Salvi et al., “Cloud based data analysis and monitoring of smart multi-level irrigation system using IoT,” Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017. pp. 752–757, 2017. doi: 10.1109/I-SMAC.2017.8058279.
[18] K. Cao, Y. Liu, G. Meng, and Q. Sun, “An Overview on Edge Computing Research,” IEEE Access, vol. 8, pp. 85714–85728, 2020, doi: 10.1109/ACCESS.2020.2991734.
[19] M. H. Guo et al., “Attention mechanisms in computer vision: A survey,” Comput. Vis. Media, vol. 8, no. 3, pp. 331–368, 2022, doi: 10.1007/s41095-022-0271-y.
[20] S. Hamdan, M. Ayyash, and S. Almajali, “Edge-computing architectures for internet of things applications: A survey,” Sensors (Switzerland), vol. 20, no. 22, pp. 1–52, 2020, doi: 10.3390/s20226441.
[21] S. S. Esfahlani, H. Shirvani, J. Butt, I. Mirzaee, and K. S. Esfahlani, “Machine Learning role in clinical decision-making: Neuro-rehabilitation video game,” Expert Syst. Appl., vol. 201, no. June 2021, p. 117165, 2022, doi: 10.1016/j.eswa.2022.117165.
[22] T. Edition, “Book Review Python Machine Learning : Machine Learning and Deep Learning With Python ,” vol. 11, no. 1, pp. 67–70, 2021.
[23] L. Fischer et al., “AI System Engineering — Key Challenges and Lessons Learned †,” pp. 56–83, 2021.
[24] B. I. Akhigbe, K. Munir, O. Akinade, L. Akanbi, and L. O. Oyedele, “IoT Technologies for Livestock Management : A Review of Present Status , Opportunities , and Future Trends,” 2021.
[25] R. Majeed, N. A. Abdullah, I. Ashraf, Y. Bin Zikria, M. F. Mushtaq, and M. Umer, “An Intelligent , Secure , and Smart Home Automation System,” vol. 2020, 2020.
[26] J. Fan, Y. Zhang, W. Wen, S. Gu, X. Lu, and X. Guo, “The future of Internet of Things in agriculture: Plant high-throughput phenotypic platform,” J. Clean. Prod., vol. 280, p. 123651, 2021, doi: 10.1016/j.jclepro.2020.123651.
[27] A. Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, “Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0,” Inf. Fusion, vol. 50, pp. 92–111, 2019, doi: 10.1016/j.inffus.2018.10.005.
[28] J. Woetzel et al., “Smart Cities: Digital Solutions for a More Livable Future,” McKinsey Co., 2018.
[29] M. Alkhatib, M. El Barachi, and K. Shaalan, “An Arabic social media based framework for incidents and events monitoring in smart cities,” J. Clean. Prod., vol. 220, pp. 771–785, 2019, doi: 10.1016/j.jclepro.2019.02.063.
[30] M. Ammar, G. Russello, and B. Crispo, “Journal of Information Security and Applications Internet of Things : A survey on the security of IoT frameworks,” J. Inf. Secur. Appl., vol. 38, pp. 8–27, 2018, doi: 10.1016/j.jisa.2017.11.002.
[31] N. Rajabli, F. Flammini, R. Nardone, and V. Vittorini, “Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review,” IEEE Access, pp. 4797–4819, 2020, doi: 10.1109/ACCESS.2020.3048047.
[32] A. Cyril Jose and R. Malekian, “Smart Home Automation Security: A Literature Review,” Smart Comput. Rev., vol. 5, no. 4, pp. 269–285, 2015, doi: 10.6029/smartcr.2015.04.004.
[33] V. Bhatt and S. Chakraborty, “Improving service engagement in healthcare through internet of things based healthcare systems,” J. Sci. Technol. Policy Manag., vol. 14, no. 1, pp. 53–73, 2023, doi: 10.1108/JSTPM-03-2021-0040.
[34] L. Liu, C. Chen, Q. Pei, S. Maharjan, and Y. Zhang, “Vehicular Edge Computing and Networking: A Survey,” Mob. Networks Appl., vol. 26, no. 3, pp. 1145–1168, 2021, doi: 10.1007/s11036-020-01624-1.
[35] W. Peng and O. Karimi Sadaghiani, “A review on the applications of machine learning and deep learning in agriculture section for the production of crop biomass raw materials,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 45, no. 3, pp. 9178–9201, 2023, doi: 10.1080/15567036.2023.2232322.
[36] N. Tariq, A. Qamar, M. Asim, and F. A. Khan, “Blockchain and smart healthcare security: A survey,” Procedia Comput. Sci., vol. 175, no. 2019, pp. 615–620, 2020, doi: 10.1016/j.procs.2020.07.089.
[37] C. Zeng, F. Zhang, and M. Luo, “A deep neural network-based decision support system for intelligent geospatial data analysis in intelligent agriculture system,” Soft Comput., vol. 26, no. 20, pp. 10813–10826, 2022, doi: 10.1007/s00500-022-07018-7.
[38] H. Sabireen and V. Neelanarayanan, “A Review on Fog Computing: Architecture, Fog with IoT, Algorithms and Research Challenges,” ICT Express, vol. 7, no. 2, pp. 162–176, 2021, doi: 10.1016/j.icte.2021.05.004.
[39] D. Rajapaksha, C. Tantithamthavorn, J. Jiarpakdee, C. Bergmeir, J. Grundy, and W. Buntine, “SQAPlanner: Generating Data-Informed Software Quality Improvement Plans,” IEEE Trans. Softw. Eng., vol. 5589, no. c, pp. 1–24, 2021, doi: 10.1109/TSE.2021.3070559.
Published
2025-01-20
How to Cite
Noor, D., Khan, I., Qamar, S., & Ayaz, S. (2025). Edge Computing: Applications & Challenges: A Short Review. International Journal of Computing and Related Technologies, 5(1), 36-48. Retrieved from http://ijcrt.smiu.edu.pk/index.php/smiu/article/view/215
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Copyright (c) 2025 Dua Noor, Imran Khan, Shahbaz Qamar, Shahzad Ayaz
This work is licensed under a Creative Commons Attribution 4.0 International License.
