Edge Computing in IoT: Challenges and Opportunities for Engineers

Year : 2024 | Volume : 14 | Issue : 03 | Page : 14 19
    By

    Vamsi Krishna Thatikonda,

  • Hemavantha Rajesh Varma Mudunuri,

  1. Software Engineer, Snoqualmie, Washington, United States of America
  2. Systems Architect, Cumming, Georgia, Washington, United States of America

Abstract

The internet of things (IoT) has revolutionized how we interact with our environment, collect data, and make decisions. However, the exponential growth of IoT devices has led to significant challenges in data processing, latency, and bandwidth usage. Edge computing provides an effective solution to these issues by bringing computation and data storage nearer to the source of data generation. This paper provides a comprehensive exploration of the challenges and opportunities that edge computing presents for engineers working in the IoT domain. We examine the current state of edge computing in IoT, discussing its potential to transform various industries and applications. The paper explores significant technical challenges, such as limited resources, concerns about security and privacy, scalability difficulties, and the requirement for resilient, fault-tolerant systems. Additionally, we highlight the exciting opportunities edge computing offers, such as enabling low latency applications, optimizing bandwidth usage, enhancing data privacy, and facilitating artificial intelligence (AI) and machine learning at the edge. Through case studies, data visualizations, and analysis of current research, we provide insights into real-world implementations and their impacts. The paper also discusses future trends and research directions, including the integration of 5G networks, the development of more efficient edge AI algorithms, and the creation of an edge-cloud continuum. By addressing these challenges and leveraging emerging technologies, engineers can unlock the full potential of edge computing to enable a new generation of IoT applications that are more efficient, responsive, and privacy-aware.

Keywords: Edge computing, internet of things (IoT), distributed computing, real-time processing, low latency, bandwidth optimization, edge AI, security and privacy, scalability, 5G networks

[This article belongs to Journal of Communication Engineering & Systems ]

How to cite this article:
Vamsi Krishna Thatikonda, Hemavantha Rajesh Varma Mudunuri. Edge Computing in IoT: Challenges and Opportunities for Engineers. Journal of Communication Engineering & Systems. 2024; 14(03):14-19.
How to cite this URL:
Vamsi Krishna Thatikonda, Hemavantha Rajesh Varma Mudunuri. Edge Computing in IoT: Challenges and Opportunities for Engineers. Journal of Communication Engineering & Systems. 2024; 14(03):14-19. Available from: https://journals.stmjournals.com/joces/article=2024/view=177260


References

  1. Cisco Annual Internet Report – Cisco Annual Internet Report (2018–23) [White paper]. Cisco, USA; 2022. Available from: https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html.
  2. Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge computing: Vision and challenges. IEEE Internet Things J. 2016;3:637-46. DOI: 10.1109/JIOT.2016.2579198.
  3. Satyanarayanan M. The emergence of edge computing. Computer. 2017;50:30-9. DOI: 10.1109/MC.2017.9.
  4. 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. 2012 Aug 17. p. 13-6. DOI: 10.1145/2342509.2342513.
  5. Yousefpour A, Fung C, Nguyen T, Kadiyala K, Jalali F, Niakanlahiji A, Kong J, Jue JP. All one needs to know about fog computing and related edge computing paradigms: A complete survey. J Syst Archit. 2019;98:289-330. DOI: 10.1016/j.sysarc.2019.02.009.
  6. Chen J, Ran X. Deep learning with edge computing: A review. Proc IEEE. 2019;107:1655-74. DOI: 10.1109/JPROC.2019.2921977.
  7. Porambage P, Okwuibe J, Liyanage M, Ylianttila M, Taleb T. Survey on multi-access edge computing for internet of things realization. IEEE Commun Surv Tutor. 2018;20:2961-91. DOI: 10.1109/COMST.2018.2849509.
  8. Li H, Ota K, Dong M. Learning IoT in edge: Deep learning for the Internet of Things with edge computing. IEEE Netw. 2018;32:96-101. DOI: 10.1109/MNET.2018.1700202.
  9. Khan WZ, Ahmed E, Hakak S, Yaqoob I, Ahmed A. Edge computing: A survey. Future Gener Comput Syst. 2019;97:219-35. DOI: 10.1016/j.future.2019.02.050.
  10. Taleb T, Samdanis K, Mada B, Flinck H, Dutta S, Sabella D. On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun Surv Tutor. 2017;19:1657-81. DOI: 10.1109/COMST.2017.2705720.
Regular Issue Subscription Review Article
Volume 14
Issue 03
Received 14/09/2024
Accepted 23/09/2024
Published 07/10/2024
914

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