edge computing research papers ieee

An Overview on Edge Computing Research

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

• Create Account

Main navigation dropdown

Publications, collaborative ai for edge-cloud computing and applications, publication date, third quarter 2023, manuscript submission deadline, 30 june 2023, call for papers.

Edge–cloud collaboration has become a popular framework to enable the solving of resource-intensive tasks with a set of distributed deployed edge devices that collaboratively work with the cloud to achieve low-latency and high efficiency. It envisions a wide range of applications in Internet of Things (IoT), Vehicle-to-Everything (V2X) Networks, Crowd Sourcing, Unmanned Aerial Vehicles (UAV) systems, etc. There is an emerging trend to train and deploy collaborative artificial intelligence (AI) model with the edge–cloud paradigm, which integrates the edges’ cognition to develop far superior intelligence through goal-driven strategic interactions among the collaborating edges and the cloud. It is critical to develop advanced edge–cloud computing mechanisms to enable collaborative AI to confront challenges like device heterogeneity, resource constraints, energy efficiency, communication costs, data privacy, scalability, model accuracy and robustness, etc.

This Special Issue brings together leading research experts from industry and academia to present their novel and original contributions on utilizing edge-cloud computing technology to enable collaborative AI.

The topics of interest for this special issue include, but are not limited to:

• Novel design of machine learning approaches for edge-cloud systems and applications.
• Collaborative AI for optimizing wireless edge-cloud communication systems.
• Collaborative AI for distributed resource management, including cloud resources, edge resources, energy resources, computing resources, and communication infrastructure, etc.
• Collaborative AI and its applications in 5G-and-beyond, Internet of Things (IoT), Vehicle-to-Everything (V2X) Networks, Crowd Sourcing, Unmanned Aerial Vehicles (UAV) systems, etc.
• Cloud AI for training and accelerating large-scale AI models for the areas of Graph, CV, NLP, and web services.
• Edge intelligence in dealing with the bandwidth, privacy or compute-transmission balance.
• Distributed machine learning mechanisms such as federated edge learning for data privacy and device heterogeneity.
• Collaborative AI for smart home, smartphone and mobile applications.
• Deep learning and machine learning for mobile systems and networking.

Submission Guidelines

Prospective authors should submit their manuscripts following the  IEEE OJCOMS   guidelines . Authors should submit a manuscript trough  Manuscript Central .

Important Dates

Manuscript Submission Deadline: 30 June 2023 Publication Date: Third Quarter 2023

Lead Guest Editor

Wenzhong Li , Nanjing University, China

Guest Editors

Luigi Iannone , Huawei Technologies France, France Yipeng Zhou , Macquarie University, Australia Xin Wang , Stony Brook University, New York, USA Xiaoming Fu , Georg-August-University of Goettingen, Germany

IEEE Conferences

• iEDGE 2023 Symposium
• Call for Papers
• Organizing Committee

IEEE EDGE 2023

IEEE INTERNATIONAL CONFERENCE ON EDGE COMPUTING & COMMUNICATIONS

Hybrid event

Chicago, Illinois USA

Hyatt Regency Chicago

July 2-8, 2023

About IEEE EDGE 2023

IEEE International Conference on Edge Computing (EDGE) aims to become a prime international forum for both researchers and industry practitioners to exchange the latest fundamental advances in the state of the art and practice of Edge computing, identify emerging research topics, and define the future of Edge computing. EDGE covers the localized resource sharing and connections with the cloud.

About IEEE SERVICES 2023

SERVICES 2023 is solely sponsored by the IEEE Computer Society under the auspice of the Technical Community on Services Computing (TCSVC). The scope of SERVICES 2023 covers all aspects of services computing and applications, current or emerging. Centered around services computing, SERVICES 2023 covers various systems and networking research pertaining to cloud, edge and Internet-of Things (IoT), as well as technologies for intelligent computing, learning, Big Data and blockchain applications, addressing critical issues such as knowledge network, high performance, security, privacy, dependability, trustworthiness, and cost-effectiveness. Particularly, the 2023 Congress will welcome papers on the aftermath and the impact of COVID-19 on services and the world infrastructure. In addition to co-located theme-topic conferences, the Congress will also include symposia and workshops supporting deep-dive discussions on emerging important topics, and complement the SERVICES 2023 program with industry and application presentations and panels. Authors are invited to prepare early and submit original papers to any of these conferences at www.easychair.org. All submitted manuscripts will be peer-reviewed by at least three reviewers. Accepted and presented papers will appear in the conference proceedings published by the IEEE Computer Society Press. SERVICES 2023 is the only premier professional event for the services computing field offered by IEEE.

Important Dates for 2023

December 1, 2023: EasyChair open for DRAFT submissions UPDATED: March 25, 2023: EasyChair closes for submissions (e.g., HARD submission deadline) UPDATED: May 8, 2023: Acceptance notification UPDATED: Camera-ready due: June 5, 2023 July 2-8, 2023: SERVICES Congress in Chicago

To contact EDGE organizers, please send an email to [email protected]

About IEEE and IEEE Computer Society

IEEE is the world’s largest professional association advancing innovation and technological excellence for the benefit of humanity. IEEE and its members inspire a global community to innovate for a better tomorrow through its highly cited publications, conferences, technology standards, and professional and educational activities. IEEE is the trusted voice for engineering, computing and technology information around the globe.

With nearly 85,000 members, the IEEE Computer Society (CS) is the world’s leading organization of computing professionals. Founded in 1946, and the largest of the 38 societies of the Institute of Electrical and Electronics Engineers (IEEE), the CS is dedicated to advancing the theory and application of computer and information-processing technology.

About the Technical Community on Services Computing (TCSVC)

Founded in 2003, IEEE Computer Society's Technical Community on Services Computing (TCSVC) is a multidisciplinary group whose purpose is to advance and coordinate work in the field of Services Computing carried out throughout the IEEE in scientific, engineering, standard, literary and educational areas. IEEE TCSVC membership details are available at http://tab.computer.org/tcsvc/

Help | Advanced Search

Computer Science > Networking and Internet Architecture

Title: edge computing for iot.

Abstract: Over the past few years, The idea of edge computing has seen substantial expansion in both academic and industrial circles. This computing approach has garnered attention due to its integrating role in advancing various state-of-the-art technologies such as Internet of Things (IoT) , 5G, artificial intelligence, and augmented reality. In this chapter, we introduce computing paradigms for IoT, offering an overview of the current cutting-edge computing approaches that can be used with IoT. Furthermore, we go deeper into edge computing paradigms, specifically focusing on cloudlet and mobile edge computing. After that, we investigate the architecture of edge computing-based IoT, its advantages, and the technologies that make Edge computing-based IoT possible, including artificial intelligence and lightweight virtualization. Additionally, we review real-life case studies of how edge computing is applied in IoT-based Intelligent Systems, including areas like healthcare, manufacturing, agriculture, and transportation. Finally, we discuss current research obstacles and outline potential future directions for further investigation in this domain.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Edge Computing for Real-Time Internet of Things Applications: Future Internet Revolution

• Published: 26 July 2023
• Volume 132 , pages 1423–1452, ( 2023 )

Cite this article

• Nguyen Minh Quy 1 ,
• Le Anh Ngoc 2 ,
• Nguyen Tien Ban 3 ,
• Nguyen Van Hau 1 &
• Vu Khanh Quy   ORCID: orcid.org/0000-0002-0242-5606 1  

607 Accesses

7 Citations

Explore all metrics

The Internet of Things (IoT) is a concept that permits the integration of all objects into an Internet environment. IoT has spawned numerous intelligent applications and services to benefit organizations, society, and consumer experiences. On the other hand, traditional computing methods are incapable of handling the demands of these services. The advent of cloud computing methods that provides software, platform, and infrastructure such as services have realized these applications. However, one of the critical obstacles of real-time cloud-based IoT applications is service response time. Edge computing solutions have been developed to address these issues. In this work, we provide a comprehensive survey of driving enforce edge computing for IoT applications on aspects of the research timeline, applications, vision, challenges, and open research issues. Through this, we highlight the benefits of edge computing over cloud computing in almost domains. This study will contribute to driving empowerment intelligence to the edge of networks to form the next intelligent edge era.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Edge Computing Architectures in Industry 4.0: A General Survey and Comparison

Survey of Edge Computing Based on a Generalized Framework and Some Recommendation

Exploring the Future of Edge Computing: Advantages, Limitations, and Opportunities

Data availability.

Enquiries about data availability should be directed to the authors.

Quy, V. K., Van-Hau, N., Quy, N. M., Anh, D. V., Ngoc, L. A., & Chehri, A. (2023). An efficient edge computing management mechanism for sustainable smart cities. Sustainable Computing: Informatics and Systems, 37 , 100867. https://doi.org/10.1016/j.suscom.2023.100867

Article   Google Scholar  

Ahmed, S. T., Kumar, V. V., Singh, K. K., Singh, A., Muthukumaran, V., & Gupta, D. (2022). 6G enabled federated learning for secure IoMT resource recommendation and propagation analysis. Computers and Electrical Engineering, 102 , 108210. https://doi.org/10.1016/j.compeleceng.2022.108210

Quy, V. K., Chehri, A., Han, N. D., & Ban, N. T. (2023). Innovative trends in the 6G era: A comprehensive survey of architecture, applications, technologies, and challenges. IEEE Access . https://doi.org/10.1109/ACCESS.2023.3269297

Dao, N.-N., Pham, Q.-V., Do, D.-T., & Dustdar, S. (2021). The sky is the edge—Toward mobile coverage from the sky. IEEE Internet Computing, 25 (2), 101–108. https://doi.org/10.1109/MIC.2020.3033976

Zikria, Y. B., Ali, R., Afzal, M. K., & Kim, S. W. (2021). Next-generation Internet of Things (IoT): Opportunities, challenges, and solutions. Sensors (Basel, Switzerland), 21 (4), 1174. https://doi.org/10.3390/s21041174

El-Sayed, H., et al. (2018). Edge of things: The big picture on the integration of edge, IoT and the cloud in a distributed computing environment. IEEE Access, 6 , 1706–1717. https://doi.org/10.1109/ACCESS.2017.2780087

Wang, T., Ke, H., Zheng, X., Wang, K., Sangaiah, A. K., & Liu, A. (2020). Big data cleaning based on mobile edge computing in industrial sensor-cloud. IEEE Transactions on Industrial Informatics, 16 (2), 1321–1329. https://doi.org/10.1109/TII.2019.2938861

De Donno, M., Tange, K., & Dragoni, N. (2019). Foundations and evolution of modern computing paradigms: Cloud, IoT, edge, and fog. IEEE Access, 7 , 150936–150948. https://doi.org/10.1109/ACCESS.2019.2947652

Quy, V. K., Hung, L. N., & Han, N. D. (2019). CEPRM: A cloud-assisted energy-saving and performance-improving routing mechanism for MANETs. Journal of Communications, 14 (12), 1211–1217. https://doi.org/10.12720/jcm.14.12.1211-1217

Ramaiah, N. S., & Ahmed, S. T. (2022). An IoT-based treatment optimization and priority assignment using machine learning. ECS Transactions, 107 (1), 1487. https://doi.org/10.1149/10701.1487ecst

Dang, V. A., Quy, V. K., Hau, V. N., Nguyen, T., & Nguyen, D. C. (2023). Intelligent healthcare: Integration of emerging technologies and Internet of Things for humanity. Sensors, 23 (9), 4200. https://doi.org/10.3390/s23094200

Ren, J., He, Y., Huang, G., Yu, G., Cai, Y., & Zhang, Z. (2019). An edge-computing based architecture for mobile augmented reality. IEEE Network, 33 (4), 162–169. https://doi.org/10.1109/MNET.2018.1800132

Hassan, N., Yau, K. A., & Wu, C. (2019). Edge computing in 5G: A review. IEEE Access, 7 , 127276–127289. https://doi.org/10.1109/ACCESS.2019.2938534

Khalid, M., et al. (2021). Autonomous transportation in emergency healthcare services: Framework, challenges, and future work. IEEE Internet of Things Magazine, 4 (1), 28–33. https://doi.org/10.1109/IOTM.0011.2000076

Yang, Z., Liang, B., & Ji, W. (2021). An intelligent end-edge-cloud architecture for visual IoT assisted healthcare systems. IEEE Internet of Things Journal . https://doi.org/10.1109/JIOT.2021.3052778

Kang, J., et al. (2019). Blockchain for secure and efficient data sharing in vehicular edge computing and networks. IEEE Internet of Things Journal, 6 (3), 4660–4670. https://doi.org/10.1109/JIOT.2018.2875542

Tang, J., Liu, S., Liu, L., Yu, B., & Shi, W. (2020). LoPECS: A low-power edge computing system for real-time autonomous driving services. IEEE Access, 8 , 30467–30479. https://doi.org/10.1109/ACCESS.2020.2970728

Su, X., Sperlì, G., Moscato, V., Picariello, A., Esposito, C., & Choi, C. (2019). An edge intelligence empowered recommender system enabling cultural heritage applications. IEEE Transactions on Industrial Informatics, 15 (7), 4266–4275. https://doi.org/10.1109/TII.2019.2908056

Sun, C., Li, H., Li, X., Wen, J., Xiong, Q., & Zhou, W. (2020). Convergence of recommender systems and edge computing: A comprehensive survey. IEEE Access, 8 , 47118–47132. https://doi.org/10.1109/ACCESS.2020.2978896

Ghosh, S., Mukherjee, A., Ghosh, S. K., & Buyya, R. (2020). Mobi-IoST: Mobility-aware cloud-fog-edge-IoT collaborative framework for time-critical applications. IEEE Transactions on Network Science and Engineering, 7 (4), 2271–2285. https://doi.org/10.1109/TNSE.2019.2941754

Wang, H., et al. (2020). Architectural design alternatives based on cloud/edge/fog computing for connected vehicles. IEEE Communications Surveys & Tutorials, 22 (4), 2349–2377. https://doi.org/10.1109/COMST.2020.3020854

Xie, R., Tang, Q., Wang, Q., Liu, X., Yu, F. R., & Huang, T. (2019). Collaborative vehicular edge computing networks: Architecture design and research challenges. IEEE Access, 7 , 178942–178952. https://doi.org/10.1109/ACCESS.2019.2957749

Qadir, J., Sainz-De-Abajo, B., Khan, A., García-Zapirain, B., De La Torre-Díez, I., & Mahmood, H. (2020). Towards mobile edge computing: Taxonomy, challenges, applications and future realms. IEEE Access, 8 , 189129–189162. https://doi.org/10.1109/ACCESS.2020.3026938

Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., & Sabella, D. (2017). On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Communications Surveys & Tutorials, 19 (3), 1657–1681. https://doi.org/10.1109/COMST.2017.2705720

Quy, V. K., Hau, N. V., Anh, D. V., et al. (2021). Smart healthcare IoT applications based on fog computing: Architecture, applications and challenges. Complex and Intelligent Systems . https://doi.org/10.1007/s40747-021-00582-9

Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020). Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys & Tutorials, 22 (2), 869–904. https://doi.org/10.1109/COMST.2020.2970550

Pham, Q., et al. (2020). A survey of multi-access edge computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art. IEEE Access, 8 , 116974–117017. https://doi.org/10.1109/ACCESS.2020.3001277

Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 20 (1), 416–464. https://doi.org/10.1109/COMST.2017.2771153

Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5 (1), 450–465. https://doi.org/10.1109/JIOT.2017.2750180

Omoniwa, B., Hussain, R., Javed, M. A., Bouk, S. H., & Malik, S. A. (2019). Fog/edge computing-based IoT (FECIoT): Architecture, applications, and research issues. IEEE Internet of Things Journal, 6 (3), 4118–4149. https://doi.org/10.1109/JIOT.2018.2875544

Jiang, C., Chen, Y., Wang, Q., & Liu, K. J. R. (2018). Data-driven auction mechanism design in IaaS cloud computing. IEEE Transactions on Services Computing, 11 (5), 743–756. https://doi.org/10.1109/TSC.2015.2464810

Asim, M., Wang, Y., Wang, K., & Huang, P.-Q. (2020). A review on computational intelligence techniques in cloud and edge computing. IEEE Transactions on Emerging Topics in Computational Intelligence, 4 (6), 742–763. https://doi.org/10.1109/TETCI.2020.3007905

Alhamazani, K., et al. (2019). Cross-layer multi-cloud real-time application QoS monitoring and benchmarking as-a-service framework. IEEE Transactions on Cloud Computing, 7 (1), 48–61. https://doi.org/10.1109/TCC.2015.2441715

Liu, Y., Peng, M., Shou, G., Chen, Y., & Chen, S. (2020). Toward edge intelligence: Multiaccess edge computing for 5G and internet of things. IEEE Internet of Things Journal, 7 (8), 6722–6747. https://doi.org/10.1109/JIOT.2020.3004500

Ma, L., Wang, X., Wang, X., Wang, L., Shi, Y., & Huang, M. (2021). TCDA: Truthful combinatorial double auctions for mobile edge computing in industrial Internet of Things. IEEE Transactions on Mobile Computing . https://doi.org/10.1109/TMC.2021.3064314

Kristiani, E., Yang, C.-T., Huang, C.-Y., Ko, P.-C., & Fathoni, H. (2021). On construction of sensors, edge, and cloud (iSEC) framework for smart system integration and applications. IEEE Internet of Things Journal, 8 (1), 309–319. https://doi.org/10.1109/JIOT.2020.3004244

Ma, J., Zhou, H., Liu, C., Mingcheng, E., Jiang, Z., & Wang, Q. (2020). Study on edge-cloud collaborative production scheduling based on enterprises with multi-factory. IEEE Access, 8 , 30069–30080. https://doi.org/10.1109/ACCESS.2020.2972914

https://www.cisco.com/c/en/us/products/collateral/se/internet-of-things/at-a-glance-c45-731471.pdf . Accessed 07 May 2021.

Zhang, L., Liang, Y., & Niyato, D. (2019). 6G visions: Mobile ultra-broadband, super Internet-of-Things, and artificial intelligence. China Communications, 16 (8), 1–14. https://doi.org/10.23919/JCC.2019.08.001

Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 20 (4), 2923–2960. https://doi.org/10.1109/COMST.2018.2844341

Sezer, O. B., Dogdu, E., & Ozbayoglu, A. M. (2018). Context-aware computing, learning, and big data in internet of things: A survey. IEEE Internet of Things Journal, 5 (1), 1–27. https://doi.org/10.1109/JIOT.2017.2773600

https://www.huawei.com/en/news/2017/3/Huawei-Launched-Edge-Computing-IoT-Solution . Accessed 07 May 2021.

https://www.nokia.com/blog/edge-computing-takes-a-further-leap-forward-with-move-to-harmonize-standards . Accessed 7 May 2022.

https://www.3gpp.org/news-events/2152-edge_sa6 . Accessed 7 May 2022.

https://www.3gpp.org , Specification # 23.758. Accessed 7 May 2022.

https://www.samsungnext.com/blog/the-future-of-ai-is-on-the-edge . Accessed 7 May 2022.

Ren, P., et al. (2020). Edge AR X5: An edge-assisted multi-user collaborative framework for mobile web augmented reality in 5G and beyond. IEEE Transactions on Cloud Computing . https://doi.org/10.1109/TCC.2020.3046128

Al-Shuwaili, & Simeone, O. (2017). Energy-efficient resource allocation for mobile edge computing-based augmented reality applications. IEEE Wireless Communications Letters, 6 (3), 398–401. https://doi.org/10.1109/LWC.2017.2696539

Ahn, J., Lee, J., Yoon, S., & Choi, J. K. (2020). A novel resolution and power control scheme for energy-efficient mobile augmented reality applications in mobile edge computing. IEEE Wireless Communications Letters, 9 (6), 750–754. https://doi.org/10.1109/LWC.2019.2950250

Ahn, J., Lee, J., Niyato, D., & Park, H.-S. (2020). Novel QoS-guaranteed orchestration scheme for energy-efficient mobile augmented reality applications in multi-access edge computing. IEEE Transactions on Vehicular Technology, 69 (11), 13631–13645. https://doi.org/10.1109/TVT.2020.3020982

Qiao, X., Ren, P., Dustdar, S., Liu, L., Ma, H., & Chen, J. (2019). Web AR: A promising future for mobile augmented reality—State of the art, challenges, and insights. Proceedings of the IEEE, 107 (4), 651–666. https://doi.org/10.1109/JPROC.2019.2895105

Hou, W., Ning, Z., & Guo, L. (2018). Green survivable collaborative edge computing in smart cities. IEEE Transactions on Industrial Informatics, 14 (4), 1594–1605. https://doi.org/10.1109/TII.2018.2797922

Yu, B., Zhang, X., You, I., & Khan, U. S. (2021). Efficient computation offloading in edge computing enabled smart home. IEEE Access, 9 , 48631–48639. https://doi.org/10.1109/ACCESS.2021.3066789

Deng, Y., Chen, Z., Yao, X., Hassan, S., & Wu, J. (2019). Task scheduling for smart city applications based on multi-server mobile edge computing. IEEE Access, 7 , 14410–14421. https://doi.org/10.1109/ACCESS.2019.2893486

Liu, Y., Yang, C., Jiang, L., Xie, S., & Zhang, Y. (2019). Intelligent edge computing for IoT-based energy management in smart cities. IEEE Network, 33 (2), 111–117. https://doi.org/10.1109/MNET.2019.1800254

Khan, L. U., Yaqoob, I., Tran, N. H., Kazmi, S. M. A., Dang, T. N., & Hong, C. S. (2020). Edge-computing-enabled smart cities: A comprehensive survey. IEEE Internet of Things Journal, 7 (10), 10200–10232. https://doi.org/10.1109/JIOT.2020.2987070

Cui, J., Wei, L., Zhong, H., Zhang, J., Xu, Y., & Liu, L. (2020). Edge computing in VANETs—An efficient and privacy-preserving cooperative downloading scheme. IEEE Journal on Selected Areas in Communications, 38 (6), 1191–1204. https://doi.org/10.1109/JSAC.2020.2986617

Huang, C.-M., & Lai, C.-F. (2020). The delay-constrained and network-situation-aware V2V2I VANET data offloading based on the multi-access edge computing (MEC) architecture. IEEE Open Journal of Vehicular Technology, 1 , 331–347. https://doi.org/10.1109/OJVT.2020.3028684

Deng, Z., Cai, Z., & Liang, M. (2020). A multi-hop VANETs-assisted offloading strategy in vehicular mobile edge computing. IEEE Access, 8 , 53062–53071. https://doi.org/10.1109/ACCESS.2020.2981501

Cui, J., Wei, L., Zhang, J., Xu, Y., & Zhong, H. (2019). An efficient message-authentication scheme based on edge computing for vehicular ad hoc networks. IEEE Transactions on Intelligent Transportation Systems, 20 (5), 1621–1632. https://doi.org/10.1109/TITS.2018.2827460

Li, J., et al. (2020). A secured framework for SDN-based edge computing in IoT-enabled healthcare system. IEEE Access, 8 , 135479–135490. https://doi.org/10.1109/ACCESS.2020.3011503

Abdellatif, et al. (2021). MEdge-chain: Leveraging edge computing and blockchain for efficient medical data exchange. IEEE Internet of Things Journal . https://doi.org/10.1109/JIOT.2021.3052910

Alabdulatif, Khalil, I., Yi, X., & Guizani, M. (2019). Secure edge of things for smart healthcare surveillance framework. IEEE Access, 7 , 31010–31021. https://doi.org/10.1109/ACCESS.2019.2899323

Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A. (2019). An edge-based architecture to support efficient applications for healthcare industry 4.0. IEEE Transactions on Industrial Informatics, 15 (1), 481–489. https://doi.org/10.1109/TII.2018.2843169

Amin, S. U., & Hossain, M. S. (2021). Edge intelligence and internet of things in healthcare: A survey. IEEE Access, 9 , 45–59. https://doi.org/10.1109/ACCESS.2020.3045115

Usman, M., Jolfaei, A., & Jan, M. A. (2020). RaSEC: An intelligent framework for reliable and secure multilevel edge computing in industrial environments. IEEE Transactions on Industry Applications, 56 (4), 4543–4551. https://doi.org/10.1109/TIA.2020.2975488

Jiang, C., Wan, J., & Abbas, H. (2021). An edge computing node deployment method based on improved k-means clustering algorithm for smart manufacturing. IEEE Systems Journal, 15 (2), 2230–2240. https://doi.org/10.1109/JSYST.2020.2986649

Qi, Q., & Tao, F. (2019). A smart manufacturing service system based on edge computing, fog computing, and cloud computing. IEEE Access, 7 , 86769–86777. https://doi.org/10.1109/ACCESS.2019.2923610

Li, X., Wan, J., Dai, H., Imran, M., Xia, M., & Celesti, A. (2019). A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing. IEEE Transactions on Industrial Informatics, 15 (7), 4225–4234. https://doi.org/10.1109/TII.2019.2899679

Lee, K. M., Huo, Y. Z., Zhang, S. Z., & Ng, K. K. H. (2020). Design of a smart manufacturing system with the application of multi-access edge computing and blockchain technology. IEEE Access, 8 , 28659–28667. https://doi.org/10.1109/ACCESS.2020.2972284

Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., & Wu, D. O. (2020). Edge computing in industrial Internet of Things: Architecture, advances and challenges. IEEE Communications Surveys & Tutorials, 22 (4), 2462–2488. https://doi.org/10.1109/COMST.2020.3009103

Wang, J., Cao, C., Wang, J., Lu, K., Jukan, A., & Zhao, W. (2021). Optimal task allocation and coding design for secure edge computing with heterogeneous edge devices. IEEE Transactions on Cloud Computing . https://doi.org/10.1109/TCC.2021.3050012

Li, K. (2019). Computation offloading strategy optimisation with multiple heterogeneous servers in mobile edge computing. IEEE Transactions on Sustainable Computing . https://doi.org/10.1109/TSUSC.2019.2904680

Chen, X., Li, W., Lu, S., Zhou, Z., & Fu, X. (2018). Efficient resource allocation for on-demand mobile-edge cloud computing. IEEE Transactions on Vehicular Technology, 67 (9), 8769–8780. https://doi.org/10.1109/TVT.2018.2846232

Zhao, J., Li, Q., Gong, Y., & Zhang, K. (2019). Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks. IEEE Transactions on Vehicular Technology, 68 (8), 7944–7956. https://doi.org/10.1109/TVT.2019.2917890

Zhang, P., Zhang, Y., Dong, H., & Jin, H. (2021). Mobility and dependence-aware QoS monitoring in mobile edge computing. IEEE Transactions on Cloud Computing . https://doi.org/10.1109/TCC.2021.3063050

Li, J., Li, X., Gao, Y., Gao, Y., & Zhang, R. (2017). Dynamic cloudlet-assisted energy-saving routing mechanism for mobile ad hoc networks. IEEE Access, 5 , 20908–20920. https://doi.org/10.1109/ACCESS.2017.2759138

He, X., Jin, R., & Dai, H. (2020). Physical-layer assisted secure offloading in mobile-edge computing. IEEE Transactions on Wireless Communications, 19 (6), 4054–4066. https://doi.org/10.1109/TWC.2020.2979456

Xu, X., Huang, Q., Yin, X., Abbasi, M., Khosravi, M. R., & Qi, L. (2020). Intelligent offloading for collaborative smart city services in edge computing. IEEE Internet of Things Journal, 7 (9), 7919–7927. https://doi.org/10.1109/JIOT.2020.3000871

Ni, J., Lin, X., & Shen, X. S. (2019). Toward edge-assisted internet of things: From security and efficiency perspectives. IEEE Network, 33 (2), 50–57. https://doi.org/10.1109/MNET.2019.1800229

Xiao, Y., Jia, Y., Liu, C., Cheng, X., Yu, J., & Lv, W. (2019). Edge computing security: State of the art and challenges. Proceedings of the IEEE, 107 (8), 1608–1631. https://doi.org/10.1109/JPROC.2019.2918437

Quy, V. K., Nam, V. H., Linh, D. M., et al. (2021). A survey of QoS-aware routing protocols for the MANET-WSN convergence scenarios in IoT networks. Wireless Personal Communications . https://doi.org/10.1007/s11277-021-08433-z

Tseng, L., Wong, L., Otoum, S., Aloqaily, M., & Othman, J. B. (2020). Blockchain for managing heterogeneous internet of things: A perspective architecture. IEEE Network, 34 (1), 16–23. https://doi.org/10.1109/MNET.001.1900103

Download references

Acknowledgements

The authors thank sincerely Prof. Isaac Woungang and Prof. Abdellah Chehri for their valuable contributions and comments on this research.

The authors have not disclosed any funding.

Author information

Authors and affiliations.

Hung Yen University of Technology and Education, Hung Yen, 160000, Vietnam

Nguyen Minh Quy, Nguyen Van Hau & Vu Khanh Quy

Swinburne Vietnam, FPT University, Hanoi, 100000, Vietnam

Le Anh Ngoc

Posts and Telecommunications Institute of Technology, Hanoi, 100000, Vietnam

Nguyen Tien Ban

You can also search for this author in PubMed   Google Scholar

Contributions

N.M. Quy and V.KQ have performed the study conception and deployment. Data collection and analysis were performed by NMQ, LAN, NTB, NVH and VKQ. The first draft of the manuscript was written by NMQ and VKQ. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. The author corresponding is VKQ.

Corresponding author

Correspondence to Vu Khanh Quy .

Ethics declarations

Conflict of interest.

The authors declare no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Acronyms used in this paper

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Quy, N.M., Ngoc, L.A., Ban, N.T. et al. Edge Computing for Real-Time Internet of Things Applications: Future Internet Revolution. Wireless Pers Commun 132 , 1423–1452 (2023). https://doi.org/10.1007/s11277-023-10669-w

Download citation

Accepted : 13 July 2023

Published : 26 July 2023

Issue Date : September 2023

DOI : https://doi.org/10.1007/s11277-023-10669-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Edge computing
• Cloud computing
• Internet of Things
• Real-time applications
• Find a journal
• Publish with us
• Track your research

• Keynote and Panel
• Proceedings
• --> Special Events --> --> Poster Session --> PhD Forum --> Women in Computing --> -->
• Poster Session
• Camera Ready
• Submissions
• Student Travel Grant

Call for Papers

• Call for Journal Paper Presentations
• Submission Instructions
• Call for Posters/Demos

EdgeSP: The Fifth ACM/IEEE Workshop on Security and Privacy in Edge Computing

As the technologies of edge computing continue to advance, new security and privacy challenges and opportunities emerge, leading to the flourishing research on and fast development of various security solutions. The aim of this workshop is to provide a platform for researchers and practitioners to present advances in security and privacy enhancing technologies in the emerging field of edge computing. The workshop will be co-located with the Seventh ACM/IEEE Symposium on Edge Computing (SEC), December 5-8, 2022, in Seattle, WA, USA.

All papers presented at the workshop will be included in the IEEE Digital Library. Extended versions of accepted papers will be recommended for publication in Elsevier High-Confidence Computing.

We invite authors to submit previously unpublished papers offering novel research contributions addressing security and privacy issues in edge computing. Topics include, but are not limited to:

• Attacks and defences
• Authentication
• Side-channel attacks and defences for edge computing
• Intrusion detection and prevention
• Key Management for ddge computing
• Mobile and Web security and privacy
• IoT security and privacy on edge computing
• Network and systems security
• Privacy technologies and mechanisms
• Protocol security
• Secure information flow
• Security and privacy metrics
• Security and privacy policies
• Security architectures
• Security in content delivery
• Usable security and privacy

Important Dates

Deadline for Submission: extended ): --> September 25, 2022 August 20, 2020, 11:59pm AoE August 16, 2020, 11:59pm AoE extended ) -->

Notifications of Acceptance: October 5, 2022

Camera-ready Paper Submission: October 14, 2022 (firm)

Workshop: December 8, 2022

Workshop Organizers

Guoming Zhang , Shandong University

Hong Li , Institute of Information Engineering, Chinese Academy of Sciences

Workshop Co-Chairs

Zizhan Zheng , Tulane University

Lei Ding , American University

Hao Fu , Facebook, Inc.

Pengfei Hu , VMWare Inc

Riccardo Spolaor , University of Oxford

Publicity Chair

Yunze Zeng , Bosch Research

Technical Program Committee

PLACE HOLDER , Institution

Tianbo Gu , Microsoft

Xiang Chen , George Mason University

Xin Chen , DiDi Labs

Tianbo Gu , University of California, Davis

Hao Han , Nanjing University of Aeronautics & Astronautics

Chunguo Li , Southeast University, China

Wei Li , Georgia State University

Boxiang Dong , Montclair State University

Chhagan Lal , University of Padua

Riccardo Lazzeretti , Sapienza University of Rome

Mimi Xie , UT San Antonio

Instructions for authors

Papers describing timely research contributions in EdgeSP’s areas of interest are solicited. Papers reporting on initial results as well as papers discussing mature research projects or case studies of deployed systems are sought out. Submissions describing big ideas that may have significant impact and could lead to interesting discussions at the workshop are encouraged.

Submitted papers must be neither previously published nor under review by another workshop, conference or journal. Only electronic submissions in PDF will be accepted. Submitted papers must be no longer than 6 single-spaced 8.5" x 11" pages, including figures and tables, but excluding references, and using 10-point type on 12-point (single-spaced) leading, two-column format, Times Roman, or a similar font, within a text block 7.14" wide x 9.22" deep. IEEE Standard template for Latex and Word meet these specifications and can be found at: https://www.ieee.org/conferences/publishing/templates.html. Papers not meeting these criteria will be rejected without review, and no deadline extensions will be granted for reformatting. Pages should be numbered, and figures and tables should be legible in black and white, without requiring magnification. Papers so short as to be considered "extended abstracts" will not receive full consideration. Papers must be submitted to https://easychair.org/conferences/?conf=edgesp2022

At least one of the authors of each paper accepted for presentation in EdgeSP 2022 must register for the workshop.

Questions? Contact workshop co-chairs at zzheng3 [at] tulane.edu or ding [at] american.edu.

Navigation bar

In Cooperation

Conference Sponsorship

Diamond Sponsors

Platinum Sponsors

Gold Sponsors

Silver Sponsors

Bronze Sponsors