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

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Abstract

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.

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References

  1. 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 

  2. 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

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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

    Article  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. 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

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. 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

    Article  Google Scholar 

  17. 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

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. 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

    Article  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. 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

    Article  Google Scholar 

  24. 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

    Article  Google Scholar 

  25. 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

    Article  Google Scholar 

  26. 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

    Article  Google Scholar 

  27. 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

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. 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

    Article  Google Scholar 

  30. 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

    Article  Google Scholar 

  31. 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

    Article  Google Scholar 

  32. 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

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. 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

    Article  Google Scholar 

  35. 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

    Article  Google Scholar 

  36. 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

    Article  Google Scholar 

  37. 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

    Article  Google Scholar 

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

  39. 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

    Article  Google Scholar 

  40. 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

    Article  Google Scholar 

  41. 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

    Article  Google Scholar 

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

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

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

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

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

  47. 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

    Article  Google Scholar 

  48. 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

    Article  Google Scholar 

  49. 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

    Article  Google Scholar 

  50. 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

    Article  Google Scholar 

  51. 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

    Article  Google Scholar 

  52. 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

    Article  Google Scholar 

  53. 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

    Article  Google Scholar 

  54. 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

    Article  Google Scholar 

  55. 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

    Article  Google Scholar 

  56. 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

    Article  Google Scholar 

  57. 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

    Article  Google Scholar 

  58. 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

    Article  Google Scholar 

  59. 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

    Article  Google Scholar 

  60. 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

    Article  Google Scholar 

  61. 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

    Article  Google Scholar 

  62. 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

    Article  Google Scholar 

  63. 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

    Article  Google Scholar 

  64. 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

    Article  Google Scholar 

  65. 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

    Article  Google Scholar 

  66. 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

    Article  Google Scholar 

  67. 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

    Article  Google Scholar 

  68. 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

    Article  Google Scholar 

  69. 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

    Article  Google Scholar 

  70. 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

    Article  Google Scholar 

  71. 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

    Article  Google Scholar 

  72. 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

    Article  Google Scholar 

  73. 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

    Article  Google Scholar 

  74. 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

    Article  Google Scholar 

  75. 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

    Article  Google Scholar 

  76. 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

    Article  Google Scholar 

  77. 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

    Article  Google Scholar 

  78. 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

    Article  Google Scholar 

  79. 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

    Article  Google Scholar 

  80. 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

    Article  Google Scholar 

  81. 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

    Article  Google Scholar 

  82. 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

    Article  Google Scholar 

  83. 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

    Article  Google Scholar 

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Acknowledgements

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

Funding

The authors have not disclosed any funding.

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Authors and Affiliations

Authors

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.

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Appendix

Appendix

Acronyms used in this paper

Acronym

Meaning

Acronym

Meaning

AR

Augmented reality

EG

Edge computing

AI

Artificial intelligence

M2M

Mechanism to mechanism

AODV

Ad-hoc on-demand distance vector

MANET

Mobile ad hoc networks

API

Application programming interface

MCC

Multi-cloud computing

D2D

Device to device

MEC

Mobile edge computing

DDoS

Distributed denial of service

PHM

Prognostics and health management

DoS

Denial of service

QoS

Quality of service

IIoT

Industrial Internet of Things

RSU

Road side unit

CC

Cloud computing

SaaS

Software as a service

GIS

Geographic information systems

SDN

Software-defined networking

GPRS

General packet radio service

FC

Fog computing

GPS

Global positioning system

UAV

Unmanned aerial vehicle

IaaS

Infrastructure as a service

V2I

Vehicle to infrastructure

IoT

Internet of Things

V2V

Vehicle to vehicle

IoVs

Internet of vehicles

VANET

Vehicular ad hoc networks

EC

Edge computing

  

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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

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

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