Edge Computing for Real-Time Internet of Things Applications: Future Internet Revolution (original) (raw)

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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