skip to main content
research-article

AI-enabled IoT-Edge Data Analytics for Connected Living

Authors Info & Claims
Published:16 July 2021Publication History
Skip Abstract Section

Abstract

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

References

  1. M. Du, K. Wang, Y. Chen, et al. 2018. Big data privacy preserving in multi-access edge computing for heterogeneous Internet of Things. IEEE Commun. Mag. 56, 8 (2018), 62–67. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. T. Wang, L. Qiu, A. K. Sangaiah, et al. 2020. Edge-computing-based trustworthy data collection model in the internet of things. IEEE Internet Things J. 7, 5 (2020), 4218–4227.Google ScholarGoogle ScholarCross RefCross Ref
  3. K. Kaur, S. Garg, G. S. Aujla, et al. 2018. Edge computing in the industrial internet of things environment: Software-defined-networks-based edge-cloud interplay. IEEE Commun. Mag. 56, 2 (2018), 44–51. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. P. Porambage, J. Okwuibe, M. Liyanage, et al. 2018. Survey on multi-access edge computing for internet of things realization. IEEE Commun. Surveys Tutor. 20, 4 (2018), 2961–2991.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. X. Li, D. Li, J. Wan, et al. 2018. Adaptive transmission optimization in SDN-based industrial internet of things with edge computing. IEEE Internet Things J. 5, 3 (2018), 1351–1360.Google ScholarGoogle Scholar
  6. J. Pan and J. McElhannon. 2017. Future edge cloud and edge computing for internet of things applications. IEEE Internet Things J. 5, 1 (2017), 439–449.Google ScholarGoogle ScholarCross RefCross Ref
  7. D. Puthal, S. Nepal, R. Ranjan, et al. 2016. Threats to networking cloud and edge datacenters in the Internet of Things. IEEE Cloud Comput. 3, 3 (2016), 64–71.Google ScholarGoogle ScholarCross RefCross Ref
  8. M. B. Mollah, M. A. K. Azad, and A. Vasilakos. 2017. Secure data sharing and searching at the edge of cloud-assisted internet of things. IEEE Cloud Comput. 4, 1 (2017), 34–42.Google ScholarGoogle ScholarCross RefCross Ref
  9. G. Mitsis, E. E. Tsiropoulou, and S. Papavassiliou. 2020. Data offloading in UAV-assisted multi-access edge computing systems: A resource-based pricing and user risk-awareness approach. Sensors 20, 8 (2020), 2434.Google ScholarGoogle ScholarCross RefCross Ref
  10. H. Gupta, A. Vahid Dastjerdi, S. K. Ghosh, et al. 2017. iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, edge and fog computing environments. Softw.: Pract. Exper. 47, 9 (2017), 1275–1296.Google ScholarGoogle ScholarCross RefCross Ref
  11. R. Ranjan, O. Rana, S. Nepal, et al. 2018. The next grand challenges: Integrating the internet of things and data science. IEEE Cloud Comput. 5, 3 (2018), 12–26.Google ScholarGoogle ScholarCross RefCross Ref
  12. H. Khelifi, S. Luo, B. Nour, et al. 2018. Bringing deep learning at the edge of information-centric internet of things. IEEE Communications Letters 23, 1 (2018), 52–55.Google ScholarGoogle ScholarCross RefCross Ref
  13. D. Sabella, A. Vaillant, P. Kuure, et al. 2016. Mobile-edge computing architecture: The role of MEC in the Internet of Things. IEEE Consum. Electron. Mag. 5, 4 (2016), 84–91.Google ScholarGoogle ScholarCross RefCross Ref
  14. G. C. Nobre and E. Tavares. 2017. Scientific literature analysis on big data and internet of things applications on circular economy: A bibliometric study. Scientometrics 111, 1 (2017), 463–492. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. R. Li, T. Song, B. Mei, et al. 2018. Blockchain for large-scale internet of things data storage and protection. IEEE Trans. Services Comput. 12, 5 (2018), 762–771.Google ScholarGoogle ScholarCross RefCross Ref
  16. F. Lin, Y. Zhou, X. An, et al. 2018. Fair resource allocation in an intrusion-detection system for edge computing: Ensuring the security of Internet of Things devices. IEEE Consum. Electron. Mag. 7, 6 (2018), 45–50.Google ScholarGoogle ScholarCross RefCross Ref
  17. C. H. Chen, M. Y. Lin, and C. C. Liu. 2018. Edge computing gateway of the industrial internet of things using multiple collaborative microcontrollers. IEEE Netw. 32, 1 (2018), 24–32.Google ScholarGoogle Scholar
  18. E. Fitzgerald, M. Pióro, and A. Tomaszewski. 2018. Energy-optimal data aggregation and dissemination for the Internet of Things. IEEE Internet Things J. 5, 2 (2018), 955–969.Google ScholarGoogle ScholarCross RefCross Ref
  19. Z. Ning, P. Dong, X. Kong, et al. 2018. A cooperative partial computation offloading scheme for mobile edge computing enabled Internet of Things. IEEE Internet Things J. 6, 3 (2018), 4804–4814.Google ScholarGoogle ScholarCross RefCross Ref
  20. A. Munir, P. Kansakar, S. U. Khan. 2017. IFCIoT: Integrated fog cloud IoT: A novel architectural paradigm for the future Internet of Things. IEEE Consum. Electr. Mag. 6, 3 (2017), 74–82.Google ScholarGoogle ScholarCross RefCross Ref
  21. T. Adegbija, A. Rogacs, C. Patel, et al. 2017. Microprocessor optimizations for the internet of things: A survey. IEEE Trans. Comput.-Aided Design Integr. Circ. Syst. 37, 1 (2017), 7–20.Google ScholarGoogle ScholarCross RefCross Ref
  22. Z. Xiong, Y. Zhang, N. C. Luong, et al. 2020. The best of both worlds: A general architecture for data management in blockchain-enabled Internet-of-Things. IEEE Netw. 34, 1 (2020), 166–173.Google ScholarGoogle ScholarCross RefCross Ref
  23. M. Abbasi, E. M. Pasand, and M. R. Khosravi. 2020. Workload allocation in IoT-fog-cloud architecture using a multi-objective genetic algorithm. J. Grid Comput. 18 (2020), 43–56.Google ScholarGoogle Scholar
  24. J. Lin, W. Yu, N. Zhang, et al. 2017. A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4, 5 (2017), 1125–1142.Google ScholarGoogle ScholarCross RefCross Ref
  25. Y. Zhang, H. Huang, L. X. Yang, et al. 2019. Serious challenges and potential solutions for the industrial Internet of Things with edge intelligence. IEEE Netw. 33, 5 (2019), 41–45.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Q. Huang, Y. Yang, and L. Wang. 2017. Secure data access control with ciphertext update and computation outsourcing in fog computing for Internet of Things. IEEE Access 5 (2017), 12941–12950.Google ScholarGoogle ScholarCross RefCross Ref
  27. Y. Chen, N. Zhang, Y. Zhang, et al. 2018. Dynamic computation offloading in edge computing for Internet of Things. IEEE Internet Things J. 6, 3 (2018), 4242–4251.Google ScholarGoogle ScholarCross RefCross Ref
  28. G. Jia, G. Han, H. Xie, et al. 2018. Hybrid-LRU caching for optimizing data storage and retrieval in edge computing-based wearable sensors. IEEE Internet Things J. 6, 2 (2018), 1342–1351.Google ScholarGoogle ScholarCross RefCross Ref
  29. O. Elijah, T. A. Rahman, I. Orikumhi, et al. 2018. An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet Things J. 5, 5 (2018), 3758–3773.Google ScholarGoogle Scholar
  30. W. Chen, Z. Zhang, Z. Hong, et al. 2019. Cooperative and distributed computation offloading for blockchain-empowered industrial Internet of Things. IEEE Internet Things J. 6, 5 (2019), 8433–8446.Google ScholarGoogle ScholarCross RefCross Ref
  31. W. Wang, Q. Wang, and K. Sohraby. 2016. Multimedia sensing as a service (MSaaS): Exploring resource saving potentials of at cloud-edge IoT and fogs. IEEE Internet Things J. 4, 2 (2016), 487–495.Google ScholarGoogle Scholar
  32. G. Li, J. He, S. Peng, et al. 2018. Energy efficient data collection in large-scale internet of things via computation offloading. IEEE Internet Things J. 6, 3 (2018), 4176–4187.Google ScholarGoogle ScholarCross RefCross Ref
  33. G. Li, J. Wu, J. Li, et al. 2018. Service popularity-based smart resources partitioning for fog computing-enabled industrial Internet of Things. IEEE Trans. Industr. Inform. 14, 10 (2018), 4702–4711.Google ScholarGoogle ScholarCross RefCross Ref
  34. B. Du, R. Huang, Z. Xie, et al. 2018. KID model-driven things-edge-cloud computing paradigm for traffic data as a service. IEEE Netw. 32, 1 (2018), 34–41.Google ScholarGoogle ScholarCross RefCross Ref
  35. Z. Lv and H. Song. 2019. Mobile internet of things under data physical fusion technology. IEEE Internet Things J. 7, 5 (Nov. 2019), 4616–4624.Google ScholarGoogle Scholar
  36. R. Shi, Y. Gan, and Y. Wang. 2018. Evaluating scalability bottlenecks by workload extrapolation. In Proceedings of the IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS’18). IEEE, 333–347.Google ScholarGoogle Scholar
  37. Z. Lv and W. Xiu. 2019. Interaction of edge-cloud computing based on SDN and NFV for next generation IoT. IEEE Internet Things J. 7, 7 (Oct. 2019), 5706–5712.Google ScholarGoogle Scholar
  38. S. Dang, O. Amin, B. Shihada, and M. S. Alouini. 2020. What should 6G be? Nature Electron. 3, 1 (2020), 20–29.Google ScholarGoogle ScholarCross RefCross Ref
  39. Z. Lv, X. Li, H. Lv, and W. Xiu. 2019. BIM big data storage in WebVRGIS. IEEE Trans. Industr. Inform. 16, 4 (May 2019), 2566–2573.Google ScholarGoogle Scholar
  40. A. Zhou, S. Wang, S. Wan, and L. Qi. 2020. LMM: Latency-aware micro-service mashup in mobile edge computing environment. Neural Comput. Appl. 1–15, 2020.Google ScholarGoogle Scholar
  41. S. Wan, Z. Gu, and Q. Ni. 2020. Cognitive computing and wireless communications on the edge for healthcare service robots. Comput. Commun. 149 (2020), 99–106.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. S. Yang, B. Deng, J. Wang, H. Li, M. Lu, Y. Che, X. Wei, and K. A. Loparo. 2019. Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons. IEEE Trans. Neural Netw. Learn. Syst. 31, 1 (Mar. 2019), 148–62.Google ScholarGoogle Scholar
  43. S. Wan, Y. Xia, L. Qi, Y. H. Yang, and M. Atiquzzaman. 2020. Automated colorization of a grayscale image with seed points propagation. IEEE Trans. Multimedia 22, 7 (2020), 1756–1768.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. AI-enabled IoT-Edge Data Analytics for Connected Living

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          • Published in

            cover image ACM Transactions on Internet Technology
            ACM Transactions on Internet Technology  Volume 21, Issue 4
            November 2021
            520 pages
            ISSN:1533-5399
            EISSN:1557-6051
            DOI:10.1145/3472282
            • Editor:
            • Ling Lu
            Issue’s Table of Contents

            Copyright © 2021 Association for Computing Machinery.

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 16 July 2021
            • Revised: 1 August 2020
            • Accepted: 1 August 2020
            • Received: 1 July 2020
            Published in toit Volume 21, Issue 4

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Refereed

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format .

          View HTML Format
          About Cookies On This Site

          We use cookies to ensure that we give you the best experience on our website.

          Learn more

          Got it!