skip to main content
research-article

An LSH-based Offloading Method for IoMT Services in Integrated Cloud-Edge Environment

Authors Info & Claims
Published:08 January 2021Publication History
Skip Abstract Section

Abstract

Benefiting from the massive available data provided by Internet of multimedia things (IoMT), enormous intelligent services requiring information of various types to make decisions are emerging. Generally, the IoMT devices are equipped with limited computing power, interfering with the process of computation-intensive services. Currently, to satisfy a wide range of service requirements, the novel computing paradigms, i.e., cloud computing and edge computing, can potentially be integrated for service accommodation. Nevertheless, the private information (i.e., location, service type, etc.) in the services is prone to spilling out during service offloading in the cloud-edge computing. To avoid privacy leakage while improving service utility, including the service response time and energy consumption for service executions, a <underline>L</underline>ocality-sensitive-hash (LSH)-based <underline>o</underline>ffloading <underline>m</underline>ethod, named LOM, is devised. Specifically, LSH is leveraged to encrypt the feature information for the services offloaded to the edge servers with the intention of privacy preservation. Eventually, comparative experiments are conducted to verify the effectiveness of LOM with respect to promoting service utility.

References

  1. S. He and W. Wang. 2020. Multimedia upstreaming Cournot game in non-orthogonal multiple access Internet of Things. IEEE Trans. Netw. Sci. Eng. 7, 1 (2020), 398--408.Google ScholarGoogle ScholarCross RefCross Ref
  2. Z. Zhang, R. Sun, X. Wang, and C. Zhao. 2019. A situational analytic method for user behavior pattern in multimedia social networks. IEEE Trans. Big Data 5, 4 (2019), 520--528.Google ScholarGoogle ScholarCross RefCross Ref
  3. X. Zhou, W. Liang, K. I. Wang, H. Wang, L. T. Yang, and Q. Jin. 2020. Deep learning enhanced human activity recognition for Internet of Healthcare Things. IEEE Internet Things J. (2020), 1--1.Google ScholarGoogle Scholar
  4. C. Singhal, C. F. Chiasserini, and C. E. Casetti. 2019. EMB: Efficient multimedia broadcast in multi-tier mobile networks. IEEE Trans. Vehic. Technol. 68, 11 (2019), 11186--11199.Google ScholarGoogle ScholarCross RefCross Ref
  5. X. Zhou, W. Liang, K. I. Wang, and S. Shimizu. 2019. Multi-modality behavioral influence analysis for personalized recommendations in health social media environment. IEEE Trans. Comput. Soc. Syst. 6, 5 (2019), 888--897.Google ScholarGoogle ScholarCross RefCross Ref
  6. J. Shen, T. Zhou, D. He, Y. Zhang, X. Sun, and Y. Xiang. 2019. Block design-based key agreement for group data sharing in cloud computing. IEEE Trans. Depend. Secure Comput. 16, 6 (2019), 996--1010.Google ScholarGoogle ScholarCross RefCross Ref
  7. G. Skourletopoulos, C. X. Mavromoustakis, G. Mastorakis, J. M. Batalla, H. Song, J. N. Sahalos, and E. Pallis. 2019. Elasticity debt analytics exploitation for green mobile cloud computing: An equilibrium model. IEEE Trans. Green Commun. Netw. 3, 1 (2019), 122--131.Google ScholarGoogle ScholarCross RefCross Ref
  8. J. Son and R. Buyya. 2019. Priority-aware VM allocation and network bandwidth provisioning in software-defined networking (SDN)-enabled clouds. IEEE Trans. Sustain. Comput. 4, 1 (2019), 17--28.Google ScholarGoogle ScholarCross RefCross Ref
  9. L. Wang, L. Jiao, J. Li, J. Gedeon, and M. Muhlhauser. 2019. MOERA: Mobility-agnostic online resource allocation for edge computing. IEEE Trans. Mobile Comput. 18, 8 (2019), 1843--1856.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. X. Zhou, W. Liang, K. I. Wang, R. Huang, and Q. Jin. 2018. Academic influence aware and multidimensional network analysis for research collaboration navigation based on scholarly big data. IEEE Trans. Emerg. Topics Comput. (2018), 1--1.Google ScholarGoogle Scholar
  11. Z. Tian, W. Shi, Y. Wang, C. Zhu, X. Du, S. Su, Y. Sun, and N. Guizani. 2019. Real-time lateral movement detection based on evidence reasoning network for edge computing environment. IEEE Trans. Industr. Info. 15, 7 (2019), 4285--4294.Google ScholarGoogle ScholarCross RefCross Ref
  12. E. Li, L. Zeng, Z. Zhou, and X. Chen. 2020. Edge AI: On-demand accelerating deep neural network inference via edge computing. IEEE Trans. Wireless Commun. 19, 1 (2020), 447--457.Google ScholarGoogle ScholarCross RefCross Ref
  13. Y. Zhu, Q. He, J. Liu, B. Li, and Y. Hu. 2020. When crowd meets big video data: Cloud-edge collaborative transcoding for personal livecast. IEEE Trans. Netw. Sci. Eng. 7, 1 (2020), 42--53.Google ScholarGoogle ScholarCross RefCross Ref
  14. X. Hu, L. Wang, K. Wong, M. Tao, Y. Zhang, and Z. Zheng. 2020. Edge and central cloud computing: A perfect pairing for high energy efficiency and low-latency. IEEE Trans. Wireless Commun. 19, 2 (2020), 1070--1083.Google ScholarGoogle ScholarCross RefCross Ref
  15. M. Jia, Z. Yin, D. Li, Q. Guo, and X. Gu. 2019. Toward improved offloading efficiency of data transmission in the IoT-cloud by leveraging secure truncating OFDM. IEEE Internet Things J. 6, 3 (2019), 4252--4261.Google ScholarGoogle ScholarCross RefCross Ref
  16. Q. He, B. Li, F. Chen, J. Grundy, X. Xia, and Y. Yang. 2020. Diversified third-party library prediction for mobile app development. IEEE Trans. Softw. Eng. (2020), 1--1.Google ScholarGoogle Scholar
  17. S. Han, X. Xu, S. Fang, Y. Sun, Y. Cao, X. Tao, and P. Zhang. 2019. Energy efficient secure computation offloading in NOMA-based mMTC networks for IoT. IEEE Internet Things J. 6, 3 (2019), 5674--5690.Google ScholarGoogle ScholarCross RefCross Ref
  18. M. A. Jan, M. Usman, X. He, and A. Ur Rehman. 2019. SAMS: A seamless and authorized multimedia streaming framework for WMSN-based IoMT. IEEE Internet Things J. 6, 2 (2019), 1576--1583.Google ScholarGoogle ScholarCross RefCross Ref
  19. K. Thiyagarajan, R. Lu, K. El-Sankary, and H. Zhu. 2019. Energy-aware encryption for securing video transmission in Internet of Multimedia Things. IEEE Trans. Circ. Syst. Video Technol. 29, 3 (2019), 610--624.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. Ren, G. Yu, Y. He, and G. Y. Li. 2019. Collaborative cloud and edge computing for latency minimization. IEEE Trans. Vehic. Technol. 68, 5 (2019), 5031--5044.Google ScholarGoogle ScholarCross RefCross Ref
  21. B. Lin, F. Zhu, J. Zhang, J. Chen, X. Chen, N. N. Xiong, and J. Lloret Mauri. 2019. A time-driven data placement strategy for a scientific workflow combining edge computing and cloud computing. IEEE Trans. Industr. Info. 15, 7 (2019), 4254--4265.Google ScholarGoogle ScholarCross RefCross Ref
  22. F. Hao, D. Park, J. Kang, and G. Min. 2019. 2L-MC3: A two-layer multi-community-cloud/cloudlet social collaborative paradigm for mobile edge computing. IEEE Internet Things J. 6, 3 (2019), 4764--4773.Google ScholarGoogle ScholarCross RefCross Ref
  23. L. Ruan, Y. Yan, S. Guo, F. Wen, and X. Qiu. 2020. Priority-based residential energy management with collaborative edge and cloud computing. IEEE Trans. Industr. Info. 16, 3 (2020), 1848--1857.Google ScholarGoogle ScholarCross RefCross Ref
  24. M. Thai, Y. Lin, Y. Lai, and H. Chien. 2020. Workload and capacity optimization for cloud-edge computing systems with vertical and horizontal offloading. IEEE Trans. Netw. Service Manage. 17, 1 (2020), 227--238.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Z. Hong, W. Chen, H. Huang, S. Guo, and Z. Zheng. 2019. Multi-hop cooperative computation offloading for industrial IoT--Edge--Cloud computing environments. IEEE Trans. Parallel Distrib. Syst. 30, 12 (2019), 2759--2774.Google ScholarGoogle ScholarCross RefCross Ref
  26. X. He, R. Jin, and H. Dai. 2019. Deep PDS-learning for privacy-aware offloading in MEC-enabled IoT. IEEE Internet Things J. 6, 3 (2019), 4547--4555.Google ScholarGoogle ScholarCross RefCross Ref
  27. X. He, R. Jin, and H. Dai. 2020. Peace: Privacy-preserving and cost-efficient task offloading for mobile-edge computing. IEEE Trans. Wireless Commun. 19, 3 (2020), 1814--1824.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. X. Xu, C. He, Z. Xu, L. Qi, S. Wan, and M. Z. A. Bhuiyan. 2020. Joint optimization of offloading utility and privacy for edge computing enabled IoT. IEEE Internet Things J. 7, 4 (2020), 2622--2629.Google ScholarGoogle ScholarCross RefCross Ref
  29. M. Min, X. Wan, L. Xiao, Y. Chen, M. Xia, D. Wu, and H. Dai. 2019. Learning-based privacy-aware offloading for healthcare IoT with energy harvesting. IEEE Internet Things J. 6, 3 (2019), 4307--4316.Google ScholarGoogle ScholarCross RefCross Ref
  30. T. Bai, J. Wang, Y. Ren, and L. Hanzo. 2019. Energy-efficient computation offloading for secure UAV-edge-computing systems. IEEE Trans. Vehic. Technol. 68, 6 (2019), 6074--6087.Google ScholarGoogle ScholarCross RefCross Ref
  31. W. Hu, Y. Fan, J. Xing, L. Sun, Z. Cai, and S. Maybank. 2018. Deep constrained siamese hash coding network and load-balanced locality-sensitive hashing for near duplicate image detection. IEEE Trans. Image Process. 27, 9 (2018), 4452--4464.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. W. Shao, R. Xiao, J. Huang, H. Liu, and X. Du. 2019. FJLT-FLSH: More efficient fly locality-sensitive hashing algorithm via FJLT for WMSN IoT search. IEEE Internet Things J. 6, 4 (2019), 7122--7136.Google ScholarGoogle ScholarCross RefCross Ref
  33. K. Ding, C. Huo, B. Fan, S. Xiang, and C. Pan. 2018. In defense of locality-sensitive hashing. IEEE Trans. Neural Netw. Learn. Syst. 29, 1 (2018), 87--103.Google ScholarGoogle ScholarCross RefCross Ref
  34. W. Wu, B. Li, L. Chen, C. Zhang, and P. S. Yu. 2019. Improved consistent weighted sampling revisited. IEEE Trans. Knowl. Data Eng. 31, 12 (2019), 2332--2345.Google ScholarGoogle ScholarCross RefCross Ref
  35. H. Li, S. Nutanong, H. Xu, C. Yu, and F. Ha. 2019. C2Net: A network-efficient approach to collision counting LSH similarity join. IEEE Trans. Knowl. Data Eng. 31, 3 (2019), 423--436.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. C. Guo, J. Jia, Y. Jie, C. Z. Liu, and K. R. Choo. 2020. Enabling secure cross-modal retrieval over encrypted heterogeneous IoT databases with collective matrix factorization. IEEE Internet Things J. 7, 4 (2020), 3104--3113.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. An LSH-based Offloading Method for IoMT Services in Integrated Cloud-Edge Environment

        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 Multimedia Computing, Communications, and Applications
          ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 3s
          Special Issue on Privacy and Security in Evolving Internet of Multimedia Things and Regular Papers
          October 2020
          190 pages
          ISSN:1551-6857
          EISSN:1551-6865
          DOI:10.1145/3444536
          Issue’s Table of Contents

          Copyright © 2021 ACM

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 8 January 2021
          • Revised: 1 May 2020
          • Accepted: 1 May 2020
          • Received: 1 February 2020
          Published in tomm Volume 16, Issue 3s

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Research
          • 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!