An LSH-based offloading method for IoMT services in integrated cloud-edge environment

Xu, Xiaolong; Huang, Qihe; Zhang, Yiwen; Li, Shancang; Qi, Lianyong; Dou, Wanchun

Abstract

© 2021 ACM. 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 Locality-sensitive-hash (LSH)-based offloading method, 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.

Citation

Xu, X., Huang, Q., Zhang, Y., Li, S., Qi, L., & Dou, W. (2021). An LSH-based offloading method for IoMT services in integrated cloud-edge environment. ACM Transactions on Multimedia Computing, Communications and Applications, 16(3s), Article 94. https://doi.org/10.1145/3408319

• Journal Article Type: Article
• Acceptance Date: May 21, 2020
• Online Publication Date: Jan 1, 2021
• Publication Date: Jan 1, 2021
• Deposit Date: May 23, 2020
• Publicly Available Date: Feb 2, 2021
• Journal: ACM Transactions on Multimedia Computing, Communications and Applications
• Print ISSN: 1551-6857
• Electronic ISSN: 1551-6865
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 3s
• Article Number: 94
• DOI: https://doi.org/10.1145/3408319
• Public URL: https://uwe-repository.worktribe.com/output/5997623
• Publisher URL: https://dl.acm.org/journal/tomm
• Additional Information: Accepted on 21 May 2020