Manchester Metropolitan University's Research Repository

    A context-aware encryption protocol suite for edge computing-based IoT devices

    Dar, Zaineb, Ahmad, Adnan, Khan, Farrukh Aslam, Zeshan, Furkh, Iqbal, Razi, Sherazi, Hafiz Husnain Raza and Bashir, Ali Kashif ORCID logoORCID: https://orcid.org/0000-0001-7595-2522 (2020) A context-aware encryption protocol suite for edge computing-based IoT devices. The Journal of Supercomputing, 76 (4). pp. 2548-2567. ISSN 0920-8542

    Accepted Version
    Download (592kB) | Preview


    Heterogeneous devices are connected with each other through wireless links within a cyber physical system. These devices undergo resource constraints such as battery, bandwidth, memory and computing power. Moreover, the massive interconnections of these devices result in network latency and reduced speed. Edge computing offers a solution to this problem in which devices transmit the preprocessed actionable data in a formal way, resulting in reduced data traffic and improved speed. However, to provide the same level of security to each piece of information is not feasible due to limited resources. In addition, not all the data generated by Internet of things devices require a high level of security. Context-awareness principles can be employed to select an optimal algorithm based on device specifications and required information confidentiality level. For context-awareness, it is essential to consider the dynamic requirements of data confidentiality as well as device available resources. This paper presents a context-aware encryption protocol suite that selects optimal encryption algorithm according to device specifications and the level of data confidentiality. The results presented herein clearly exhibit that the devices were able to save 79% memory consumption, 56% battery consumption and 68% execution time by employing the proposed context-aware encryption protocol suite.

    Impact and Reach


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics for this dataset are available via IRStats2.


    Actions (login required)

    View Item View Item