Metaheuristics with Vector Quantization Enabled Codebook Compression Model for Secure Industrial Embedded Environment

Abstract

At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image compression in the sensor, on the other hand, expedites the transfer of data while simultaneously reducing bandwidth use. The traditional method of protecting sensitive data is rendered less effective in an environment dominated by IoT owing to the involvement of third parties. The image encryption model provides a safe and adaptable method to protect the confidentiality of picture transformation and storage inside an IIoT system. This helps to ensure that image datasets are kept safe. The Linde–Buzo–Gray (LBG) methodology is an example of a vector quantization algorithm that is extensively used and a relatively new form of picture reduction known as vector quantization (VQ). As a result, the purpose of this research is to create an artificial humming bird optimization approach that combines LBG-enabled codebook creation and encryption (AHBO-LBGCCE) for use in an IIoT setting. In the beginning, the AHBO-LBGCCE method used the LBG model in conjunction with the AHBO algorithm in order to construct the VQ. The Burrows-Wheeler Transform (BWT) model is used in order to accomplish codebook compression. In addition, the Blowfish algorithm is used in order to carry out the encryption procedure so that security may be attained. A comprehensive experimental investigation is carried out in order to verify the effectiveness of the proposed algorithm in comparison to other algorithms. The experimental values ensure that the suggested approach and the outcomes are examined in a variety of different perspectives in order to further enhance them.