A Secure IoT‐Based Cloud Platform Selection Using Entropy Distance Approach and Fuzzy Set Theory

Abstract

With the growing emergence of the Internet connectivity in this era of Gen Z, several IoT solutions have come into existence for exchanging large scale of data securely, backed up by their own unique cloud service providers (CSPs). It has, therefore, generated the need for customers to decide the IoT cloud platform to suit their vivid and volatile demands in terms of attributes like security and privacy of data, performance efficiency, cost optimization, and other individualistic properties as per unique user. In spite of the existence of many software solutions for this decision-making problem, they have been proved to be inadequate considering the distinct attributes unique to individual user. This paper proposes a framework to represent the selection of IoT cloud platform as a MCDM problem, thereby providing a solution of optimal efficacy with a particular focus in user-specific priorities to create a unique solution for volatile user demands and agile market trends and needs using optimized distance-based approach (DBA) aided by Fuzzy Set Theory.

[Retracted] Secure Techniques for Channel Encryption in Wireless Body Area Network without the Certificate

“…The goal of the research is to analyze the concerns that have been raised by thoroughly evaluating a variety of case studies of significant cloud-based IoT systems that have been proven to infringe on users' constitutionally given rights to privacy and security. Many case studies of key Internet of Things systems will be studied as part of this inquiry [28]. Smartphone users may now roam about freely without having to give up control of their position to stay connected to the network.…”

A Hybrid Group Multi-Criteria Approach Based on SAW, TOPSIS, VIKOR, and COPRAS Methods for Complex IoT Selection Problems

“…The DMs select a set of criteria (based on experience and literature reviews [4,37,42,48,49,51,52,59]). These criteria are of two categories: IoT platform requirements and criteria for IoT platform vendors.…”

Review on multi-modality medical image fusion using deep learning

“…A considerable amount of storage space is required for the output U-Net but as the CNN is naturally represented to be sparse, the connection of neurons is between the previous stages and the neurons adjacent to it thus reducing the capacity or storage required and resulting in improvement of computational performance. The non-uniqueness of the weights in classical networks is eliminated via parameter sharing in CNN [3]. In the CNN stage, the weight being constant makes it superior in terms of storage requirements to others.…”

“…It has been possible to extract valuable details from the images by performing the method called image fusion. The primary objective of conducting image fusion is to produce clearer and more informative images that provide us desired results with higher accuracy and are more appropriate [1][2][3]. As the name suggests, this process of image fusion is called so because it involves the merging of multiple images into one that has all the characteristic features contained in all the images separately [4][5].…”