This study proposes a multistep approach to evaluate, categorize, and rate cloud-based IoT platforms via implementing Multicriteria Decision-Making (MCDM), probabilistic linguistic term sets (PLTSs), and finally, a probabilistic linguistic best-worst (PLBW) is used to score all platforms
Though the proposed method seems complex but a real-time implementation via case study provides cogent proof of its efficiency. It also outperforms individual scoring, classification, and evaluating methods.
The data used in the case study is limited which explains the flow of the method but falls back to prove its cogency. Moreover, inclusion of latest hybrid techniques in the domain for comparative analysis could further edify the study’s significance.
Cloud service provider selection approach is proposed via application of Multicriteria Decision-Making (MCDM), analytical hierarchical process (AHP), technique for order of preference by similarity to ideal solution (TOPSIS), and the best-worst method (BWM). Case study is presented to support the same.
The study successfully identifies and provides solutions the drawbacks of classical multicriteria decision-making (MCDM) approaches in terms of accuracy, time required, and complexity of computation. AHP is outperformed by proposed approach.
The use case scenario presented used stimulated scenarios and data that raises question against the cogency of the proposed study.
Additive manufacturing based cloud-based service providing framework is proposed to include both hard and soft services for the ease of customer use. These include data-based testing, design, 3D printing, remote control of printers, and face recognition using AI.
This study understands and provides solution to the real-time consumer or customer problems. Its feature providing framework proves to be easy, feasible, and effective.
The study only provides a framework along with it merit without any details of implementing or developing the framework for real world application.
The study is aimed at identifying various determinants that cause deprecation of various ministry of micro, small, and medium enterprises in India, contributing a huge impact on Indian economy. Data is collated from 500 Indian MSMEs. Multiple criteria include social influence, Internet of Things, perceived ease of use, trust, and perceived IT security risk among others.
This study evaluates real-time data from 500 MSMEs that proves its cogency. Moreover, it provides insight that can be directly deliberated by policy makers to create maximum impact.
A comparative analysis with other policy-making insight provider algorithms along with impact of implementing the recommended changes would create more clarity and value for the research.
A comparative analysis is performed to obtain the best cloud-based IoT platform for any business or organization by deliberating multiple criteria, functional and nonfunctional requirements among five giants, namely, Azure, AWS, SaS, ThingWorx, and Kaa IoT by application various techniques like analytical hierarchical process (AHP), -means clustering, and statistical tests.
The hierarchal method of requirement classification provides edge to the method and various statistical tests implemented on the results obtained creates increased sense of cogency or significance to the study.
The cloud-based IoT platforms are limited creating false sense of performance in terms of evaluating more than 5 platforms. Moreover, requirement classification into hierarchy is very time and effort intensive.
IoT applications built via cloud-based platforms are assayed for any kind of security challenge or data inconsistency issues that arise due to third party auditors, phishing attacks. It also provides strategies to prevent the same.
The objective of the study is very relevant to the need of the hour, providing valid and much needed information. It also provides recommendations handle the same.
The scope of study is limited to theoretical analysis without any real data implementation or case study to prove the cogency of the points mentioned in the paper.
This study poses to create a need for authorization in cloud-enabled IoT systems by assaying various security threats that such a set up encounters via two case studies in order. Proposing control-based authorization system
The aim of the study very cogent and current, deliberating recent developments of cloud-based IoT applications. The case studies presented aid to the cause of study while contributing to the significance of the proposed framework.
The framework proposed for control-based authorization lacks any sense of implementation or efforts towards prototype development.
An attack distribution detector is proposed to prevent malfunctioning of trust bounders in IoT-based applications, leading to severe data theft. A downsampler-encoder-based cooperative data generator is proposed to discriminate noisy data that may lead of data theft that malfunction trust boundaries.
The continuous updating and verification of the model provides it optimal results and performance to detection probable data thefts. The model outperforms primordial machine learning and deep learning techniques.
Inclusion of latest hybrid techniques in the domain for comparative analysis could further edify the study’s significance.
Various cogent issues with IoT middleware are brought to attention while proposing a state of art IoT middleware that can integrate with MQTT, CoAP, and HTTP as application-layer protocols.
The problem addressed by In.IoT framework is cogent, and its relevance has been shown very accurately in the study.
A comparative analysis with classical middleware and latest hybrid techniques could further edify the significance of the study.
An intrusion detection technique for cloud-based IoT application is proposed by implementing machine learning, to obtain state of art accuracy and in-depth analysis of source or type of intrusion.
The survey of 95 developments in intelligence-based intrusion detection techniques provides the study significant relevance and ground for comparative analysis with proposed technique.
Though the study shows optimal accuracy, false-positive results still hamper its cogency.
