Access Management in Lightweight IoT: A Comprehensive review of ACE-OAuth framework

With the expansion of Internet of Things (IoT), the need for secure and scalable authentication and authorization mechanism for resource-constrained devices is becoming increasingly important. This thesis reviews the authentication and authorization mechanisms in resource-constrained Internet of Things (IoT) environments. The thesis focuses on the ACE-OAuth framework, which is a lightweight and scalable solution for access management in IoT. Traditional access management protocols are not well-suited for the resource-constrained environment of IoT devices. This makes the lightweight devices vulnerable to cyber-attacks and unauthorized access. This thesis explores the security mechanisms and standards, the protocol flow and comparison of ACE-OAuth profiles. It underlines their potential risks involved with the implementation. The thesis delves into the existing and emerging trends technologies of resource-constrained IoT and identifies limitations and potential threats in existing authentication and authorization methods. Furthermore, comparative analysis of ACE profiles demonstrated that the DTLS profile enables constrained servers to effectively handle client authentication and authorization. The OSCORE provides enhanced security and non-repudiation due to the Proof-of-Possession (PoP) mechanism, requiring client to prove the possession of cryptographic key to generate the access token. The key findings in this thesis, including security implications, strengths, and weaknesses for ACE OAuth profiles are covered in-depth. It shows that the ACE-OAuth framework’s strengths lie in its customization capabilities and scalability. This thesis demonstrates the practical applications and benefits of ACE-OAuth framework in diverse IoT deployments through implementation in smart home and factory use cases. Through these discussions, the research advances the application of authentication and authorization mechanisms and provides practical insights into overcoming the challenges in constrained IoT settings

A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning

In the recent scenario, the most challenging requirements are to handle the massive generation of multimedia data from the Internet of Things (IoT) devices which becomes very difficult to handle only through the cloud. Fog computing technology emerges as an intelligent solution and uses a distributed environment to operate. The objective of the paper is latency minimization in e-healthcare through fog computing. Therefore, in IoT multimedia data transmission, the parameters such as transmission delay, network delay, and computation delay must be reduced as there is a high demand for healthcare multimedia analytics. Fog computing provides processing, storage, and analyze the data nearer to IoT and end-users to overcome the latency. In this paper, the novel Intelligent Multimedia Data Segregation (IMDS) scheme using Machine learning (k-fold random forest) is proposed in the fog computing environment that segregates the multimedia data and the model used to calculate total latency (transmission, computation, and network). With the simulated results, we achieved 92% as the classification accuracy of the model, an approximately 95% reduction in latency as compared with the pre-existing model, and improved the quality of services in e-healthcare

Interference as an Issue and a Resource in Wireless Networks

This dissertation will be focused on the phenomenon of interference in wireless net- works. On one hand, interference will be viewed as a negative factor that one should mitigate in order to improve the performance of a wireless network in terms of achiev- able rate, and on the other hand as an asset to increase the performance of a network in terms of security. The problems that will be investigated are, first, the character- isation of the performance of a communication network modelled as an interference channel (IC) when interference alignment (IA) is used to mitigate the interference with imperfect knowledge of the channel state, second, the characterisation of the secrecy in the Internet-of-Things (IoT) framework where some devices may use artificial noise to generate interference to potential eavesdroppers. Different scenarios will be studied in the case where interference is unwanted; the first one is when the channel error is bounded. A lower bound on the capacity achievable in this case is provided and a new performance metric namely the saturating SNR is derived. The derived lower bound is studied with respect to some parameters of the estimation strategy when using Least-Square estimation to estimate the channel ma- trices. The second scenario deals with unbounded Gaussian estimation errors, here the statistical distribution of the achievable rate is given along with a new performance metric called outage probability that simplifies the study of the IC with IA under im- perfect CSI. The results are used to optimise the network parameters and extend the analysis further to the case of cellular networks. In the wanted interference situation, the secrecy of the worst-case communication is studied and the conditions for secrecy are provided. Furthermore the average number of secure links achievable in the network is studied according to a theoretical model that is developed for the IoT case