Search

The integration between blockchains, Internet-of-Thing (IoT), and smart contracts is an emerging and promising technology. The advantages of this technology have raised the importance of Industrial Internet-of-Thing (IIoT) and have paved the pathway for “Industry 4.0.” Surprisingly, access control has received less attention in IIoTs. Though there are some solutions coming forward to use blockchains for IIoT to enable secure and resilient access control management, the challenge is to satisfy the low-latency requirements of IIoTs for validating and adding the blocks to the chain. Besides, role-based and rule-based access controls in the existing systems can be forged without organizational access controls and compliance. Therefore, we address these problems in this article. In the present work, we propose DHACS, a Decentralized Hybrid Access Control for Smart contract, for IIoTs. DHACS aims to provide transparency, reliability, and robustness to the existing access control mechanism in IIoTs. The framework is based on blockchain feasibilities that contribute to an interconnected hybrid access control through smart contract provision. It is a novel idea in the domain of IIoTs. We use three access control strategies, role-based, rule-based, and organization-based, to develop a hybrid approach for smart contract in DHACS. The operational transactions along with their access controls are accounted and blocks are made by the transaction pooler and block creator. We use a private blockchain environment; however, it can be extended to a public blockchain or consortium blockchain for geographical distributed dependency. We compare DHACS with three existing approaches in recent time. We measure the performance in terms of computational costs, storage complexity, and energy consumption. DHACS outperforms the others approaches and is considered to be efficient for IIoT applications with more than 30% better efficiency in access control management. To the best of our knowledge, DHACS is the first attempt to use decentralized blockchains with smart contract for hybrid access control in IIoTs. [ABSTRACT FROM AUTHOR]

In this paper, an Internet of Energy (IoE) solution for providing optimal charging and control of resources in the era of electric vehicles (EVs) with energy trading and security is considered. A blockchain based security solution is presented by considering the IoE as a distributed energy system with internet of things (IoT). However, by inheriting the security concerns of IoTs, IoE also faces the security challenges and is considered here. Hierarchical key generation is used for cryptographic keys, based on lattice constructions. Along with a consensus tailored with Practical Byzantine Fault Tolerance (PBFT), an additional smart contract is also applied for utilization of energy transfer. The presented solution provides transparency, reliability, availability and failure recovery inheriting from the blockchain aspects. To the best of our knowledge, the present study is the first attempt in this direction of IoE-EV interface solutions. The experimental results, comparative analysis and security validation affirm the superiority of the proposed solution over the existing approaches. [ABSTRACT FROM AUTHOR]

In this paper, we develop a feature extraction model using the amalgamation of machine and deep learning techniques for Industrial Internet of Thing (IIoT). We train the model with the most effective feature set evaluated using machine learning algorithms and deep learning feature extraction methods. We test these features with deep learning based network models for validation. We consider error metrics and accuracy as the major factors for combining the machine learning and deep learning techniques. The error rates are analysed using mean square error which show low error rate for the subset than the model tested for full dataset. Further, mean square error, accuracy rate and false values are analysed to test the performance of the proposed model. The comparative analysis with IIoT dataset and existing methods show that our approach is 25% better than the others. [Display omitted] • We address the multi-dimensional problem of the IIoT features to make an adequate feature extraction method. • We use machine learning classifier and wrapper subset evaluation technique with Naive-Bayes and meta bagging methods for feature selection. Deep learning auto encoders are implemented as a part of dimensionality reduction techniques and provides a amalgamation for the appropriateness of the features. We verify them with Principal Component Analysis (PCA) and ranking. Thus, only relevant features are extracted for analysis. • We create network models for analysing detection using deep learning Feed forward neural network and Elman Back propagation techniques for classification and detection with various training methods. [ABSTRACT FROM AUTHOR]