In the past few years, the implementation of blockchain technology for various applications has been widely discussed in the research community and the industry. There are sufficient number of articles that discuss the possibility of applying blockchain technology in various areas, such as, healthcare, IoT, and business. However, in this article, we present a comparative analysis of core blockchain architecture, its fundamental concepts, and its applications in three major areas: the Internet-of-Things (IoT), healthcare, business and vehicular industry. For each area, we discuss in detail, challenges and solutions that have been proposed from the research community and industry. This research studies also presented the complete ecosystem of blockchain of all the papers we reviewed and summarized. Moreover, analysis is performed of various blockchain platforms, their consensus models, and applications. Finally, we discuss key aspects that are required for the widespread future adoption of blockchain technology in these major areas. INDEX TERMS Blockchain, IoT blockchain, healthcare blockchain, permissioned blockchain, business blockchain.

The Internet of Things (IoT) is undergoing rapid growth in the IT industry, but, it continues to be associated with several security and privacy concerns as a result of its massive scale, decentralised topology, and resource-constrained devices. Blockchain (BC), a distributed ledger technology used in cryptocurrency has attracted significant attention in the realm of IoT security and privacy. However, adopting BC to IoT is not straightforward in most cases, due to overheads and delays caused by BC operations. In this paper, we apply a BC technology known as Hyperledgder Fabric, to an IoT network. This technology introduces an execute-order technique for transactions that separates the transaction execution from consensus, resulting in increased efficiency. We demonstrate that our proposed IoT-BC architecture is sufficiently secure with regard to fundamental security goals i.e., confidentiality, integrity, and availability. Finally, the simulation results are highlighted that shows the performance overheads associated with our approach are as minimal as those associated with the Hyperledger Fabric framework and negligible in terms of security and privacy.

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Businesses need trust to confidently perform trade among each other. Centralized business models are the only mature solutions available to perform trades over the Internet. However, they have many problems which includes but are not limited to the fact that these create bottleneck on the server as well as requires trusted third parties. Recently, decentralized solutions have gained significant popularity and acceptance for future businesses. The wide acceptance of such systems is indeed due to the trust management among various untrusted business stakeholders. Many solutions have been proposed in this regard to provide decentralized infrastructure for various business models. A standard solution that is acceptable to the industry is still in demand. Hyperledger umbrella Blockchain projects, that are supported by IBM and many other industry big players are gaining popularity due to its efficient and pluggable design. In this study, the author present the idea of utilizing Blockchain to design a Value-Added Tax (VAT) system for Saudi Arabia's newly introduced tax system. The reason to select this business model for VAT is twofold. First, it provides an untampered distributed ledger, which cannot be deceived by any party. Each transaction in the system cannot go unnoticed by the smart contract. Secondly, it provides a transparent record, and updates all involved parties regarding each activity performed by stakeholders. The newly proposed system will provide a transparent database of VAT transactions according to our smart contract design and at each stage of supply chain, tax will be deducted and stored on peer-to-peer network via consensus process. The author believes that the proposed solution will have significant impact on VAT collection in the Kingdom of Saudi Arabia.

With a rapid growth in the IT industry, Internet of Things (IoT) has gained a tremendous attention and become a central aspect of our environment. In IoT the things (devices) communicate and exchange the data without the act of human intervention. Such autonomy and proliferation of IoT ecosystem make the devices more vulnerable to attacks. In this paper, we propose a behavior monitor in IoT-Blockchain setup which can provide trust-confidence to outside networks. Behavior monitor extracts the activity of each device and analyzes the behavior using deep auto-encoders. In addition, we also incorporate Trusted Execution Technology (Intel SGX) in order to provide a secure execution environment for applications and data on blockchain. Finally, in evaluation we analyze three IoT devices data that is infected by mirai attack. The evaluation results demonstrate the ability of our proposed method in terms of accuracy and time required for detection.

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Takaful -an Islamic alternative to conventional insurance -is fast becoming one of the most important constituents of modern Islamic financial market. The fundamental difference between the two forms of risk mitigation is entrenched from the type of contract selected. The conventional insurance work on the principle of bilateral contracts between the customer (insured) and insurance provider where the insured pay regular premium in return for payment of compensation, in case of a predefined event occurs. On the other hand, Takaful works on the principle of mutual guarantee, cooperation and indemnity where the participants in the scheme mutually insure each other. The Takaful providers are mainly responsible for managing, administering and investigating the Takaful funds according to Islamic laws. This studies provides a decentralized architecture that securely implements Takaful risk mitigation system according to its principles. Since all major banking sectors are shifting towards Blockchain technology, as it is currently the only viable solution to offers security, transparency, integrity of resources and ensure trustworthiness among customers. The proposed studies offer state-of-the-art Blockchain technology and focus provide a Takaful system that strictly follows the underlying Islamic laws for this risk mitigation system. Moreover, the proposed platform provides all Takaful transactions over Blockchain that brings confidence and transparency to the community involved in the process.

With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed.