Dr. Raja Wasim Ajmad Khan holds a master’s degree in Computer Science from COMSATS University Islamabad (CUI), Abbottabad Campus (2011), and a Ph.D. in Computer Science from the Center for Mobile Cloud Computing Research (C4MCCR), University of Malaya, Malaysia. He is currently an Assistant Professor of IT at the Department of IT, Ajman University. Previously, Dr. Khan served as a Postdoctoral Research Fellow at Khalifa University of Science and Technology, where he contributed to projects focused on industry digitization using blockchain technology, covering areas such as Telehealthcare, Aerospace Engineering, Smart Cities, Waste Management, the Automotive Industry, Machine Learning, and Aviation. Dr. Khan has published extensively in high-impact journals, including IEEE Transactions on Emerging Topics in Computing, IEEE Systems Journal, IEEE Access, Journal of Network and Computer Applications, and Renewable and Sustainable Energy Reviews. His research has been recognized at national and international conferences, with several articles listed among the most downloaded, and he received the Best Paper Award at the SPECTS Symposium in France (2018). He actively contributes to the academic community as a reviewer for leading journals and conferences, has served as a Guest Associate Editor for IEEE Access, and is currently an Associate Editor for Cluster Computing.
Today's systems, approaches, and technologies leveraged for managing oil and gas supply chain operations fall short of providing operational transparency, traceability, audit, security, and trusted data provenance features. Also, a large portion of the existing systems are centralized, manual, and highly disintegrated, which make them vulnerable to manipulation and the single point of failure problem. In this survey, we explore the potential opportunities and applications of blockchain technology in managing the exploration, production, and supply chain and logistics operations in the oil and gas industry as it can offer traceability, immutability, transparency, and audit features in a decentralized, trusted, and secure manner. We discuss state-of-the-art blockchain-based schemes, research projects, business initiatives, and case studies to highlight the practicability of blockchain in the oil and gas industry. We present the potential opportunities brought about by blockchain technology in various use cases and application scenarios. We present several systems that use blockchain-based smart contracts to automate critical services such as petroleum product tracking and tracing, international trade document protection, and coordination of purchasing and bidding activities for granting oil exploration rights to petroleum exploration and development companies. Finally, we present open challenges acting as future research directions.
Product recall management in the automotive industry is a challenging problem that affects human lives and the safe operation of automobiles. Product recalls can assist in removing potentially unsafe products from the marketplace and minimizing a company’s responsibility for corporate negligence. Today’s systems and technologies leveraged for product recall management in the automotive supply chain fall short in providing transparency, traceability, reliability, audit, security, and trust features. In this paper, we propose a blockchain-based approach to overcome the aforementioned problems related to product recall management. We employ the public Ethereum blockchain and integrate it with the decentralized storage of the InterPlanetary File System (IPFS) to deal with the large-sized data problem. We present the system design and six algorithms explaining the working principles, information exchange flow, and stakeholders’ detail and their sequential interactions. We discuss the implementation details, generalization aspects, and cost and security analyses to evaluate the performance of the proposed approach. The proposed solution is cost-effective, secure, and enables automakers to have end-to-end visibility of information during product recalls. We make the smart contracts’ code publicly available on GitHub.
Today’s technologies, techniques, and systems leveraged for managing energy trading operations in electric vehicles fall short in providing operational transparency, immutability, fault tolerance, traceability, and trusted data provenance features. They are centralized and vulnerable to the single point of failure problem, and less trustworthy as they are prone to the data modifications and deletion by adversaries. In this paper, we present the potential advantages of blockchain technology to manage energy trading operations between electric vehicles as it can offer data traceability, immutability, transparency, audit, security, and confidentiality in a fully decentralized manner. We identify and discuss the essential requirements for the successful implementation of blockchain technology to secure energy trading operations among electric vehicles. We present a detailed discussion on the potential opportunities offered by blockchain technology to secure the energy trading operations of electric vehicles. We discuss several blockchain-based research projects and case studies to highlight the practicability of blockchain technology in electric vehicles energy trading. Finally, we identify and discuss open challenges in fulfilling the requirements of electric vehicles energy trading applications.
Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today’s deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly trustworthy deep learning frameworks.
Accepted