Unpredictable environmental conditions make renewable energy generators of a microgrid unreliable. Energy trade among microgrids may reduce dependency on utility grid (which uses traditional energy generator that causes massive greenhouse gas emission). Coalition formation algorithms are popular tools to execute energy trade among microgrids. In this paper we develop coalition formation method using the blockchain mechanism. Advantages of our solution over existing coalition formation algorithms for microgrid energy trade are as follows: Distributed execution: We show how to find coalition structure in a distributed fashion. It improves robustness of the computation compared with centralized executions of coalition formation algorithms. This is because failure of a centralized computing entity will lead to the failure of energy trade among all MGs but in a distributed solution such problem can be avoided. Asynchronous execution: In our solution, multiple coalition formation algorithms are executed asynchronously. Most of the existing solutions execute coalition formation algorithms at regular time intervals in synchronous fashion. Our solution reduces the waiting time for a microgrid to trade energy with other microgrids. Energy requirement of microgrids may depend on weather and changes in their energy requirement may be not synchronous, i.e., energy requirement of all microgrids may not change at the same time and after regular intervals. Scalable: We show that the distributed execution of coalition formation algorithm is more scalable than centralized algorithms for coalition formation. Convergence: We show that the proposed distributed algorithm converges quickly. Local energy trade: We show that the proposed distribution algorithm promotes local energy trade to reduce energy loss due to long transmission. It should be noted that feasibility of local energy trade depends on satisfaction of constraints in distribution network. Our solution aim to reduce the distance between matched energy supplier and energy provider if the distribution network allows such an energy transfer. Trust: The trust between a microgrid and the computation entity who is responsible for generating coalition structure is ignored in the existing solutions. We use a blockchain to eliminate such trust establishment requirement. Security and Privacy: The existing literature ignores the security of the computation framework. A MG’s energy surplus / deficit reveals its energy consumption patterns and it may cause security problems. We mitigate such security problems using the blockchain mechanism.

The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems.

Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains: the absence of semantic interoperability. Today, businesses operating in a digital environment require increased supply-chain automation, interoperability, and data governance. While research on the semantic web and interoperability has recently received much attention, a dearth of studies investigates the relationship between these two concepts in depth. To address this knowledge gap, the objective of this study is to conduct a review and bibliometric analysis of 3511 Scopus-registered papers on the semantic web and interoperability published over the past two decades. In addition, the publications were analyzed using a variety of bibliometric indicators, such as publication year, journal, authors, countries, and institutions. Keyword co-occurrence and co-citation networks were utilized to identify the primary research hotspots and group the relevant literature. The findings of the review and bibliometric analysis indicate the dominance of conference papers as a means of disseminating knowledge and the substantial contribution of developed nations to the semantic web field. In addition, the keyword co-occurrence network analysis reveals a significant emphasis on semantic web languages, sensors and computing, graphs and models, and linking and integration techniques. Based on the co-citation clustering, the Internet of Things, semantic web services, ontology mapping, building information modeling, bioinformatics, education and e-learning, and semantic web languages were identified as the primary themes contributing to the flow of knowledge and the growth of the semantic web and interoperability field. Overall, this review substantially contributes to the literature and increases scholars’ and practitioners’ awareness of the current knowledge composition and future research directions of the semantic web field.

In the Infrastructure as a Service (IaaS) paradigm of cloud computing, computational resources are available for rent. Although it offers a cost efficient solution to virtual network requirements, low trust on the rented computational resources prevents users from using it. To reduce the cost, computational resources are shared, i.e., there exists multi-tenancy. As the communication channels and other computational resources are shared, it creates security and privacy issues. A user may not identify a trustworthy co-tenant as the users are anonymous. The user depends on the Cloud Provider (CP) to assign trustworthy co-tenants. But, it is in the CP's interest that it gets maximum utilization of its resources. Hence, it allows maximum co-tenancy irrespective of the behaviours of users. In this paper, we propose a robust reputation management mechanism that encourages the CPs in a federated cloud to differentiate between good and malicious users and assign resources in such a way that they do not share resources. We show the correctness and the efficiency of the proposed reputation management system using analytical and experimental analysis.