Power network disruptions triggered by weather events, malfunction, sabotage, or other phenomena can leave harrowing effects on communities. Microgrids with distributed energy resources have the potential to enable swift localized restoration following the interruption of utility power. However, because of the limited generation and storage capacity of microgrids, service restoration will require prioritizing critical load and optimality of operations to render relief to those in dire need. Moreover, this essentially requires fair energy allocation, trust-free energy exchanges, and the integrity of transactions. This paper proposes a consortium blockchain-based energy sharing approach for service restoration using energy crowdsourced through donation or trade. The proposed approach provides a framework that utilizes microgrids’ supply situation and reputation as consortium admission criteria for optimizing the energy cost of blockchain operations. The proposed approach uses a measure called proof of welfare (PoWel), which solves the rationing problem to produce an energy allocation block. Accordingly, it proposes an algorithm by utilizing weighted rationing for prioritizing critical load restoration and an evolutionary optimization algorithm for maximizing social welfare and minimizing power losses. The winner block selected through consensus intrinsically preserves the network stability while conforming to resource and stability constraints. An extensive performance and security analysis ascertain the effectiveness of the proposed approach.

Collaborative Intrusion Detection System (CIDS) protect large networks against distributed attacks. However, a CIDS is vulnerable to insider attacks that decrease the mutual trust among the CIDS nodes. Most existing trust management approaches rely on a central authority, trusted third parties or network peers for managing trust. The current techniques are prone to high false positives and vulnerable to various reputation attacks. For instance, device attestation manages trust among CIDS nodes by verifying the integrity of a node’s hardware and software configuration. However, it lacks real-time monitoring of the dynamic state, limiting its effectiveness against ongoing attacks and malware. Therefore, incorporating the system’s dynamic state in the trust framework is crucial, but it causes false positives requiring corrective mechanisms. To address these challenges, this paper proposes a blockchain-based integrated trust management framework for CIDS, incorporating the device’s genome attestation, the system’s dynamic parameters, and a false positive resilient reputation mechanism. By storing the reputation scores on the blockchain, the framework alleviates the need for a third party for trust management and thus mitigates attacks applicable to reputation-based systems. The paper performs a comprehensive security and performance analysis of the proposed framework to gauge its efficiency and study the effects of a penalty on a node’s reputation during the recovery and rally phases. We also study the impact of false positives on the reputation of a node. The results show that Hyperledger Fabric offers lower transaction latency and low CPU utilization compared to Ethereum Blockchain.

Exorbitant energy expenses can supersede data center profits. Electricity prices often vary across the geographic regions, caused by gaps in the supply-demand, time of use, and production cost factors. Geo-distributed cloud data centers facilitated by a smart grid and enabled by cloud computing can potentially utilize the spatiotemporal diversity of energy prices to reduce operational expenditure and maximize profit. In this article, we solve the data center profit by formulating it as a constrained multi-objective optimization problem. The proposed solution utilizes an evolutionary algorithm-based higher-level heuristic that optimizes data center revenue and expense objectives simultaneously. The proposed technique provides system managers with trade-off solutions suited to varied operational scenarios. Ours is a multi-step approach, utilizing the optimization scheme to obtain Pareto optimal solutions for the request dispatch and resource allocation problem. When broadly evaluated against a comparative resource optimization scheme, our technique increases revenue while lowering expense and collectively yields a higher profit. It exhibits such performance over a broad range of price changes regardless of the data center's size and utilization level. The extensive simulation results ascertain the effectiveness of the proposed approach across a myriad of system parameters.

Natural hazards and technical malfunctions can cause widespread outages of power networks, adversely affecting communities and infrastructures. Microgrids with distributed generation and storage can help mitigate some of these devastating effects. However, not many communities and infrastructures have alternative power mechanisms. When needed, microgrids may help needy neighbors or critical communities, such as hospitals, by donating or trading surplus capacity. Energy donation in a smart grid is a viable and highly effective restoration option to mitigate the effects of a disaster. However, because of the limited capacity of microgrids, service restoration requires prioritizing critical load and optimality of operations for rendering relief to those in dire need. In this paper, we propose a new framework for enabling energy donation in a smart grid during a crisis when the main supply is cut off. The proposed technique allocates energy using weighted and optimized rationing approaches. It does so by catering to the load critically and users’ historical contribution during restoration. The proposed method is based on an algorithm which employs evolutionary optimization technique that maximizes social welfare and minimizes losses while satisfying the resource and network constraints. Extensive simulation results ascertain the effectiveness of the proposed approach across a myriad of system parameters.