The uncertainty and unpredictable power generation and usage of resources at demand as well as supply side causes the mismatch problem. Therefore, the power demand and supply management of regional smart meters have become an essential and challenging research issue in the current era. The existing solutions have inadequately explored the relationship between forecasted load and power demand supply management of smart meters, which imposes a huge impact on the performance of power services to the regional customers. In this context, a novel quantum machine learning-driven power distribution and supply management (QPD-SM) model is proposed, which estimates the power demand of regional smart meters proactively by utilizing the load forecasting values and managing power supply efficiently. The model deploys a newly developed “quantum controlled-NOT gate induced feed-forward neural network” (QCNN) which is optimized using the quantum adaptive differential evolutionary (QuADE) learning algorithm to forecast regional load with precise accuracy. Further, a demand–supply management approach is designed to analyze and estimate the over-/under-supply of power units relative to different regions and govern the power generation and supply management accordingly. Experimental results from the Irish Commission for Energy Regulation and the Australian Smart Grid Smart City show the improvement of root-mean-squared error up to 77.02% and 78.92%, respectively, by the proposed model as compared to the existing state-of-the-art works.

In the era of crowd delivery vehicle routing and traffic management in smart cities, a complex challenge appears indistinctly, affecting both developed and developing nations worldwide. This challenging problem involves optimizing multi-depot routes while addressing various hurdles: minimizing travel time, distance, fuel consumption, and carbon emissions, all while navigating dynamic traffic congestion across diverse pathways. Existing approaches often focus on isolated aspects like shortest paths, carbon emissions, or traffic prediction, leaving the comprehensive multi-depot traffic management problem unaddressed. In response, this research work proposes an Intelligent Multi-Depot Vehicle Routing and Management (IM-VRM) model which provides a comprehensive and holistic solution. It employs a Graph Neural Network (GNN) learning-based routing with a greedy optimization to establish initial optimal pathways for multi-depot journeys. Subsequently, the IM-VRM model integrates traffic congestion prediction with green parameter computation, engaging the Dijkstra algorithm to select the most admissible routes. This consecutive steps-based travel route guidance process optimizes routing for heterogeneous vehicles, including both heavy-duty and light-duty types. It accounts for load-dependent fuel consumption, velocity, and carbon emissions. By doing so, it simplifies the complexities of multi-depot traffic routing and management. The proposed model has been rigorously evaluated using a real-world multi-depot traffic dataset, demonstrating its practical viability. Notably, IM-VRM model achieves a remarkable improvement in fuel savings, reduced carbon emissions, and shorter travel time outperforming previous state-of-the-art methods in both efficiency and precision.

Secure resource management (SRM) within a cloud computing environment is a critical yet infrequently studied research topic. This article provides a comprehensive survey and comparative performance evaluation of potential cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management. Cybersecurity is explored specifically in the context of cloud resource management, with an emphasis on identifying the associated challenges. The cyber threat countermeasure methods are categorized into three classes: defensive strategies, mitigating strategies, and hybrid strategies. The existing countermeasure strategies belonging to each class are thoroughly discussed and compared. In addition to conceptual and theoretical analysis, the leading countermeasure strategies within these categories are implemented on a common platform and examined using two real-world virtual machine (VM) data traces. Based on this comprehensive study and performance evaluation, this article discusses the tradeoffs among these countermeasure strategies and their utility, providing imperative concluding remarks on the holistic study of cloud cyber threat countermeasures and SRM. Furthermore, the study suggests future methodologies that could effectively address the emerging challenges of secure cloud resource management.