In the cloud computing environment, the privacy of the electronic data is a serious issue that requires special considerations. We have presented a state-of-the-art review of the methodologies and approaches that are currently being used to cope with the significant issue of privacy. We have categorized the privacy-preserving approaches into four categories, i.e., privacy by cryptography, privacy by probability, privacy by anonymization and privacy by ranking. Moreover, we have developed taxonomy of the techniques that have been used to preserve the privacy of the governing data. We also presented a comprehensive comparison of the privacy-preserving approaches from the angle of the privacy-preserving requirements’ satisfaction. Therefore, it is highly desirable that the mechanisms should be developed to deploy efficient auditing and accountability mechanisms that anonymously monitor the utilization of data records and track the provenance to ensure the confidentiality of the data.

The management of data while maintaining its utility and preservation of security scheme is a matter of concern for the cloud owner. In order to minimize the overhead at cloud service provider of applying security over each document and then send it to the client, we propose a layered architecture. This approach maintains security of the sensitive document and privacy of its data sensitivity. To make a balance between data security and utility, the proposed approach categorizes the data according to its sensitivity. Perseverance of various categorization requires different algorithmic schemes. We set up a cloud distributed environment where data is categorized into four levels of sensitivity; public, confidential, secret, top secret and a different approach has been used to preserve the security at each level. At the most sensitive layers i.e. secret and top secret data, we made a provision to detect the faulty node that is responsible for data leakage. Finally, experimental analysis is carried out to analyze the performance of the layered approach. The experimental results show that time taken (in ms) in processing 200 documents of size 20 MB is 437, 2239, 3142, 3900 for public, confidential, secret and top secret data respectively when the documents are distributed among distinct users, which proves the practicality of the proposed approach.

Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman–Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%–43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.

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.