EditorialEnabling Technologies for Energy Cloud
Section snippets
Aims & Scope
While distributed renewable energy resources continue to grow exponentially, and the grid becomes more digitized, the utilities’ customer relationships and their operations get even more complex. The more evolution of dynamic demand–response, smart homes and billing, and social media applications will significantly change the way utilities interact with customers (e.g., utilities’ customers have started to act as prosumers by generating their own power and sell it back to the national
Security and privacy
The authors of [12] propose extensions to database systems to allow applications that are used in managing the operations of Energy Clouds, to safely delegate the security and privacy policies to the database, whereas the authors in [10] present a decentralized AI-based Energy Cloud management system framework for energy management by using blockchain. The paper shows how blockchain and AI can be used together to mitigate Energy Cloud management with security and privacy issues. This research
AI and machine learning
The authors in [10] present a unique integration of Artificial Intelligence (AI) and blockchain for Energy Cloud management. This fusion helps in mitigating the various issues encountered by the Energy Cloud system at the runtime including security issues, as mentioned above. Besides, Swarna Priya R.M. et al. [16] propose Energy Efficient Cloud-based Internet of Everything (EECloudIoE) architecture, which acts as an initial step in integrating these two wide areas thereby providing valuable
SDN and networking
As mentioned above, the authors in [17] introduce a new algorithm to secure routing with untrusted devices in Software Defined Network (SDN) –based Energy Cloud critical infrastructure. The new algorithm helps in controlling the optimal number of replicated devices in order to minimize the cost of implementing secure routing in spite of the presence of untrusted devices in SDN-based Energy Cloud critical infrastructure. The presented results show that the proposed algorithm decreases
Optimization, QoS and performance
Devaraj et al. introduce in [4] the fusion of Firefly and Improved Multi-Objective Particle Swarm Optimization (FIMPSO) algorithm, which helps achieving effective average load for making and enhancing the measures of proper resource usage and response time of the tasks over Energy Cloud. FIMPSO achieved outstanding results when compared to the state-of-the-art methods with the least average response time of 13.58 ms, maximum CPU utilization of 98%, memory utilization of 93%, reliability of 67%
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The guest editors would like to express sincere thanks to the Editor-in-Chief for allowing organizing this special issue. The editorial office people have done excellent work and thanks for their support. The guest editors are also thankful to all the authors who made this special issue possible through their contributions, and to the reviewers for their thoughtful comments and feedback.
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