Special Issue "Applications of IoT and Cloud Computing in Smart Grids"
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 1395
Interests: big data applications; smart grid analytics; cloud computing
Interests: data driven analytics and optimization; smart grids; reinforcement learning
Interests: distributed systems; Internet of Things; cloud computing; edge computing; graph processing
Interests: resource management in cluster; computational grids and clouds; parallel and distributed processing (high performance computing); virtualization; computer architecture
We are on the verge of the so-called fourth industrial revolution. Recent advances in cyber-physical systems are enabling the acquisition, delivery, and analysis of increasingly large volumes of data. Cloud computing, with its seamless approach to scalable computing, has emerged as a suitable solution for businesses running data analytics involving machine learning tasks that would otherwise not be feasible using traditional computers. The increasing number of sensors has showcased the potential of IoT in making our world safer, more accessible, and comfortable. However, until the advent of 5G, there was no suitable means of making these two worlds communicate efficiently. The energy sector with its smart grids is such an example, where, for instance, (1) data from tens of thousands of sensors from customers are gathered by providers to gain meaningful insight on real-time demand optimization (2) and real-time monitoring and predictive maintenance of remote renewable energy sources is needed for optimizing the load balancing. For a significant period, transmitting this data in near real-time over Wi-Fi and wireless has been hindered by the network capability. Software-driven solutions involving on edge or fog preprocessing combined with periodic cloud-based analytics of the resulting metadata have been a key focus of research. The advent of 5G opens new opportunities for at-scale bi-directional delivery and analysis of real-time customer and utility data.
In this Special Issue, we aim at bringing together researchers and practitioners from the field of smart grids to showcase state-of-the-art cyber-physical solutions to current challenges in smart grid analytics. These should cover the full spectrum of hardware (sensors, networks), software (middleware, platforms, applications), and data (storage, privacy, security) aspects for fast scalable data analytics involving the entire cyber-physical stack, from IoT to cloud systems. We, therefore, invite contributors to present their models, frameworks, and applications with a focus on hybrid IoT/cloud computing for smart grids with all its challenges.
Dr. Marc Frincu
Dr. Sanmukh Rao Kuppannagari
Prof. Dr. Yogesh Simmhan
Prof. Dr. Cesar A. F. De Rose
Dr. Mayank Malik
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- Data analytics
- Smart grid
- Near real-time processing
- Cloud computing
- Edge computing
- Machine learning
- Data privacy and security