| Title | RETRACTED: Enduring data analytics for reliable data management in handling smart city services (Retracted article. See JAN, 2023) |
|---|---|
| ID_Doc | 39754 |
| Authors | Kalra, D; Pradhan, MR |
| Title | RETRACTED: Enduring data analytics for reliable data management in handling smart city services (Retracted article. See JAN, 2023) |
| Year | 2021 |
| Published | Soft Computing, 25, 18 |
| Abstract | Smart energy management improves the efficiency of the services and computing systems operated in a smart city environment. The heterogeneous environment makes use of artificial intelligence allied technologies for energy management. Depending upon the service data and its processing, energy management is proliferated for providing reliable service outcomes. In this paper, Enduring Data Analytics for Energy Management (EDA-EM) is proposed. The proposed method makes use of data distribution factors for optimal energy distribution. In this process, the cumulative and independent data streams require different energy requirements. This requirement is identified using principal component analysis for identifying other data streams. The data stream is a series of feature vectors describing one or more underlying patterns together. A stream framework demonstrates how the underlying patterns of different stream elements can be reconstructed. The stream classification helps to allocate desired energy without overloaded instances in the big data processing. The remaining and available renewable energy is distributed for the classified streams in handling big data. Big data streaming indicates that Big Data is processed rapidly to gain insight into it in virtual environments. The production data are in movement. The data are in trend. Ideally, big data streaming is a frequency solution that processes a prolonged data stream. In this allocation process, renewable energy available for the successive handling interval is predicted for improving the seamlessness in the data processing. The proposed method's performance is verified using energy distribution ratio, conservation rate, and data loses, processing rate, and processing delay. In EDA-EM, the achieved distribution ratio is 95.13% which is comparatively reasonable than other approaches. |
Similar Articles
| ID | Score | Article |
|---|---|---|
42351
|
Potdar, V; Chandan, A; Batool, S; Patel, N Big Energy Data Management for Smart Grids-Issues, Challenges and Recent Developments(2018) | |
| 42676
|
Jindal, A; Kumar, N; Singh, M A unified framework for big data acquisition, storage, and analytics for demand response management in smart cities(2020) | |
| 45718
|
Babar, M; Khattak, AS; Jan, MA; Tariq, MU Energy aware smart city management system using data analytics and Internet of Things(2021) | |
| 37748
|
Jnr, BA Smart city data architecture for energy prosumption in municipalities: concepts, requirements, and future directions(2020)International Journal Of Green Energy, 17.0, 13 | |
| 42215
|
Ali, M; Scandurra, P; Moretti, F; Blaso, L Architecting a big data-driven software architecture for smart street lighting(2023) | |
| 38388
|
Matheri, AN; Ngila, JC; Njenga, CK; Belaid, M; van Rensburg, NJ Smart City Energy Trend Transformation in the Fourth Industrial Revolution Digital Disruption(2019) | |
| 37400
|
Kumari, A; Tanwar, S Secure data analytics for smart grid systems in a sustainable smart city: Challenges, solutions, and future directions(2020) |