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RETRACTED ARTICLE: Enduring data analytics for reliable data management in handling smart city services

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This article was retracted on 24 January 2023

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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.

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References

  • Ahmed W, Ansari H, Khan B, Ullah Z, Ali SM, Mehmood CAA et al (2020) Machine learning based energy management model for smart grid and renewable energy districts. IEEE Access 8:185059–185078

    Article  Google Scholar 

  • Ahrarinouri M, Rastegar M, Seifi AR (2020) Multi-agent reinforcement learning for energy management in residential buildings. IEEE Trans Indus Inform 17:659–666

    Article  Google Scholar 

  • Anbarasan M, Muthu B, Sivaparthipan CB, Sundarasekar R, Kadry S, Krishnamoorthy S, Dasel AA (2020) Detection of flood disaster system based on IoT, big data and convolutional deep neural network. Comput Commun 150:150–157

    Article  Google Scholar 

  • Anthony B, Petersen SA, Ahlers D, Krogstie J, Livik K (2019) Big data-oriented energy prosumption service in smart community districts: a multi-case study perspective. Energy Inform 2(1):36

    Article  Google Scholar 

  • Babar M, Rahman A, Arif F, Jeon G (2018) Energy-harvesting based on internet of things and big data analytics for smart health monitoring. Sustain Comput Inform Syst 20:155–164

    Google Scholar 

  • Bibri SE (2020) The eco-city and its core environmental dimension of sustainability: green energy technologies and their integration with data-driven smart solutions. Energy Inform 3(1):1–26

    Article  Google Scholar 

  • Bibri SE, Krogstie J (2020) Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism. Energy Inform 3(1):1–59

    Article  Google Scholar 

  • Cao X, Liu L, Cheng Y, Shen XS (2017) Towards energy-efficient wireless networking in the big data era: A survey. IEEE Commun Surveys Tutor 20(1):303–332

    Article  Google Scholar 

  • Celesti A, Fazio M (2019) A framework for real time end to end monitoring and big data oriented management of smart environments. J Parallel Distrib Comput 132:262–273

    Article  Google Scholar 

  • Chen L, Qiao S, Han N, Yuan C, Song X, Huang P, Xiao Y (2020) Friendship prediction model based on factor graphs integrating geographical location. CAAI Trans Intell Technol 5(3):193–199. https://doi.org/10.1049/trit.2020.0033

    Article  Google Scholar 

  • Cui S, Wang YW, Xiao JW (2019) Peer-to-peer energy sharing among smart energy buildings by distributed transaction. IEEE Trans Smart Grid 10(6):6491–6501

    Article  Google Scholar 

  • Easwaramoorthy S, Thamburasa S, Samy G, Bhushan SB, Aravind K (2016) Digital forensic evidence collection of cloud storage data for investigation. In: 2016 International Conference on Recent Trends in Information Technology (ICRTIT), 2016, pp 1–6. IEEE

  • Edalatpanah S (2020) Data envelopment analysis based on triangular neutrosophic numbers. CAAI Trans Intell Technol 5(2):94–98. https://doi.org/10.1049/trit.2020.0016

    Article  Google Scholar 

  • Hafeez G, Alimgeer KS, Wadud Z, Khan I, Usman M, Qazi AB, Khan FA (2020) An innovative optimization strategy for efficient energy management with day-ahead demand response signal and energy consumption forecasting in smart grid using artificial neural network. IEEE Access 8:84415–84433

    Article  Google Scholar 

  • Hopf K, Sodenkamp M, Staake T (2018) Enhancing energy efficiency in the residential sector with smart meter data analytics. Electron Mark 28(4):453–473

    Article  Google Scholar 

  • Kim S, Mowakeaa R, Kim SJ, Lim H (2020) Building energy management for demand response using kernel lifelong learning. IEEE Access 8:82131–82141

    Article  Google Scholar 

  • Manogaran G, Lopez D (2018) Spatial cumulative sum algorithm with big data analytics for climate change detection. Comput Electr Eng 1(65):207–221

    Article  Google Scholar 

  • Manogaran G, Lopez D (2018) A Gaussian process based big data processing framework in cluster computing environment. Clust Comput 21(1):189–204

    Article  Google Scholar 

  • Manogaran G, Vijayakumar V, Varatharajan R, Kumar PM, Sundarasekar R, Hsu CH (2018) Machine learning based big data processing framework for cancer diagnosis using hidden Markov model and GM clustering. Wireless Pers Commun 102(3):2099–2116

    Article  Google Scholar 

  • Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2018) A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system. Futur Gener Comput Syst 1(82):375–387

    Article  Google Scholar 

  • Marinakis V, Doukas H, Tsapelas J, Mouzakitis S, Sicilia Á, Madrazo L, Sgouridis S (2018) From big data to smart energy services: An application for intelligent energy management. Fut Gener Comput Syst. https://doi.org/10.1016/j.future.2018.04.062

    Article  Google Scholar 

  • Marino CA, Marufuzzaman M (2020) A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics. Comput Ind Eng 143:106392

    Article  Google Scholar 

  • Nie X, Fan T, Wang B, Li Z, Shankar A, Manickam A (2020) Big data analytics and IoT in operation safety management in under water management. Comput Commun 154:188–196

    Article  Google Scholar 

  • Raspopov D, Belousov P (2020) Development of methods and algorithms for identification of a type of electric energy consumers using artificial intelligence and machine learning models for Smart Grid Systems. Proc Comput Sci 169:597–605

    Article  Google Scholar 

  • Saravanan V, Anpalagan A, Kothari DP, Woungang I, Obaidat MS (2014) A comparative simulation study on the power–performance of multi-core architecture. J Supercomput 70(1):465–487

    Article  Google Scholar 

  • Silva BN, Khan M, Han K (2020) Integration of big data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management. Futur Gener Comput Syst 107:975–987

    Article  Google Scholar 

  • Sivaparthipan CB, Muthu BA, Manogaran G, Maram B, Sundarasekar R, Krishnamoorthy S, Hsu CH, Chandran K (2019) Innovative and efficient method of robotics for helping the Parkinson’s disease patient using IoT in big data analytics. Trans Emerg Telecommun Technol 11:e3838

    Google Scholar 

  • Varatharajan R, Manogaran G, Priyan MK (2018) A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimed Tools Appl 77(8):10195–10215

    Article  Google Scholar 

  • Wang S, Liang YC, Li WD, Cai XT (2018) Big data enabled intelligent immune system for energy efficient manufacturing management. J Clean Prod 195:507–520

    Article  Google Scholar 

  • Wu W, Lin W, Hsu CH, He L (2018) Energy-efficient hadoop for big data analytics and computing: a systematic review and research insights. Futur Gener Comput Syst 86:1351–1367

    Article  Google Scholar 

  • Yin H, Zhao C, Ma C (2018) Decentralized real-time energy management for a reconfigurable multiple-source energy system. IEEE Trans Industr Inf 14(9):4128–4137

    Article  Google Scholar 

  • Zhang Y, Ma S, Yang H, Lv J, Liu Y (2018) A big data driven analytical framework for energy-intensive manufacturing industries. J Clean Prod 197:57–72

    Article  Google Scholar 

  • Zhang W, Zhang Z, Zeadally S, Chao HC, Leung VC (2019) MASM: A multiple-algorithm service model for energy-delay optimization in edge artificial intelligence. IEEE Trans Indus Inf 15(7):4216–4224

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Information Technology, Skyline University College, Sharjah, United Arab Emirates

    Deepak Kalra & Manas Ranjan Pradhan

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  1. Deepak Kalra
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  2. Manas Ranjan Pradhan
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Correspondence to Manas Ranjan Pradhan.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by Vincente Garcia Diaz.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00500-023-07868-9

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Kalra, D., Pradhan, M.R. RETRACTED ARTICLE: Enduring data analytics for reliable data management in handling smart city services. Soft Comput 25, 12213–12225 (2021). https://doi.org/10.1007/s00500-021-05892-1

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