Assessment on economic power management for smart city through IoT sensor model

被引:0
|
作者
Khan, Bhola [1 ]
S, Kirubadevi [2 ]
Agarwal, Swathi [3 ]
M, Prasad [4 ]
Thupakula, Bhaskar [5 ]
Raja Ambethkar, M. [6 ]
机构
[1] Department of Economics, Yobe State University, Nigeria
[2] Department of Electrical and Electronics Engg, University College of Engineering, Tamilnadu, Dindigul,624622, India
[3] Department of IT, CVR College of Engineering, Mangalpalli, Telangana, Ibrahimpatnam, India
[4] School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, TamilNadu, Chennai,600127, India
[5] Department of Computer Engineering, Sanjivani College of Engineering Kopargaon, Tal.Kopargaon, Maharashtra, Ahmednagar,423 603, India
[6] Department of Engineering English, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh State, 522302, India
来源
Measurement: Sensors | 2022年 / 24卷
关键词
Internet of things;
D O I
10.1016/j.measen.2022.100515
中图分类号
学科分类号
摘要
Despite not being uniform, urban economic growth usually shows some inconsistency. Urban sustainable development may be harmed if the financial instability goes above a particular threshold. This article examines the current status of India's macro-economic real-time management of early warning alert systems and information warehouse-related innovations. In order to deal with the significant loss caused by the increased variation of an urban economy, it analyzes and explicates the position of Internet of Things (IoT) technology in the macro-economic alert system. This reviews the development path of financial monitoring and early alerting system whichsorts out and compares numerous popular economic monitoring techniques.Depending on IoT technology, the urban economic reports surveillance & advance warning system has conducted research on information transfer, tracking, forecasts, analysis, and presentation. After simulating the system, the proposed research's findings indicate that the overall accuracy of the model's macroeconomic fluctuation forecast approaches 80%, which may be used in real life. © 2022 The Authors
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