Cloud Computing in Smart Energy Meter Management

G. Senbagavalli, PhD, is an associate professor in the Department of Electronics and Communication Engineering, AMC Engineering College, Bengaluru, India with over 18 years of experience in teaching and research. She has published three patents, two book chapters, and 15 papers in national and international conferences and journals. She is also a lifetime member of the International Society for Technology in Education and the Institution of Electronics and Telecommunications Engineers. Her research interests include image and video processing, computer vision, machine learning, and VLSI Design. T. Kavitha, PhD, is a professor in the Department of Electronics and Communication Engineering, AMC Engineering College, Bengaluru, India with over twenty years of experience in teaching and research. She has published five patents, two book chapters, 15 papers in international journals, and over 30 papers in national and international conferences. She is also a lifetime member of the International Society for Technology in Education and the Institution of Engineers (India). Her research interests include wireless networks, wireless sensor networks, information security, Internet of Things, deep learning, and machine learning. N. Amuthan, PhD, is a professor at AMC Engineering College, Bengaluru, India with over 22 years of teaching experience. He has over 26 publications in reputed national and international conferences, workshops, and journals and serves as a reviewer for various national and international journals. He is also a member of numerous national and international committees and societies. His research interests include power electronics, energy conservation, auditing, renewable energy sources, and implementation of the cloud for integration at the national level. Ferdin Joe John Joseph, PhD, is an assistant professor in the Department of Information Technology at the Thai Nichi Institute of Technology, Bangkok with over a decade of teaching experience. He has several publications in international journals and conferences and has been designated as a Most Valuable Professional with Alibaba Cloud. His areas of research include deep learning, Internet of Things, and Cloud AI.

• List of Contributors xviiPreface xxiii1 Fundamentals of Smart Meter 1G. Senbagavalli, T. Kavitha and S.T. Bibin Shalini1.1 Introduction 11.2 Advanced Metering Infrastructure (AMI) 31.3 Types of Smart Meters 61.4 Meter Standards 81.5 Testing and Maintenance of Smart Meters 111.6 AMI Data Management Services 121.7 Demand Response 141.8 Cloud Services 161.9 Security in Smart Meters 201.10 Case Studies 21Conclusion 27References 282 Empowering Consumers and Utilities for a Smarter Future: The Pivotal Role of Advanced Metering Infrastructure (AMI) in Smart Meter Technology 31N. Amuthan, M. Sathya and Nisha C. Rani2.1 Introduction 322.2 AMI Architecture 362.3 How AMI Works? 392.4 Architecture and Components of AMI 412.5 AMI Protocols—Standards and Initiatives 452.6 Home Area Network 472.7 Neighborhood Area Network (NAN) 522.8 Functions of Head End Systems 552.9 Meter Data Management 562.10 AMI System Design/MDAS/MDMS 562.11 Metering Head End Design 572.12 Conclusion 62References 633 Demystifying Smart Meters: Powering the Next-Generation Grid 67M. Marsaline Beno, N. Sivakumar and R. Saravanan3.1 Introduction 673.2 Exploring the Emerging Functionalities of Smart Meters 693.3 Smart Metering Infrastructure 713.4 Communication Technology for Smart Metering Applications 763.5 Regulatory Framework for Smart Meter Deployment 793.6 Benefits of Smart Meters in Grid Modernization 803.7 Hardware of Smart Meter 823.8 Smart Meters and Consumer Empowerment 853.9 Smart Meter Using Internet of Things Technology 853.10 A Meter Using Cloud and Edge Computing 873.11 Wide-Area Network for Smart Energy Meters 883.12 Smart Meter in Internet of Energy (IoE) 893.13 Implementation Strategies for Smart Meters in IoE 903.14 Future Prospects and Innovations in Smart Meter Technology 923.15 Conclusion 93References 954 Communication and Networking in Advanced Metering 99N. Palani Karthik, Behara Mohith and Vallidevi Krishnamurthy4.1 Olden Days Electric Meter 1004.2 Government Initiative for Smart Meter 1014.3 Introduction: Networking and Communication 1044.4 IoT with Smart Meters 1074.5 Connectivity of Smart Meters 1084.6 Electric Utility Commission Architecture 111  4.7 Technology Selection in Advanced Metering Architecture 1194.8 Case Study of Smart Meter Using RF 1224.9 Why RF is Better Than Other Technologies Like 2G, 3G, and 4G 1274.10 Concise Use of RF and WAN 1294.11 Conclusion 131References 1335 Meter Data Acquisition Using Cloud Computing 137S. P. Angelin Claret and B. Prashanthi5.1 Introduction 1385.2 Literature Review 1395.3 Methodology and Implementation of Smart Meters Using Cloud Platform 1425.4 Machine Learning Algorithms for Advanced Metering 1465.5 Applications of Cloud Data Acquisition for Smart Meters 1505.6 Implementing OSS Layer for Smart Meters 1525.7 Challenges and Opportunities of Smart Metering with Cloud-Based Data Acquisition 1555.8 Future Directions of Smart Metering with Cloud-Based Data Acquisition 1595.9 Conclusion and Summary of Key Findings 164References 1666 Smart Energy Meter Data Management in the Cloud Hadoop, SQL, HBase 169B. Priya Esther, Priya Boopalan and P. Velrajkumar6.1 Introduction to Data Management 1706.2 Benefits of Data Management 1746.3 Significant Benefits of Smart Energy Meter Data Management 1776.4 Challenges of Data Management 1786.5 Solutions and Strategies for Effective SEM Cloud Data Management 1826.6 Challenges in Data Management for Smart Energy Meter 1856.7 Importance of Data Management for Smart Energy Meter 1866.8 Data Management for Smart Energy Meter Architecture 1876.9 Role of Cloud Computing in Data Management for Smart Energy Meter 1876.10 Data Management for Smart Energy Meter in the Cloud 1886.11 Smart Energy Meter Data Management Using Hadoop 1896.12 Storing and Accessing Smart Energy Meter Data Using SQL Databases 1916.13 Storing and Accessing Smart Energy Meter Data Using HBase 1926.14 Modern Technology for a Modern Grid 1936.15 Benefits of Using a Managed Service in the Cloud 1946.16 Capabilities of the Highest Order in Data Analytics and Machine Learning 1956.17 Case Studies of Successful SEM Cloud Data Management 1986.18 Future Trends and Advancements in SEM Cloud Data Management 200Conclusion 202References 2047 Smart Energy Meter Data Processing and Billing 207S. Jeyadevi and Kalyani7.1 Billing System 2087.2 Big Data Analytics in Smart Metering 2187.3 Data Flow From Smart Meter to Billing System 2247.4 Security in Smart Metering System 2287.5 Integrating Legacy Metering Infrastructure Into Smart Metering Systems 2337.6 Conclusion and Future Scope 236References 2368 Smart Meter Security—Fraud Detection in Power Theft 239B. Devi Vighneswari and Kothai Andal C.8.1 Introduction 2408.2 Different Aspects of Smart Meter Security 2418.3 Data Privacy and Encryption 2438.4 Authentication and Authorization 2458.5 Firmware and Software Updates 2478.6 Physical Security 2488.7 Network Security 2508.8 Remote Access Control 2528.9 Device Identity Management 2548.10 Anomaly Detection 2558.11 Regulatory Compliance 2578.12 User Understanding and Directions 2598.13 Conclusion 260References 2619 Cybersecurity in ICT-Enabled Smart Metering Systems: Addressing Challenges and Implementing Solutions 263J. Selvin Paul Peter, C. Rajesh Babu and B. Priya Esther9.1 Introduction 2649.2 Cyber Attack in Smart Meters 2659.3 Blockchain in Smart Meters 2669.4 IoT-Enabled Smart Meters 2739.5 Navigating the Complex Landscape of Smart Grid Communications 2809.6 Securing Smart Meters 2839.7 Conclusion 287References 28810 Challenges in Smart Metering 291R. Selvamathi, V. Indragandhi and N. Amuthan10.1 Introduction 29210.2 Growth of Smart Meter 29410.3 Challenges in the Replacement of Existing Meters with Smart Meters with Prepayment 30010.4 Technology Challenges in Smart Metering 30610.5 Operational Challenges 31310.6 Case Study 314References 31511 Quality of Service (QoS) Protocol in Advanced Metering Infrastructure (AMI) 319Robin Rohit Vincent, Nisha F. and Rose Priyanka11.1 Introduction to QoS in AMI 32011.2 Background 32111.3 Smart Grid System 32411.4 Proposed Research Contribution 32511.5 Survey Related to QoS of AMI With Smart Grid 32611.6 Proposed Deep Learning-Based Optimization Model 32711.7 Modeling a System and Formulating a Problem 33811.8 Strategy Performed Along with Terms of Effectiveness as Well as Quick Confluence 34211.9 Results, Discussion, Findings, and Analysis 34311.10 Conclusion 346References 34612 Web Services/Mobile Application to Monitor the Smart Meter Data 349Jarin T., Muniraj Rathinam, Ulaganathan M., Aswin V. M. and Jithin K. Jose12.1 Introduction 34912.2 Comparison of Kilowatt-Hour Meter and Smart Meter 35912.3 Mobile Applications for Smart Meter Data 36212.4 Comparison of Different Factors 36512.5 Conclusion 36712.6 Future Scope 368References 36813 Advanced Smart Prepaid Meter 371Ezhilarasi P., Ramesh L., Balamurugan J. and J.B. Holm-Nielsen13.1 Introduction 37213.2 Literature Review 37913.3 Cost-Efficient Futuristic M2M Smart Prepaid Meter 38613.4 Smart Metering Results 40713.5 Conclusions with Future Research Scopes 415References 41614 Edge Computing and Cyber-Physical System in Smart Meter 419Revathi M., Udayakumar K. and Prabhakaran M. V.14.1 Introduction 42014.2 Literature Survey 42214.3 Smart Meter Components and Their Architecture 42414.4 Smart Meter Data Analytics on Edge Devices 42714.5 Smart Metering Infrastructure 42814.6 IoT-Enabled Smart Meter 43214.7 An Overview of Cyber-Physical System 43414.8 Case Study and Application 43814.9 Challenges and Future Research Scopes 44214.10 Conclusion 448References 44915 Case Study on Real-Time Smart Meter 453Yasha Jyothi M. Shirur, Bindu S. and Jyoti R. Munavalli15.1 Introduction 45415.2 Literature Review 45815.3 Case Study 1: Smart Energy Monitoring 46115.4 Case Study 2: Power Theft 46815.5 Conclusion 482Acknowledgments 483References 484About the Editors 487Index 489