IoT and IoE Driven Smart Cities

• Autore: Nath Sur Samarendra
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2022; ©2022
• Descrizione fisica: 1 online resource (328 pages)
• Soggetto genere / forma: Electronic books.
• Altri autori: BalasValentina Emilia BhoiAkash Kumar NayyarAnand
• Nota di contenuto: Intro -- Preface -- Contents -- About the Editors -- 1 A Comprehensive Review on Physical Layer Design for Smart Cities -- 1.1 Introduction -- 1.2 Backscatter Communication Systems -- 1.3 RIS -- 1.4 Physical Layer Security -- 1.5 A Case Study of Backscatter System -- 1.5.1 Outage Probability Analysis -- 1.5.2 OP of D1 -- 1.5.3 OP of BD -- 1.5.4 OP of D2 -- 1.5.5 Numerical Results -- 1.6 Conclusion -- References -- 2 An Overview of Security and Privacy in Smart Cities -- 2.1 Introduction -- 2.2 Smart City Architecture -- 2.2.1 The IoT in Smart City -- 2.3 Smart City Applications -- 2.4 Smart City Characteristics -- 2.5 Security and Privacy Challenges -- 2.5.1 Privacy Challenges in Smart Cities -- 2.5.2 Countermeasures and Solutions for Securing Smart City -- 2.6 Related Work -- 2.6.1 Summary -- 2.7 Challenges and Future Directions -- 2.8 Conclusion -- References -- 3 Multi-antenna-Enabled Technologies for IoT-Driven Smart Cities -- 3.1 Introduction -- 3.2 Multi-antenna Systems -- 3.2.1 SISO -- 3.2.2 SIMO -- 3.2.3 MISO -- 3.2.4 MIMO -- 3.2.5 Massive MIMO -- 3.2.6 Smart Antenna -- 3.2.7 Signal Processing in Multi-antenna Systems -- 3.3 Benefits of Multi-antenna System in IoT -- 3.4 Multi-antenna-Enabled Technologies -- 3.4.1 5G -- Millimeter-Wave Radio Access Technology in 5G -- 5G Architecture -- 3.4.2 Beyond 5G -- AI and ML in Telecom Networks -- 3.4.3 New Wi-Fi, WiMAX Versions -- 3.4.4 Vehicular Networks -- 3.5 Smart Cities' Case Studies -- 3.5.1 Smart City of Toronto -- 3.5.2 Smart City of Santiago -- 3.6 Conclusion -- 3.7 Future Scope -- References -- 4 Design of Multiple Access Network by Enabling User Grouping and Energy Harvesting in Relaying System for Smart Cities -- 4.1 Introduction -- 4.2 System Model -- 4.3 Outage Probability -- 4.3.1 Outage Probability of U1 -- 4.3.2 Outage Probability of U2 -- 4.3.3 The Imperfect SIC at U1. ; 4.3.4 The Benchmark: OMA -- 4.4 Numerical and Simulation Results -- 4.5 Conclusion -- Appendix A -- Proof of Proposition 1 -- Appendix B -- Proof of Proposition 2 -- Appendix C -- Proof of Proposition 3 -- Appendix D -- Proof of Proposition 4 -- References -- 5 Examining the Adoption and Application of Internet of Things for Smart Cities -- 5.1 Introduction -- 5.1.1 IoT Technologies for Smart Cities -- 5.2 Concepts of Smart City -- 5.3 Motivations for Smart City Development in the Developing Countries -- 5.4 Requirements of IoT Platform for Smart City -- 5.5 Taxonomy of IoT-Based Smart City -- 5.6 IoT Platform for Smart Cities -- 5.6.1 DIMMER -- 5.6.2 FLEXIMETER -- 5.7 Issues and Challenges -- 5.8 Applications of IoT in Smart Cities -- 5.9 Case Studies -- 5.10 Conclusion -- References -- 6 Internet of Everything (IoE) in Smart City Paradigm Using Advanced Sensors for Handheld Devices and Equipment -- 6.1 Introduction -- 6.1.1 Need for Smart City -- 6.1.2 IoE and the Smart City -- 6.1.3 Organization -- 6.2 Areas of Smart City Applications and Services -- 6.2.1 Smart Buildings -- 6.2.2 City Air Management Tool (CyAM) -- Consequences -- 6.2.3 Traffic Management -- 6.2.4 Smart Parking -- 6.2.5 Smart Waste Management -- 6.3 Big Data, IoT, and Cloud Computing -- 6.4 Smart City Smart Infrastructure (2010-2023*) -- 6.5 Blockchain Technology in IoT -- 6.5.1 Scenario 1 -- 6.5.2 Scenario 2 -- 6.6 Energy-Efficient Wireless Network -- 6.7 WSN in Smart Cities -- 6.8 Areas of WSN Application -- 6.8.1 Touchscreen -- 6.8.2 Motion Sensors -- 6.8.3 Multimedia Sensors -- 6.8.4 Barometer -- 6.8.5 Ambient Light Sensor -- 6.9 Conclusion -- References -- 7 Machine Learning and Deep Learning Algorithms for Smart Cities: A Start-of-the-Art Review -- 7.1 Introduction -- 7.2 Application of Internet of Things (IoT) in Smart Cities -- 7.2.1 IoT Technologies in Smart Cities. ; 7.3 Machine Learning and Deep Learning Algorithm Technology for Smart Cities -- 7.4 The Relevance of Machine Learning and Deep Learning in Urban Sustainability and Smart Cities -- 7.4.1 Smart City-Supporting Technologies -- 7.5 The Research Challenges of Machine Learning and Deep Learning in Urban Sustainability and Smart Cities -- 7.5.1 The Futuristic Solutions in Using Machine Learning and Deep Learning in Smart City Implementations -- 7.6 Conclusion and Future Directions -- References -- 8 A Framework for the Actualization of Green Cloud-Based Design for Smart Cities -- 8.1 Introduction -- 8.2 The Prospect and Challenges of the Smart Cities -- 8.2.1 The Features of Smart Cities -- 8.2.2 Green Smart IoT-Cloud-Based Smart Cities -- 8.2.3 Prospect and Challenges of Smart Cities -- 8.3 Literature Review -- 8.4 Framework for Green Smart Cities -- 8.4.1 Green IoT/IoE Devices -- 8.4.2 Fog Infrastructure -- 8.4.3 The Cloud Services -- 8.4.4 Smart City -- 8.5 Conclusion and Future Directions -- References -- 9 Design of a Confidentiality Model Using Semantic-Based Information Segmentation (SBIS) and Scattered Storage in Cloud Computing -- 9.1 Introduction -- 9.1.1 Organization -- 9.2 Preface -- 9.3 Genesis -- 9.4 Contributions -- 9.5 Safety Prototypes and System Framework -- 9.6 Safeguarding Information Without Negotiating Confidentiality -- 9.6.1 Illustration 1 (Background Refinement) -- 9.6.2 Illustration 2 ((di, O(di))) Refinement -- 9.7 Subcontracting of Information with Confidentiality Assurance Using Background Refinement and Semantic Data Segmentation -- 9.7.1 Identification of Precarious Words -- 9.7.2 Illustration 3 (Data Hypothesis for Background Refinement) -- 9.7.3 Algorithm: Identification of Precarious Words -- 9.7.4 Semantic-Based Information Segmentation and Scattered Storage -- 9.7.5 Data Segmentation and Scattered Storage. ; 9.8 Performance Analysis -- 9.8.1 Evaluation Parameters -- 9.9 Results and Discussion -- 9.10 Summary -- 9.11 Conclusion -- 9.12 Future Scope -- References -- 10 Maintaining IoT Healthcare Records Using Cloud Storage -- 10.1 Introduction to Healthcare Records -- 10.2 Introduction to Cloud Storage -- 10.3 Challenges in Storing Healthcare Records on Cloud -- 10.3.1 Technical Challenges -- 10.3.2 Nontechnical Challenges -- 10.4 Challenges in IoT-Cloud-Based Healthcare Innovations -- 10.4.1 Smart Cities' Healthcare Using IoT -- 10.5 Providing Security to Cloud-Stored Healthcare Records -- 10.5.1 Access Control Model -- 10.5.2 Crypt Databases -- 10.6 IoT and Cloud Integration in Medical Healthcare -- 10.7 Integration with AI for Identification of Hidden Patterns -- 10.8 Conclusion -- References -- 11 Use of IoT in Net-Zero Smart City Concept in the Indian Context: A Bibliographic Analysis of Literature -- 11.1 Introduction -- 11.2 Methodology: Bibliographic Analysis of Literature -- 11.3 Literature Review -- 11.3.1 Planning Factors and Use of IoT -- 11.3.2 Role of Citizen Engagement and Community Participation in Net-Zero City -- 11.3.3 Energy and Transportation and Use of IoT -- 11.4 Findings and Discussions -- 11.4.1 Factorial Analysis -- 11.4.2 Three-Field Plot -- 11.4.3 Thematic Evolution and Thematic Map -- 11.4.4 Tree Mapping Analysis and Word Growth Analysis -- 11.4.5 Country Collaboration Map -- 11.5 Conclusion -- References -- 12 Smart and Innovative Water Conservation and Distribution System for Smart Cities -- 12.1 Introduction -- 12.2 Literature Review -- 12.2.1 Source of Groundwater -- 12.2.2 Community Water Treatment -- 12.2.3 Water Distribution Pattern -- 12.2.4 Water Tanks -- Types of Tanks -- 12.2.5 Water Stress in Smart Cities -- 12.3 Component Details -- 12.3.1 Automated Valve -- 12.3.2 Flow Sensor -- 12.3.3 Types of Flow Sensor. ; 12.4 NodeMCU -- 12.5 Proposed System -- 12.5.1 Working of Smart Tap -- 12.5.2 Working on Regular Mode -- 12.5.3 Saver Mode -- 12.5.4 Mobile Application -- 12.5.5 Nozzle Selection -- 12.5.6 Conservation Statistics -- 12.6 Conclusion -- References -- 13 IoT Technology-Based Urban Water Management Strategies Using Indian Traditional Knowledge System -- 13.1 Introduction -- 13.2 Research Methodology -- 13.3 Literature Review -- 13.3.1 Importance of Indian Traditional Water Management Knowledge -- 13.3.2 Review of AMRUT Scheme -- 13.3.3 IoT in Water Management -- 13.3.4 Benefits of Using IoT and Traditional Knowledge in Water Management -- 13.3.5 Role of Community in Resource Management and Sustainability -- 13.4 Study Area -- 13.4.1 Water Problems in the Selected Cities -- 13.4.2 Integrating Existing Schemes, Programs, and Policies -- 13.5 Discussion and Strategies -- 13.6 Conclusion -- References -- 14 X-IoT: Architecture and Use Cases for an IoT Platform in the Area of Smart Cities -- 14.1 Introduction -- 14.2 Role of Data and AI Platform in Collecting IoT Information in Smart Cities -- 14.2.1 Literature Overview -- 14.2.2 Practitioner Context -- 14.2.3 Safe, Healthy, and Livable: Smart City Solutions and Problems They Solve -- 14.2.4 Real-Life Implementation Examples -- 14.3 Typical Technological Challenges and Best Practices of Smart City Implementations -- 14.3.1 Data Availability, Open Data, and Data Sharing -- 14.3.2 Data Governance and Life Cycle Management for Smart Cities -- 14.3.3 Operationalization of Analytics and AI for Smart Cities -- 14.3.4 Reference Architecture for Data and AI Platform: X-IoT by Capgemini -- 14.4 Conclusion and Outlook -- References -- Index.
• Titolo autorizzato: IoT and IoE Driven Smart Cities
• ISBN: 9783030827151; 9783030827144
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910513580303321
• Lo trovi qui: Univ. Federico II
• Serie: EAI/Springer Innovations in Communication and Computing Ser.