Green mobile cloud computing / / edited by Debashis De, Anwesha Mukherjee, and Rajkumar Buyya

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (316 pages)
• Disciplina: 004.6782
• Soggetto topico: Mobile computing; Cloud computing
• Persona (resp. second.): MukherjeeAnwesha; BuyyaRajkumar; DeDebashis
• Nota di contenuto: Intro -- Contents -- Part I Mobile Cloud Computing -- Green Mobile Cloud Computing for Industry 5.0 -- 1 Introduction -- 2 Architecture of MCC -- 2.1 Service-Oriented Architecture -- 2.2 Agent - Client Architecture -- 2.3 Collaborative Architecture -- 2.4 Fog-Edge Architecture -- 3 Applications of MCC -- 3.1 Mobile Learning -- 3.2 Mobile Commerce -- 3.3 Mobile Healthcare -- 3.4 Mobile Game -- 4 Simulators of MCC -- 5 Research Challenges of MCC -- 5.1 Mobility Management -- 5.2 Offloading Method -- 5.3 Security and Privacy -- 5.4 Cost and Business Model -- 5.5 Deployment of Agents -- 5.6 Context-Aware Service Provisioning -- 5.7 Mobile Data Management -- 5.8 Energy-Efficiency -- 5.9 Resource Management -- 5.10 Integration of MCC with IoT -- 6 Green Mobile Cloud Computing -- 7 Summary and Conclusions -- References -- Optimization of Green Mobile Cloud Computing -- 1 Introduction -- 1.1 MCC Definition -- 1.2 Edge, Fog Computing and Cloudlet -- 2 Energy-Aware Algorithms in MCC -- 2.1 Content Caching -- 2.2 Computational Offloading -- 2.2.1 Energy-Aware Offloading Modeling -- 2.2.2 Green Offloading Algorithms -- 3 Energy-Aware Key Technologies in MCC -- 3.1 Energy-Aware NFV Deployment -- 3.2 Energy-Aware SDN-Enabled MCC -- 4 Renewable Energy Based MCC -- 4.1 Renewable Energy-Based MCC Risk Issues -- 4.2 Renewable Energy and MCC Functionalities -- 4.2.1 Computing (Task Scheduling and Offloading) -- 4.2.2 Content Caching -- 5 Energy-Aware Algorithms for Devices -- 6 Green AI-Based Algorithms -- 6.1 Traditional ML and Heuristic Algorithms -- 6.2 Deep Learning-Based Algorithms -- 6.3 Advanced ML Algorithms -- 7 Challenges and Future Works -- 8 Conclusion -- References -- Part II Green Mobile Cloud Computing -- Energy Efficient Virtualization and Consolidation in Mobile Cloud Computing -- 1 Introduction -- 2 Motivation -- 3 Basics MCC. ; 3.1 Architecture of MCC -- 3.2 Characteristics of MCC -- 3.3 Advantages of MCC -- 3.4 Applications of MCC -- 4 Energy Efficient Techniques -- 4.1 Energy Efficiency of Mobile Devices -- 4.2 Limited Battery Lifetime of Mobile Devices -- 4.3 Resource Scheduling -- 4.4 Task Offloading -- 4.5 Load Balancing -- 4.6 Resource Provisioning -- 5 Research Challenges -- 6 Future Research -- 7 Conclusion -- References -- Multi-criterial Offloading Decision Making in Green Mobile Cloud Computing -- 1 Introduction -- 2 Aspects of Decision-Making Regarding Offloading -- 3 Decision Making Regarding Offloading: When, What, Where and How to Offload -- 3.1 When to Offload -- 3.2 What to Offload -- 3.3 Where to Offload -- 3.4 How to Offload -- 4 Multi Criteria Decision Making (MCDM) -- 4.1 Analytical Hieratical Process (AHP) -- 4.2 Analytical Network Process (ANP) -- 4.3 Technique for Order of Preferences by Similarity to Ideal Solution (TOPSIS) -- 4.4 VIekriterijumsko KOmpromisno Rangiranje (VIKOR) -- 4.5 Tomada de decisaointerativa e multicritévio (TODIM) -- 4.6 Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) -- 4.7 ELimination Et Choix Traduisant la REalit´e (ELECTRE) -- 4.8 Grey Relational Analysis (GRA) -- 5 Use of MCDM in Offloading -- 6 Conclusion -- References -- 5G Green Mobile Cloud Computing Using Game Theory -- 1 Introduction -- 2 Advantages of Mobile Cloud Computing -- 3 The Use of Game Theory in Mobile Data Offloading -- 4 Utility Function and Game Table for Mobile Task Offloading -- 5 The Use of Game Theory in 5G Wireless Networks -- 6 Utility Function and Game Table for 5G Wireless Networks in Spectrum Allocation -- 7 The Use of Game Theory in Cloud Resource Allocation -- 8 Utility Function and Game Table for Non-Cooperative Game used in Cloud Resource Allocation -- 9 Mathematical Model -- 9.1 Delay -- 9.2 Power Consumption. ; 10 Result and Discussions -- 10.1 Delay -- 10.2 Power Consumption -- 11 Summary of Games and Mobile Cloud Computing -- 11.1 Games for Task Offloading -- 11.2 Games for 5G Wireless Networks -- 11.3 Games for MCC Resource Allocation -- 12 Future Scope -- 13 Conclusion -- References -- Security Frameworks for Green Mobile Cloud Computing -- 1 Introduction -- 2 Existing Frameworks -- 2.1 Data Security Framework -- 2.1.1 Data Security Framework Proposed by Patel et al. [19] -- 2.1.2 Data Security Framework Proposed by Zhou and Huang [23] -- 2.2 Access Control Framework -- 2.2.1 System Architecture of Li et al.'s Dynamic Attributes Based Conventional Access Control -- 2.2.2 Static and Dynamic Attribute-Based Access Control Strategy for Collective Attribute Authorities -- 2.3 Communication Framework -- 2.3.1 Benefits of GMCC Communication Framework -- 2.3.2 Some Issues in GMCC Communication Framework -- 3 Security Challenges in Green Mobile Cloud Computing (GMCC) Frameworks -- 3.1 Data Security Challenges -- 3.2 Virtualization Security Challenges -- 3.3 Mobile Cloud Applications Security Challenges -- 3.4 Privacy Challenges -- 3.5 Partitioning and Offloading Security Challenges -- 4 Conclusion -- References -- Part III Applications and Future Research Directions of Green Mobile Cloud Computing -- Sustainable Energy Management System Using Green Smart Grid in Mobile Cloud Computing Environment -- 1 Introduction -- 2 Mobile Cloud Computing and Smart Grid Overview -- 2.1 Mobile Cloud Computing -- 2.2 Smart Grid -- 2.3 Smart Metering -- 2.4 Micro Grid -- 3 Mobile Cloud Computing Key Requirements for Energy Efficiency -- 4 Architecture of Mobile Cloud Computing -- 5 MCC Advantages for Green Smart Grid -- 6 Integration of MCC in Green Smart Grid -- 7 Security Prospects of Green Energy Management -- 8 Future Scope -- 9 Conclusion -- References. ; Geospatial Green Mobile Edge Computing: Challenges, Solutions and Future Directions -- 1 Introduction -- 2 Mobile Computing Paradigms -- 3 Existing Geospatial Applications on Mobile Edge Computing -- 3.1 Smart City Services -- 3.1.1 Traffic Prediction and Road Safety -- 3.1.2 Health Care Service -- 3.1.3 Environment Monitoring -- 3.2 Disease Monitoring -- 3.3 Disaster Monitoring -- 3.4 Tourism Monitoring -- 3.5 Geospatial Data Collection and Query Processing -- 4 Existing Energy Efficient Methods in Mobile Edge Computing -- 5 Challenges in Geospatial Mobile Edge Computing -- 6 Future Directions -- 7 Summary -- References -- Dynamic Voltage and Frequency Scaling Approach for Processing Spatio-Temporal Queries in Mobile Environment -- 1 Introduction -- 2 Related Work -- 3 Spatio-Temporal Query Processing and Experimentation on Two Dataset -- 4 Energy and Power-Aware Spatio-Temporal Query Processing -- 5 Conclusion and Future Directions -- References -- Green Cloud Computing for IoT Based Smart Applications -- 1 Introduction -- 1.1 Motivation -- 1.2 Contribution -- 2 Related Works -- 3 Mobile Computing -- 4 Green Cloud Computing -- 5 Approaches for Green Computing -- 6 Towards Green Fog Computing -- 7 Virtualization -- 8 Fog Serves a more Green Purpose -- 9 IoT Use Cases in Green Computing -- 9.1 Green IoT Outdoor Lights -- 10 Scope for Future Research -- 11 Conclusion -- References -- Green Internet of Things Using Mobile Cloud Computing: Architecture, Applications, and Future Directions -- 1 Introduction -- 2 Architecture of MCC -- 3 Delay and Power Consumption of IoT-MCC Based Network -- 4 Contribution of IoT- MCC Convergence -- 5 Applications of IoT- MCC -- 6 Enabling Technologies for Green IoT-MCC -- 7 Energy Harvesting Techniques for Green IoT -- 8 Future Research Directions of IoT-MCC -- 9 Conclusion -- References. ; Predictive Analysis of Biomass with Green Mobile Cloud Computing for Environment Sustainability -- 1 Introduction -- 2 Mobile Cloud -- 3 Green Cloud Computing -- 4 Biomass and Their Composition -- 4.1 Wood and Agriculture Products -- 4.2 Solid Wastes -- 4.3 Landfill Gas and Biogas -- 4.4 Alcohol Fuels -- 5 Procedure -- 5.1 Data Mining/Collecting -- 5.2 Data Cleaning and Preprocessing -- 5.3 Exploratory Data Analysis (EDA) -- 5.4 Data Splitting -- 5.5 Selection & -- Application of Suitable Algorithm -- 5.6 Obtaining Result and Model Evaluation -- 5.7 Model Creation and Deployment into Cloud -- 5.8 Testing the Overall Process -- 6 Software Required -- 7 Cloud Server -- 8 Data Analysis Using Python -- 8.1 Gross Residue Potential -- 8.2 Bioenergy Potential -- 9 Algorithm -- 10 Deployment of the Model -- 10.1 File Upload Algorithm -- 10.2 File Download Algorithm -- 11 Dataset Used -- 12 Exploratory Data Analysis (EDA) -- 13 Advantage -- 14 Conclusion -- 15 Future Scope -- References -- 6G Based Green Mobile Edge Computing for Internet of Things (IoT) -- 1 Introduction -- 2 5G and Beyond 5G for Internet of Things -- 2.1 Protocols for Green IoT -- 2.2 MQTT Protocol -- 2.3 gRPC Protocol for Edge, Cloud Microservices -- 2.4 IoT Application Development -- 2.4.1 Edge Level Buffer -- 2.4.2 Dew Level Buffering -- 2.5 Green IoT Challenges -- 2.6 Network Slicing Under 6G Mobile Edge -- 3 Sustainable Green Sensing -- 3.1 WSNs Application Perspective -- 3.2 Energy Efficient Sensor Networks Integrating 5G & -- 6G -- 4 Federated Learning for 6G Mobile Network -- 4.1 FL Based Mobile Edge Computing in the 6G Era Has the Following Benefits -- 4.2 Artificial Intelligence of Things for Edge Enabled Mobile Computing -- 5 Conclusion -- References -- Resource Management for Future Green Mobile Cloud Computing -- 1 Introduction. ; 2 Architectures and Resource Management Challenges in GMCC.
• Titolo autorizzato: Green mobile cloud computing
• ISBN: 3-031-08038-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996495564303316
• Lo trovi qui: Univ. di Salerno