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Green mobile cloud computing / / edited by Debashis De, Anwesha Mukherjee, and Rajkumar Buyya
| Green mobile cloud computing / / edited by Debashis De, Anwesha Mukherjee, and Rajkumar Buyya |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (316 pages) |
| Disciplina | 004.6782 |
| Soggetto topico |
Mobile computing
Cloud computing |
| ISBN | 3-031-08038-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNISA-996495564303316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Green Mobile Cloud Computing / / edited by Debashis De, Anwesha Mukherjee, Rajkumar Buyya
| Green Mobile Cloud Computing / / edited by Debashis De, Anwesha Mukherjee, Rajkumar Buyya |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (316 pages) |
| Disciplina | 004.6782 |
| Soggetto topico |
Cloud Computing
Mobile computing Mobile Computing |
| ISBN | 3-031-08038-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Section I: Mobile Cloud Computing -- Green Mobile Cloud Computing for Industry 5.0 -- Optimization of green mobile cloud computing -- Section II: Green Mobile Cloud Computing -- Energy Efficient Virtualization and Consolidation in Mobile Cloud Computing -- Multi-Criterial Offloading Decision Making in Green Mobile Cloud Computing -- G Green Mobile Cloud Computing using Game Theory -- Security Frameworks for Green Mobile Cloud Computing -- Section III: Applications and Future Research Directions of Green Mobile Cloud Computing -- Sustainable Energy Management System using Green Smart Grid in Mobile Cloud Computing Environment -- Geospatial Green Mobile Edge Computing: Challenges, Solutions and Future Directions -- Dynamic Voltage and Frequency Scaling Approach for Processing Spatio-temporal Queries in Mobile Environment -- Green Cloud Computing for IoT based Smart Applications -- Green Internet of Things using Mobile Cloud Computing: Architecture, Applications and Future Directions -- Predictive Analysis of Biomass with Green Mobile Cloud Computing for Environment Sustainability -- 6G Based Green Mobile Edge Computing for Internet of Things (IoT) -- A Strategy for Advancing Research and Impact in New Computing Paradigms -- New Research Directions for Green Mobile Cloud Computing. |
| Record Nr. | UNINA-9910616371303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mobile edge computing / / Anwesha Mukherjee [and three others], editors
| Mobile edge computing / / Anwesha Mukherjee [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (598 pages) |
| Disciplina | 005.758 |
| Soggetto topico |
Edge computing
Mobile communication systems Artificial intelligence |
| ISBN | 3-030-69893-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Part I Foundations and Architectural Elements -- Introduction to Mobile Edge Computing -- 1 Introduction -- 2 Architecture of MEC -- 2.1 Edge Server Placement -- 2.2 Resource Allocation -- 3 Latency in MEC -- 4 Applications of MEC -- 5 Challenges in MEC -- 6 Summary -- References -- Performance Analysis of Mobile, Edge and Cloud Computing Platforms for Distributed Applications -- 1 Introduction -- 2 Overview of Cloud, Edge and Mobile Environments -- 3 System Architecture -- 4 System Model -- 4.1 Application Model -- 4.2 Task Execution Time Model -- 4.3 Mobile Device Energy Model -- 4.4 Monetary Cost Model -- 4.5 Overview of the Optimisation Technique -- 5 Experiment for Data-Intensive Application Offloading -- 5.1 Evaluation Metrics -- 5.2 Experimental Setup -- 5.2.1 Computing Resources -- 5.2.2 Workload Model -- 5.2.3 Network Model -- 5.3 Performance Evaluation -- 5.3.1 BoT Application Model -- 5.3.2 Workflow Application Model -- 5.3.3 IoT Application Model -- 6 Discussion and Recommendations -- 7 Conclusion and Future Work -- References -- Geospatial Edge-Fog Computing: A Systematic Review, Taxonomy, and Future Directions -- 1 Introduction -- 2 Existing Computing Paradigms -- 2.1 Geospatial Cloud Computing -- 2.2 Geospatial Cloudlet -- 2.3 Geospatial Mist Computing -- 2.4 Discussion -- 3 Taxonomy -- 3.1 Geospatial Computing -- 3.1.1 Resource Management -- 3.1.2 Service Management -- 3.2 Geospatial Data -- 3.3 Geospatial Analysis Procedures -- 3.4 Geospatial Applications -- 4 Existing Work on Geospatial Edge-Fog Computing: A Glance -- 5 Limitations in Geospatial Edge-Fog Computing -- 6 Future Directions -- 7 Summary -- References -- Study of Power Efficient 5G Mobile Edge Computing -- 1 Introduction -- 1.1 Properties of MEC -- 1.1.1 On-Premises Isolation -- 1.1.2 Proximity -- 1.1.3 Low-Latency.
1.1.4 Location-Awareness -- 1.1.5 Network Context Information -- 1.2 Challenges of Mobile Edge Computing -- 1.2.1 Reliability and Mobility -- 1.2.2 Resource Allocation -- 1.2.3 Task Offloading -- 1.2.4 Power Efficiency -- 1.2.5 Security and Privacy -- 2 Factors of Power Efficient MEC Framework -- 3 Power Efficient Models for Mobile Edge Computing -- 3.1 Power Efficient Task Offloading Model for Mobile Edge Computing -- 3.1.1 Tasks Model -- 3.1.2 Local Computation Model -- 3.1.3 Edge Computation Model -- 3.2 Power Efficient Resource Allocation Strategy for MEC -- 3.2.1 Multiple-Access Model -- 3.2.2 NOMA-Enabled Model -- 4 Research Directions -- 5 Summary and Conclusions -- References -- SMEC: Sensor Mobile Edge Computing -- 1 Introduction -- 1.1 WSN with MCC -- 1.2 WSN with Mobile Edge Computing (MEC) -- 1.3 Research Motivation -- 2 Related Work -- 2.1 IoT Applications -- 2.2 Cloud Computing Applications -- 2.3 Fog Computing Applications -- 2.4 Mobile Edge Computing Applications -- 3 The Architecture of Sensor Mobile Edge Computing (SMEC) -- 3.1 Advantages of SMEC over SMCC -- 3.1.1 Definition of SMEC -- 3.2 Latency in SMEC -- 4 Application of SMEC -- 4.1 Vehicular Network -- 4.2 Augmented Reality Service -- 4.3 Home Monitoring -- 4.4 Healthcare -- 5 Future Scope -- 5.1 Bio-inspired SMEC -- 5.2 Big Data Analytics in SMEC -- 5.3 Security and Privacy Issues of SMEC -- 5.4 Dew Computing Based Context-Aware Local Computing -- 5.5 Resource Management -- 6 Conclusion -- References -- IoT Integration with MEC -- 1 Introduction -- 2 Chapter Organization -- 3 MEC Functionalities for IoT Services -- 3.1 Real-Time Analysis and Low Latency Functionality -- 3.2 Local Content/Caching Functionality -- 3.3 Computing Functionality -- 3.3.1 Offloading -- 3.3.2 Data Analytics -- 4 MEC API -- 5 Mobility Management -- 6 Benchmark -- 6.1 China Mobile -- 6.2 AT& -- T. 6.3 SKT -- 6.4 Deutsche Telekom -- 6.5 5GPPP -- 7 Challenges and Issues -- 8 Future Research Direction -- 9 Summary -- References -- Green-Aware Mobile Edge Computing for IoT: Challenges, Solutions and Future Directions -- 1 Introduction -- 1.1 MEC Characteristics -- 1.2 Need for Sustainable IoT Application Management in MEC -- 2 Green-Aware Framework for MEC -- 3 Problem Modelling: Green-Aware Offloading -- 3.1 Task Model -- 3.2 Green Energy Provisioning Model -- 3.3 Local Processing Model -- 3.4 Edge Processing Model -- 3.5 Optimal Green-Aware Offloading -- 4 State-of-the-Art Offloading Approaches -- 4.1 GS-MEC -- 4.2 LSDQN -- 4.3 LETOC -- 4.4 GreenEdge -- 4.5 GOLL -- 4.6 SOMEC -- 4.7 Discussions of the Investigated Work -- 5 Future Research Directions -- 6 Summary and Conclusions -- References -- Part II Systems, Platforms and Services -- Prescriptive Maintenance Using Markov Decision Process and GPU-Accelerated Edge Computing -- 1 Introduction -- 2 Related Work -- 2.1 Predictive Maintenance -- 2.2 Prescriptive Maintenance -- 3 System Design and Modelling -- 3.1 POMDP Model -- 3.2 Model Estimation and Decision Algorithm -- 4 Performance Evaluations -- 5 Evaluation Results -- 5.1 Application Performance -- 5.2 System Performance -- 6 Conclusion -- References -- Software-Defined Multi-domain Tactical Networks: Foundations and Future Directions -- 1 Introduction -- 1.1 Research Questions and Challenges -- 2 System Model and Taxonomy -- 3 Multi-controller Management -- 3.1 Bootstrapping -- 3.2 Network Partitioning -- 3.3 Networked Operating System (NOS) -- 4 Middleware and Interoperability -- 4.1 Syntactic -- 4.1.1 Communication Protocols -- 4.1.2 Tunneling and Non-tunneling -- 4.2 Semantic -- 4.2.1 Protocol Translation -- 4.2.2 Protocol Oblivious Forwarding -- 4.2.3 Semantic Ontology -- 5 Network Component Management -- 5.1 Topology Awareness. 5.2 Adaptive Load and Path Management -- 5.3 Network Slicing -- 5.4 Service Function Chaining (SFC) -- 5.5 Unikernel Network Functions -- 6 Traffic Management -- 6.1 Service Level Agreement (SLA)-Aware Traffic Management -- 6.2 Intent-Based Traffic Management -- 6.3 Context-Aware Traffic Management -- 7 Policy Evaluation -- 7.1 Empirical -- 7.2 Emulation -- 7.3 Simulation -- 8 Gap Analysis and Future Directions -- 9 Summary -- References -- Mobility driven Cloud-Fog-Edge Framework for Location-Aware Services: A Comprehensive Review -- 1 Introduction -- 2 Motivations and Related Computing Paradigms -- 3 Taxonomy: Cloud-Fog-Edge System -- 3.1 Infrastructure Protocol -- 3.2 Connectivity -- 3.3 Security Issues -- 3.4 Resource Provisioning -- 4 Taxonomy: Mobility Management -- 5 Taxonomy: Location-Aware Services -- 6 Conclusions and Future Research Directions -- References -- Mobility-Based Resource Allocation and Provisioning in Fog and Edge Computing Paradigms: Review, Challenges, and FutureDirections -- 1 Introduction -- 2 Existing Mobile Based Resource Provisioning and Allocation Mechanisms in Edge -- 3 Existing Mobile Based Resource Provisioning and Allocation Mechanisms in Fog -- 4 Modelling Techniques to Support Mobility to Enhance the QoS of the Applications -- 5 Mathematical Models for Mobility Based Resource Allocation -- 6 Application Use Cases -- 6.1 Vehicular Networks -- 6.2 Smart Healthcare -- 6.3 Smart Grid -- 6.4 Others -- 7 Future Direction of Mobility-Based Resource Allocation and Provisioning in Fog and Edge related Computing Paradigms -- 7.1 Mobility-Based Resource Allocation and Provisioning -- 7.2 Security and Privacy -- 7.3 Power Utilization and Management -- 7.4 Fault Tolerance -- 7.5 Support For Application Placements Strategies -- 7.6 Support Interoperability -- 7.7 Unified and Dynamic Resource Management and Provisioning. 8 Conclusion -- References -- Cross Border Service Continuity with 5G Mobile Edge -- 1 Introduction -- 2 Background and Related Work -- 2.1 Cloud Computing -- 2.2 Edge Computing -- 2.3 Service Continuity -- 2.4 SC for MEC -- 2.5 Emerging 5G as an Enabling Technology -- 3 Security Management for SC -- 3.1 Underlying Technologies -- 3.1.1 Distributed Ledger Technologies -- 3.1.2 Blockchain -- 4 5G-CARMEN -- 4.1 Architecture -- 4.2 SC in 5G-CARMEN -- 4.3 5G-CARMEN Use Cases -- 4.3.1 Cooperative Maneuvering -- 4.3.2 Situation Awareness -- 4.3.3 Green Driving -- 4.3.4 Video Streaming -- 5 Video Streaming SC Use Case Deployment -- 5.1 Software Deployment -- 5.1.1 Omnet++ Software Architecture -- 5.1.2 NS3 Software Architecture -- 5.2 Security Mechanisms -- 5.3 Proposed Prediction Algorithm Methods for SC -- 5.4 Develop and Setup a Lab Environment -- 5.5 Assessment -- 5.5.1 Omnet++ Simulation Evaluation -- 5.5.2 NS3 Simulation Evaluation -- 5.5.3 Simulator Evaluation Overview -- 6 Future Research Directions -- 7 Conclusions -- References -- Security in Critical Communication for Mobile Edge Computing Based IoE Applications -- 1 Introduction -- 2 Applications and Security -- 3 Architecture for MEC -- 3.1 Network Model -- 4 Possible Attacks and Cryptographic Solution -- 5 Secure Communication Protocol -- 5.1 Architecture -- 5.2 Protocol in Details -- 6 Other Security Protocols: A Comparison -- 7 Issues and Challenges to Design Security Protocols -- 8 Conclusion and Future Direction -- References -- Blockchain for Mobile Edge Computing: Consensus Mechanisms and Scalability -- 1 Introduction -- 1.1 MEC and Network Slicing -- 1.2 Integration of Blockchain and MEC -- 1.3 Related Works -- 1.4 Chapter Structure -- 2 Blockchain Technology: An Evolving Paradigm -- 2.1 Proof of Work -- 2.2 Proof of Useful Work -- 2.3 Proof of Stake. 2.4 Practical Byzantine Fault Tolerance. |
| Record Nr. | UNISA-996464514703316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Mobile edge computing / / Anwesha Mukherjee [and three others], editors
| Mobile edge computing / / Anwesha Mukherjee [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (598 pages) |
| Disciplina | 005.758 |
| Soggetto topico |
Edge computing
Mobile communication systems Artificial intelligence |
| ISBN | 3-030-69893-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Part I Foundations and Architectural Elements -- Introduction to Mobile Edge Computing -- 1 Introduction -- 2 Architecture of MEC -- 2.1 Edge Server Placement -- 2.2 Resource Allocation -- 3 Latency in MEC -- 4 Applications of MEC -- 5 Challenges in MEC -- 6 Summary -- References -- Performance Analysis of Mobile, Edge and Cloud Computing Platforms for Distributed Applications -- 1 Introduction -- 2 Overview of Cloud, Edge and Mobile Environments -- 3 System Architecture -- 4 System Model -- 4.1 Application Model -- 4.2 Task Execution Time Model -- 4.3 Mobile Device Energy Model -- 4.4 Monetary Cost Model -- 4.5 Overview of the Optimisation Technique -- 5 Experiment for Data-Intensive Application Offloading -- 5.1 Evaluation Metrics -- 5.2 Experimental Setup -- 5.2.1 Computing Resources -- 5.2.2 Workload Model -- 5.2.3 Network Model -- 5.3 Performance Evaluation -- 5.3.1 BoT Application Model -- 5.3.2 Workflow Application Model -- 5.3.3 IoT Application Model -- 6 Discussion and Recommendations -- 7 Conclusion and Future Work -- References -- Geospatial Edge-Fog Computing: A Systematic Review, Taxonomy, and Future Directions -- 1 Introduction -- 2 Existing Computing Paradigms -- 2.1 Geospatial Cloud Computing -- 2.2 Geospatial Cloudlet -- 2.3 Geospatial Mist Computing -- 2.4 Discussion -- 3 Taxonomy -- 3.1 Geospatial Computing -- 3.1.1 Resource Management -- 3.1.2 Service Management -- 3.2 Geospatial Data -- 3.3 Geospatial Analysis Procedures -- 3.4 Geospatial Applications -- 4 Existing Work on Geospatial Edge-Fog Computing: A Glance -- 5 Limitations in Geospatial Edge-Fog Computing -- 6 Future Directions -- 7 Summary -- References -- Study of Power Efficient 5G Mobile Edge Computing -- 1 Introduction -- 1.1 Properties of MEC -- 1.1.1 On-Premises Isolation -- 1.1.2 Proximity -- 1.1.3 Low-Latency.
1.1.4 Location-Awareness -- 1.1.5 Network Context Information -- 1.2 Challenges of Mobile Edge Computing -- 1.2.1 Reliability and Mobility -- 1.2.2 Resource Allocation -- 1.2.3 Task Offloading -- 1.2.4 Power Efficiency -- 1.2.5 Security and Privacy -- 2 Factors of Power Efficient MEC Framework -- 3 Power Efficient Models for Mobile Edge Computing -- 3.1 Power Efficient Task Offloading Model for Mobile Edge Computing -- 3.1.1 Tasks Model -- 3.1.2 Local Computation Model -- 3.1.3 Edge Computation Model -- 3.2 Power Efficient Resource Allocation Strategy for MEC -- 3.2.1 Multiple-Access Model -- 3.2.2 NOMA-Enabled Model -- 4 Research Directions -- 5 Summary and Conclusions -- References -- SMEC: Sensor Mobile Edge Computing -- 1 Introduction -- 1.1 WSN with MCC -- 1.2 WSN with Mobile Edge Computing (MEC) -- 1.3 Research Motivation -- 2 Related Work -- 2.1 IoT Applications -- 2.2 Cloud Computing Applications -- 2.3 Fog Computing Applications -- 2.4 Mobile Edge Computing Applications -- 3 The Architecture of Sensor Mobile Edge Computing (SMEC) -- 3.1 Advantages of SMEC over SMCC -- 3.1.1 Definition of SMEC -- 3.2 Latency in SMEC -- 4 Application of SMEC -- 4.1 Vehicular Network -- 4.2 Augmented Reality Service -- 4.3 Home Monitoring -- 4.4 Healthcare -- 5 Future Scope -- 5.1 Bio-inspired SMEC -- 5.2 Big Data Analytics in SMEC -- 5.3 Security and Privacy Issues of SMEC -- 5.4 Dew Computing Based Context-Aware Local Computing -- 5.5 Resource Management -- 6 Conclusion -- References -- IoT Integration with MEC -- 1 Introduction -- 2 Chapter Organization -- 3 MEC Functionalities for IoT Services -- 3.1 Real-Time Analysis and Low Latency Functionality -- 3.2 Local Content/Caching Functionality -- 3.3 Computing Functionality -- 3.3.1 Offloading -- 3.3.2 Data Analytics -- 4 MEC API -- 5 Mobility Management -- 6 Benchmark -- 6.1 China Mobile -- 6.2 AT& -- T. 6.3 SKT -- 6.4 Deutsche Telekom -- 6.5 5GPPP -- 7 Challenges and Issues -- 8 Future Research Direction -- 9 Summary -- References -- Green-Aware Mobile Edge Computing for IoT: Challenges, Solutions and Future Directions -- 1 Introduction -- 1.1 MEC Characteristics -- 1.2 Need for Sustainable IoT Application Management in MEC -- 2 Green-Aware Framework for MEC -- 3 Problem Modelling: Green-Aware Offloading -- 3.1 Task Model -- 3.2 Green Energy Provisioning Model -- 3.3 Local Processing Model -- 3.4 Edge Processing Model -- 3.5 Optimal Green-Aware Offloading -- 4 State-of-the-Art Offloading Approaches -- 4.1 GS-MEC -- 4.2 LSDQN -- 4.3 LETOC -- 4.4 GreenEdge -- 4.5 GOLL -- 4.6 SOMEC -- 4.7 Discussions of the Investigated Work -- 5 Future Research Directions -- 6 Summary and Conclusions -- References -- Part II Systems, Platforms and Services -- Prescriptive Maintenance Using Markov Decision Process and GPU-Accelerated Edge Computing -- 1 Introduction -- 2 Related Work -- 2.1 Predictive Maintenance -- 2.2 Prescriptive Maintenance -- 3 System Design and Modelling -- 3.1 POMDP Model -- 3.2 Model Estimation and Decision Algorithm -- 4 Performance Evaluations -- 5 Evaluation Results -- 5.1 Application Performance -- 5.2 System Performance -- 6 Conclusion -- References -- Software-Defined Multi-domain Tactical Networks: Foundations and Future Directions -- 1 Introduction -- 1.1 Research Questions and Challenges -- 2 System Model and Taxonomy -- 3 Multi-controller Management -- 3.1 Bootstrapping -- 3.2 Network Partitioning -- 3.3 Networked Operating System (NOS) -- 4 Middleware and Interoperability -- 4.1 Syntactic -- 4.1.1 Communication Protocols -- 4.1.2 Tunneling and Non-tunneling -- 4.2 Semantic -- 4.2.1 Protocol Translation -- 4.2.2 Protocol Oblivious Forwarding -- 4.2.3 Semantic Ontology -- 5 Network Component Management -- 5.1 Topology Awareness. 5.2 Adaptive Load and Path Management -- 5.3 Network Slicing -- 5.4 Service Function Chaining (SFC) -- 5.5 Unikernel Network Functions -- 6 Traffic Management -- 6.1 Service Level Agreement (SLA)-Aware Traffic Management -- 6.2 Intent-Based Traffic Management -- 6.3 Context-Aware Traffic Management -- 7 Policy Evaluation -- 7.1 Empirical -- 7.2 Emulation -- 7.3 Simulation -- 8 Gap Analysis and Future Directions -- 9 Summary -- References -- Mobility driven Cloud-Fog-Edge Framework for Location-Aware Services: A Comprehensive Review -- 1 Introduction -- 2 Motivations and Related Computing Paradigms -- 3 Taxonomy: Cloud-Fog-Edge System -- 3.1 Infrastructure Protocol -- 3.2 Connectivity -- 3.3 Security Issues -- 3.4 Resource Provisioning -- 4 Taxonomy: Mobility Management -- 5 Taxonomy: Location-Aware Services -- 6 Conclusions and Future Research Directions -- References -- Mobility-Based Resource Allocation and Provisioning in Fog and Edge Computing Paradigms: Review, Challenges, and FutureDirections -- 1 Introduction -- 2 Existing Mobile Based Resource Provisioning and Allocation Mechanisms in Edge -- 3 Existing Mobile Based Resource Provisioning and Allocation Mechanisms in Fog -- 4 Modelling Techniques to Support Mobility to Enhance the QoS of the Applications -- 5 Mathematical Models for Mobility Based Resource Allocation -- 6 Application Use Cases -- 6.1 Vehicular Networks -- 6.2 Smart Healthcare -- 6.3 Smart Grid -- 6.4 Others -- 7 Future Direction of Mobility-Based Resource Allocation and Provisioning in Fog and Edge related Computing Paradigms -- 7.1 Mobility-Based Resource Allocation and Provisioning -- 7.2 Security and Privacy -- 7.3 Power Utilization and Management -- 7.4 Fault Tolerance -- 7.5 Support For Application Placements Strategies -- 7.6 Support Interoperability -- 7.7 Unified and Dynamic Resource Management and Provisioning. 8 Conclusion -- References -- Cross Border Service Continuity with 5G Mobile Edge -- 1 Introduction -- 2 Background and Related Work -- 2.1 Cloud Computing -- 2.2 Edge Computing -- 2.3 Service Continuity -- 2.4 SC for MEC -- 2.5 Emerging 5G as an Enabling Technology -- 3 Security Management for SC -- 3.1 Underlying Technologies -- 3.1.1 Distributed Ledger Technologies -- 3.1.2 Blockchain -- 4 5G-CARMEN -- 4.1 Architecture -- 4.2 SC in 5G-CARMEN -- 4.3 5G-CARMEN Use Cases -- 4.3.1 Cooperative Maneuvering -- 4.3.2 Situation Awareness -- 4.3.3 Green Driving -- 4.3.4 Video Streaming -- 5 Video Streaming SC Use Case Deployment -- 5.1 Software Deployment -- 5.1.1 Omnet++ Software Architecture -- 5.1.2 NS3 Software Architecture -- 5.2 Security Mechanisms -- 5.3 Proposed Prediction Algorithm Methods for SC -- 5.4 Develop and Setup a Lab Environment -- 5.5 Assessment -- 5.5.1 Omnet++ Simulation Evaluation -- 5.5.2 NS3 Simulation Evaluation -- 5.5.3 Simulator Evaluation Overview -- 6 Future Research Directions -- 7 Conclusions -- References -- Security in Critical Communication for Mobile Edge Computing Based IoE Applications -- 1 Introduction -- 2 Applications and Security -- 3 Architecture for MEC -- 3.1 Network Model -- 4 Possible Attacks and Cryptographic Solution -- 5 Secure Communication Protocol -- 5.1 Architecture -- 5.2 Protocol in Details -- 6 Other Security Protocols: A Comparison -- 7 Issues and Challenges to Design Security Protocols -- 8 Conclusion and Future Direction -- References -- Blockchain for Mobile Edge Computing: Consensus Mechanisms and Scalability -- 1 Introduction -- 1.1 MEC and Network Slicing -- 1.2 Integration of Blockchain and MEC -- 1.3 Related Works -- 1.4 Chapter Structure -- 2 Blockchain Technology: An Evolving Paradigm -- 2.1 Proof of Work -- 2.2 Proof of Useful Work -- 2.3 Proof of Stake. 2.4 Practical Byzantine Fault Tolerance. |
| Record Nr. | UNINA-9910510579503321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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