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Biblioteca

MARC Lista (tabellare)
Explainable edge AI, a futuristic computing perspective / / edited by Aboul Ella Hassanien [and three others]
Explainable edge AI, a futuristic computing perspective / / edited by Aboul Ella Hassanien [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (187 pages)
Disciplina 006.3
Collana Studies in Computational Intelligence
Soggetto topico Artificial intelligence
Edge computing
ISBN 3-031-18292-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910629287903321
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Federated learning over wireless edge networks / / Wei Yang Bryan Lim, [and four others]
Federated learning over wireless edge networks / / Wei Yang Bryan Lim, [and four others]
Autore Lim Wei Yang Bryan
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (175 pages)
Disciplina 929.374
Collana Wireless Networks
Soggetto topico Edge computing
ISBN 3-031-07838-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- List of Figures -- List of Tables -- 1 Federated Learning at Mobile Edge Networks: A Tutorial -- 1.1 Introduction -- 1.2 Background and Fundamentals of Federated Learning -- 1.2.1 Federated Learning -- 1.2.2 Statistical Challenges of FL -- 1.2.3 FL Protocols and Frameworks -- 1.2.4 Unique Characteristics and Issues of FL -- 1.3 Communication Cost -- 1.3.1 Edge and End Computation -- 1.3.2 Model Compression -- 1.3.3 Importance-Based Updating -- 1.4 Resource Allocation -- 1.4.1 Worker Selection -- 1.4.2 Joint Radio and Computation Resource Management -- 1.4.3 Adaptive Aggregation -- 1.4.4 Incentive Mechanism -- 1.5 Privacy and Security Issues -- 1.5.1 Privacy Issues -- 1.5.1.1 Information Exploiting Attacks in Machine Learning: A Brief Overview -- 1.5.1.2 Differential Privacy-Based Protection Solutions for FL Workers -- 1.5.1.3 Collaborative Training Solutions -- 1.5.1.4 Encryption-Based Solutions -- 1.5.2 Security Issues -- 1.5.2.1 Data Poisoning Attacks -- 1.5.2.2 Model Poisoning Attacks -- 1.5.2.3 Free-Riding Attacks -- 1.6 Applications of Federated Learning for Mobile Edge Computing -- 1.6.1 Cyberattack Detection -- 1.6.2 Edge Caching and Computation Offloading -- 1.6.3 Base Station Association -- 1.6.4 Vehicular Networks -- 1.7 Conclusion and Chapter Discussion -- 2 Multi-dimensional Contract Matching Design for Federated Learning in UAV Networks -- 2.1 Introduction -- 2.2 System Model and Problem Formulation -- 2.2.1 UAV Sensing Model -- 2.2.2 UAV Computation Model -- 2.2.3 UAV Transmission Model -- 2.2.4 UAV and Model Owner Utility Modeling -- 2.3 Multi-dimensional Contract Design -- 2.3.1 Contract Condition Analysis -- 2.3.2 Conversion into a Single-Dimensional Contract -- 2.3.3 Conditions for Contract Feasibility -- 2.3.4 Contract Optimality -- 2.4 UAV-Subregion Assignment -- 2.4.1 Matching Rules.
2.4.2 Matching Implementation and Algorithm -- 2.5 Performance Evaluation -- 2.5.1 Contract Optimality -- 2.5.2 UAV-Subregion Preference Analysis -- 2.5.3 Matching-Based UAV-Subregion Assignment -- 2.6 Conclusion and Chapter Discussion -- 3 Joint Auction-Coalition Formation Framework for UAV-Assisted Communication-Efficient Federated Learning -- 3.1 Introduction -- 3.2 System Model -- 3.2.1 Worker Selection -- 3.2.2 UAV Energy Model -- 3.2.2.1 Flying Energy -- 3.2.2.2 Computational Energy -- 3.2.2.3 Communication Energy -- 3.2.2.4 Hovering Energy -- 3.2.2.5 Circuit Energy -- 3.3 Coalitions of UAVs -- 3.3.1 Coalition Game Formulation -- 3.3.2 Coalition Formation Algorithm -- 3.4 Auction Design -- 3.4.1 Buyers' Bids -- 3.4.2 Sellers' Problem -- 3.4.3 Analysis of the Auction -- 3.4.4 Complexity of the Joint Auction-Coalition Algorithm -- 3.5 Simulation Results and Analysis -- 3.5.1 Communication Efficiency in FL Network -- 3.5.2 Preference of Cells of Workers -- 3.5.3 Profit-Maximizing Behavior of UAVs -- 3.5.4 Allocation of UAVs to Cells of Workers -- 3.5.5 Comparison with Existing Schemes -- 3.6 Conclusion and Chapter Discussion -- 4 Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- 4.1 Introduction -- 4.2 System Model and Problem Formulation -- 4.2.1 System Model -- 4.2.2 Lower-Level Evolutionary Game -- 4.2.3 Upper-Level Deep Learning Based Auction -- 4.3 Lower-Level Evolutionary Game -- 4.3.1 Evolutionary Game Formulation -- 4.3.2 Worker Utility and Replicator Dynamics -- 4.3.3 Existence, Uniqueness, and Stability of the Evolutionary Equilibrium -- 4.4 Deep Learning Based Auction for Valuation of Cluster Head -- 4.4.1 Auction Formulation -- 4.4.2 Deep Learning Based Auction for Valuation of Cluster Heads -- 4.4.3 Monotone Transform Functions -- 4.4.4 Allocation Rule -- 4.4.5 Conditional Payment Rule.
4.4.6 Neural Network Training -- 4.5 Performance Evaluation -- 4.5.1 Lower-Level Evolutionary Game -- 4.5.1.1 Stability and Uniqueness of the Evolutionary Equilibrium -- 4.5.1.2 Evolutionary Equilibrium Under Varying Parameters and Conditions -- 4.5.2 Upper-Level Deep Learning Based Auction -- 4.5.2.1 Evaluation of the Deep Learning Based Auction -- 4.6 Conclusion and Chapter Discussion -- 5 Conclusion and Future Works -- References -- Index.
Record Nr. UNISA-996490358403316
Lim Wei Yang Bryan  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Federated learning over wireless edge networks / / Wei Yang Bryan Lim, [and four others]
Federated learning over wireless edge networks / / Wei Yang Bryan Lim, [and four others]
Autore Lim Wei Yang Bryan
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (175 pages)
Disciplina 929.374
Collana Wireless Networks
Soggetto topico Edge computing
ISBN 3-031-07838-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- List of Figures -- List of Tables -- 1 Federated Learning at Mobile Edge Networks: A Tutorial -- 1.1 Introduction -- 1.2 Background and Fundamentals of Federated Learning -- 1.2.1 Federated Learning -- 1.2.2 Statistical Challenges of FL -- 1.2.3 FL Protocols and Frameworks -- 1.2.4 Unique Characteristics and Issues of FL -- 1.3 Communication Cost -- 1.3.1 Edge and End Computation -- 1.3.2 Model Compression -- 1.3.3 Importance-Based Updating -- 1.4 Resource Allocation -- 1.4.1 Worker Selection -- 1.4.2 Joint Radio and Computation Resource Management -- 1.4.3 Adaptive Aggregation -- 1.4.4 Incentive Mechanism -- 1.5 Privacy and Security Issues -- 1.5.1 Privacy Issues -- 1.5.1.1 Information Exploiting Attacks in Machine Learning: A Brief Overview -- 1.5.1.2 Differential Privacy-Based Protection Solutions for FL Workers -- 1.5.1.3 Collaborative Training Solutions -- 1.5.1.4 Encryption-Based Solutions -- 1.5.2 Security Issues -- 1.5.2.1 Data Poisoning Attacks -- 1.5.2.2 Model Poisoning Attacks -- 1.5.2.3 Free-Riding Attacks -- 1.6 Applications of Federated Learning for Mobile Edge Computing -- 1.6.1 Cyberattack Detection -- 1.6.2 Edge Caching and Computation Offloading -- 1.6.3 Base Station Association -- 1.6.4 Vehicular Networks -- 1.7 Conclusion and Chapter Discussion -- 2 Multi-dimensional Contract Matching Design for Federated Learning in UAV Networks -- 2.1 Introduction -- 2.2 System Model and Problem Formulation -- 2.2.1 UAV Sensing Model -- 2.2.2 UAV Computation Model -- 2.2.3 UAV Transmission Model -- 2.2.4 UAV and Model Owner Utility Modeling -- 2.3 Multi-dimensional Contract Design -- 2.3.1 Contract Condition Analysis -- 2.3.2 Conversion into a Single-Dimensional Contract -- 2.3.3 Conditions for Contract Feasibility -- 2.3.4 Contract Optimality -- 2.4 UAV-Subregion Assignment -- 2.4.1 Matching Rules.
2.4.2 Matching Implementation and Algorithm -- 2.5 Performance Evaluation -- 2.5.1 Contract Optimality -- 2.5.2 UAV-Subregion Preference Analysis -- 2.5.3 Matching-Based UAV-Subregion Assignment -- 2.6 Conclusion and Chapter Discussion -- 3 Joint Auction-Coalition Formation Framework for UAV-Assisted Communication-Efficient Federated Learning -- 3.1 Introduction -- 3.2 System Model -- 3.2.1 Worker Selection -- 3.2.2 UAV Energy Model -- 3.2.2.1 Flying Energy -- 3.2.2.2 Computational Energy -- 3.2.2.3 Communication Energy -- 3.2.2.4 Hovering Energy -- 3.2.2.5 Circuit Energy -- 3.3 Coalitions of UAVs -- 3.3.1 Coalition Game Formulation -- 3.3.2 Coalition Formation Algorithm -- 3.4 Auction Design -- 3.4.1 Buyers' Bids -- 3.4.2 Sellers' Problem -- 3.4.3 Analysis of the Auction -- 3.4.4 Complexity of the Joint Auction-Coalition Algorithm -- 3.5 Simulation Results and Analysis -- 3.5.1 Communication Efficiency in FL Network -- 3.5.2 Preference of Cells of Workers -- 3.5.3 Profit-Maximizing Behavior of UAVs -- 3.5.4 Allocation of UAVs to Cells of Workers -- 3.5.5 Comparison with Existing Schemes -- 3.6 Conclusion and Chapter Discussion -- 4 Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- 4.1 Introduction -- 4.2 System Model and Problem Formulation -- 4.2.1 System Model -- 4.2.2 Lower-Level Evolutionary Game -- 4.2.3 Upper-Level Deep Learning Based Auction -- 4.3 Lower-Level Evolutionary Game -- 4.3.1 Evolutionary Game Formulation -- 4.3.2 Worker Utility and Replicator Dynamics -- 4.3.3 Existence, Uniqueness, and Stability of the Evolutionary Equilibrium -- 4.4 Deep Learning Based Auction for Valuation of Cluster Head -- 4.4.1 Auction Formulation -- 4.4.2 Deep Learning Based Auction for Valuation of Cluster Heads -- 4.4.3 Monotone Transform Functions -- 4.4.4 Allocation Rule -- 4.4.5 Conditional Payment Rule.
4.4.6 Neural Network Training -- 4.5 Performance Evaluation -- 4.5.1 Lower-Level Evolutionary Game -- 4.5.1.1 Stability and Uniqueness of the Evolutionary Equilibrium -- 4.5.1.2 Evolutionary Equilibrium Under Varying Parameters and Conditions -- 4.5.2 Upper-Level Deep Learning Based Auction -- 4.5.2.1 Evaluation of the Deep Learning Based Auction -- 4.6 Conclusion and Chapter Discussion -- 5 Conclusion and Future Works -- References -- Index.
Record Nr. UNINA-9910616395503321
Lim Wei Yang Bryan  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
FMEC : 2018 Second International Conference on Fog and Mobile Edge Computing : 23-26 April, 2018, Barcelona, Spain / / Institute of Electrical and Electronics Engineers
FMEC : 2018 Second International Conference on Fog and Mobile Edge Computing : 23-26 April, 2018, Barcelona, Spain / / Institute of Electrical and Electronics Engineers
Pubbl/distr/stampa Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018
Descrizione fisica 1 online resource (33 pages)
Disciplina 004.165
Soggetto topico Mobile computing
Cloud computing
Edge computing
ISBN 1-5386-5896-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996279586703316
Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
FMEC : 2018 Second International Conference on Fog and Mobile Edge Computing : 23-26 April, 2018, Barcelona, Spain / / Institute of Electrical and Electronics Engineers
FMEC : 2018 Second International Conference on Fog and Mobile Edge Computing : 23-26 April, 2018, Barcelona, Spain / / Institute of Electrical and Electronics Engineers
Pubbl/distr/stampa Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018
Descrizione fisica 1 online resource (33 pages)
Disciplina 004.165
Soggetto topico Mobile computing
Cloud computing
Edge computing
ISBN 1-5386-5896-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910280908803321
Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
FRAME '22 : proceedings of the 2nd Workshop on Flexible Resource and Application Management on the Edge : July 1, 2022, Minneapolis, MN, USA / / general chair, Luca Ferrucci
FRAME '22 : proceedings of the 2nd Workshop on Flexible Resource and Application Management on the Edge : July 1, 2022, Minneapolis, MN, USA / / general chair, Luca Ferrucci
Pubbl/distr/stampa New York : , : Association for Computing Machinery, , 2022
Descrizione fisica 1 online resource (50 pages) : illustrations
Disciplina 004.36
Collana ACM Conferences
Soggetto topico Client/server computing
Cloud computing
Computer science
Edge computing
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910580193703321
New York : , : Association for Computing Machinery, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
IEEE Std 1934-2018 : IEEE Standard for Adoption of OpenFog Reference Architecture for Fog Computing / / Institute of Electrical and Electronics Engineers
IEEE Std 1934-2018 : IEEE Standard for Adoption of OpenFog Reference Architecture for Fog Computing / / Institute of Electrical and Electronics Engineers
Pubbl/distr/stampa New York : , : IEEE, , 2018
Descrizione fisica 1 online resource (176 pages)
Disciplina 005.758
Soggetto topico Edge computing
ISBN 1-5044-5017-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti IEEE Std 1934-2018
Record Nr. UNINA-9910282232003321
New York : , : IEEE, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
IEEE Std 1934-2018 : IEEE Standard for Adoption of OpenFog Reference Architecture for Fog Computing / / Institute of Electrical and Electronics Engineers
IEEE Std 1934-2018 : IEEE Standard for Adoption of OpenFog Reference Architecture for Fog Computing / / Institute of Electrical and Electronics Engineers
Pubbl/distr/stampa New York : , : IEEE, , 2018
Descrizione fisica 1 online resource (176 pages)
Disciplina 005.758
Soggetto topico Edge computing
ISBN 1-5044-5017-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti IEEE Std 1934-2018
Record Nr. UNISA-996279938403316
New York : , : IEEE, , 2018
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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]
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