10830nam 2200517 450 991051057950332120220818082255.03-030-69893-9(MiAaPQ)EBC6810931(Au-PeEL)EBL6810931(CKB)19919643300041(OCoLC)1287131117(PPN)259389005(EXLCZ)991991964330004120220818d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMobile edge computing /Anwesha Mukherjee [and three others], editorsCham, Switzerland :Springer,[2021]©20211 online resource (598 pages)Print version: Mukherjee, Anwesha Mobile Edge Computing Cham : Springer International Publishing AG,c2022 9783030698928 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&amp -- 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.Edge computingMobile communication systemsArtificial intelligenceEdge computing.Mobile communication systems.Artificial intelligence.005.758Mukherjee AnweshaMiAaPQMiAaPQMiAaPQBOOK9910510579503321Mobile edge computing2906200UNINA