Tabla de Contenidos: “…-- The crisis affected both supply and demand and triggered reallocation -- Productivity growth surged in 2020 in most OECD countries, reflecting a fall in hours worked -- The aggregate figure masks heterogeneous productivity performance -- Services sectors were the hardest hit -- Small and informal firms were affected more severely -- Stabilisation policies have preserved employment and ease access to finance in the short term -- Adoption of digital technology and teleworking have cushioned the downturn -- The pace of digitalisation has accelerated -- The prevalence of teleworking has risen -- What long-term impact can be expected? …”

Tabla de Contenidos: “…. -- Chapter 4: Classifying Texts -- Technical requirements -- Getting the dataset and evaluation ready -- Getting ready -- How to do it... -- Performing rule-based text classification using keywords -- Getting ready -- How to do it... -- Clustering sentences using K-Means - unsupervised text classification -- Getting ready -- How to do it... -- Using SVMs for supervised text classification -- Getting ready -- How to do it... -- There's more... -- Training a spaCy model for supervised text classification -- Getting ready -- How to do it... -- Classifying texts using OpenAI models -- Getting ready -- How to do it... -- Chapter 5: Getting Started with Information Extraction -- Technical requirements -- Using regular expressions -- Getting ready -- How to do it... -- There's more... -- Finding similar strings - Levenshtein distance -- Getting ready -- How to do it... -- There's more... -- Extracting keywords -- Getting ready -- How to do it... -- There's more... -- Performing named entity recognition using spaCy -- Getting ready -- How to do it... -- There's more... -- Training your own NER model with spaCy -- Getting ready -- How to do it... -- See also -- Fine-tuning BERT for NER -- Getting ready -- How to do it... -- Chapter 6: Topic Modeling -- Technical requirements -- LDA topic modeling with gensim -- Getting ready -- How to do it... -- There's more... -- Community detection clustering with SBERT -- Getting ready -- How to do it... -- K-Means topic modeling with BERT -- Getting ready -- How to do it... -- Topic modeling using BERTopic -- Getting ready -- How to do it... -- There's more... -- Using contextualized topic models -- Getting ready -- How to do it... -- See also -- Chapter 7: Visualizing Text Data -- Technical requirements…”

Tabla de Contenidos: “…. -- There's more... -- Performing platform-specific or group-specific operations -- Getting ready -- How to do it... -- How it works... -- Using variables and vaults -- Getting ready -- How to do it... -- How it works... -- Index…”

Tabla de Contenidos: “…. -- Writing a streaming program using Kafka Streams -- Getting ready -- How to do it... -- Improving the performance of the Kafka Streams program -- Getting ready -- How to do it... -- Writing a streaming program using Apache Spark -- Getting ready -- How to do it... -- Improving the performance of the Spark job -- How to do it... -- Aggregating logs into Kafka using Log4J -- Getting ready -- How to do it...…”

Tabla de Contenidos: “…Cover -- Table of Contents -- Getting Started -- Lesson 1 Sound Editing Basics -- Understanding the Sound Editing Process -- Cleaning Dialogue -- Normalizing Audio -- Normalizing a Group of Clips -- Controlling Dynamic Range -- Separating Signal from Noise -- Controlling Audio Frequencies -- Correcting Highs and Lows -- Removing Hums -- Integrating Sound Effects -- Constructing Ambiences -- Editing Subframe Audio -- Enhancing the Scene -- Adding Music -- Lesson Review -- Lesson 2 Sound Mixing Basics -- Mixing in Final Cut Pro -- Fading In and Out -- Using Audio Keyframes to Fade -- Solo and Mute Controls -- Engaging the Button Bar -- Setting Audio Levels -- The Audio Mixer Window -- Setting Up Views in the Audio Mixer -- Mixing On the Fly -- Controlling Keyframe Frequency -- Resetting All Keyframes -- Creating Perspective -- Finishing Your Mix -- Mixing in Soundtrack Pro -- Sending to a Soundtrack Pro Multitrack Project -- Performing Basic Mixing Tasks -- Muting and Soloing -- Adding Fades -- Adjusting Levels -- Automating Level Changes -- Returning to Final Cut Pro -- Lesson Review -- Lesson 3 Getting to Know Soundtrack Pro -- Opening Sequences from Final Cut Pro -- Sending Sequences to Soundtrack Pro -- Exploring the Soundtrack Pro Editing Workspace -- Working with Panes -- Working with Tabs -- Using Window Layouts -- Setting Project Properties -- Setting the Sample Rate -- Working with Timecode -- Controlling Playback -- J-K-L Playback -- Moving Around the Timeline -- Positioning the Playhead -- Using the Scrub Tool -- Exploring the Track Header -- Volume and Pan -- Mute and Solo -- Returning to Final Cut Pro -- Lesson Review -- Lesson 4 Fixing Audio Files -- Getting Ready to Perform Audio Restoration -- Sending Audio Files to Soundtrack Pro -- Exploring the File Editor Project View -- Using the Global Waveform View -- Redrawing Waveforms…”

Tabla de Contenidos: “…Maintaining and tuning the GPFS environment -- 5.1 Performance tuning cycle -- 5.2 Tuning the GPFS environment -- 5.2.1 Tuning storage -- 5.2.2 Tuning the SAN hardware -- 5.2.3 Tuning the operating system -- 5.2.4 Tuning GPFS -- 5.2.5 Tuning the clients -- 5.3 Software tools and utilities -- 5.3.1 System administration tools -- 5.3.2 System resource monitoring tools (Linux) -- 5.3.3 Load generating tools -- 5.4 Client application considerations -- 5.4.1 Client side buffering and failover mechanism -- 5.4.2 Client side load balancing -- 5.4.3 Block size considerations -- 5.4.4 Mixed-media processing and file access patterns -- 5.5 Performance tuning considerations -- 5.6 List of GPFS related files -- Appendix A. …”

Tabla de Contenidos: “…Run SQL Script Center -- 7.1 Run SQL Scripts -- 7.1.1 ODBC and JDBC connection -- 7.1.2 Running a CL command under SQL script -- 7.1.3 Run SQL Scripts Run options -- 7.1.4 SQL Assist -- 7.2 Change Query Attributes -- 7.3 Current SQL for a job -- 7.4 SQL Performance Monitors -- 7.5 System Debugger -- Chapter 8. …”

Tabla de Contenidos: “…ICSF support for CPACF, PCIXCC, and PCICA -- 5.1 CP Assist for Cryptographic Functions (CPACF) feature -- 5.2 LPAR setup -- 5.2.1 Planning considerations -- 5.2.2 The image profile processor page -- 5.2.3 The PCI Crypto page -- 5.2.4 Viewing LPAR Cryptographic Controls -- 5.3 PCIXCC and PCICA feature installation -- 5.3.1 PCIXCC and PCICA enablement -- 5.3.2 Configuring and monitoring the status of PCIXCC and PCICA -- 5.3.3 Security issues with the PCI Cryptographic cards -- 5.4 Integrated Cryptographic Services Facility (ICSF) setup -- 5.4.1 Changes from previous release -- 5.4.2 Started task and the first time start -- 5.4.3 Master Keys -- 5.4.4 Initial Master Key entry with the pass phrase initialization utility -- 5.4.5 Installation of a new PCIXCC or PCICA card -- 5.4.6 PKDS initialization -- Chapter 6. Performance and monitoring -- 6.1 z990 Crypto hardware performance considerations -- 6.2 Monitoring and reporting -- 6.2.1 RMF reporting -- 6.2.2 ICSF SMF records -- 6.2.3 Example using RMF and SMF data -- Appendix A. …”

Tabla de Contenidos: “…-- How Will I Assess Student Performance in This Course? -- How Will I Address Attendance Requirements? …”

Tabla de Contenidos: “…6.2.2 Indirect particle heating receivers -- 6.2.2.1 Gravity-driven particle flow-through enclosures -- 6.2.2.2 Fluidized particle flow-through tubes -- 6.2.3 Summary of particle receiver technologies -- 6.3 Other high-performance receiver designs -- 6.3.1 Light-trapping receiver designs -- 6.3.1.1 Surface features -- 6.3.1.2 Spiky receiver -- 6.3.1.3 Bladed geometries -- 6.3.1.4 Fractal-like geometries -- 6.3.2 Air curtains -- 6.4 Summary and conclusions -- Acknowledgments -- References -- 7 - Next generation of liquid metal and other high-performance receiver designs for concentrating solar thermal (CST) central tower -- 7.1 Introduction -- 7.2 Thermophysical properties of liquid metals -- 7.3 Liquid metals in central receiver systems -- 7.3.1 Experience in central receiver systems -- 7.3.2 The CRS-SSPS project of the International Energy Agency -- 7.3.3 Other projects with liquid metals in solar receivers -- 7.4 Innovative power conversion cycles with liquid metals as heat transfer fluid -- 7.5 Conclusions and outlook -- References -- 4 - Advances in the power block and thermal storage systems -- 8 - Supercritical CO2 and other advanced power cycles for concentrating solar thermal (CST) systems -- 8.1 Introduction -- 8.2 Stand-alone cycles -- 8.2.1 Steam Rankine cycles -- 8.2.2 Gas Brayton cycles -- 8.2.2.1 Air Brayton cycle -- 8.2.2.2 Helium Brayton cycle -- 8.2.2.3 Supercritical carbon dioxide Brayton cycles -- Simple cycle -- Recompression cycle -- Partial cooling cycle -- 8.2.3 Comparison of the presented cycles -- 8.3 Combined cycles -- 8.3.1 Organic Rankine cycle -- 8.3.2 Supercritical organic Rankine cycle -- 8.3.3 Absorption power cycles -- 8.3.3.1 Kalina cycle -- 8.3.3.2 Goswami cycle -- 8.4 Summary and conclusions -- References -- 9 - Advances in dry cooling for concentrating solar thermal (CST) power plants -- 9.1 Introduction…”

Tabla de Contenidos: “…Long-term solvency measures -- Earnings and cash flow coverage ratios -- Interest coverage ratio -- Cash coverage ratio -- Operational efficiency or asset utilization ratios -- Inventory turnover ratio -- Receivables turnover ratio -- Market value measures -- Du Pont system -- Business risk -- Analysis of growth potential -- Analysis of bank performance -- Profitability measures -- Efficiency measures -- Expense measures -- Leverage ratios -- Asset quality -- Management quality -- Limitations of ratio analysis -- Links for websites for financial analysis -- Fundamentals of valuation -- Time value of money -- Future value and compounding -- Present value and discounting -- Discounted cash flow valuation -- Annuities -- Perpetuities -- Growing annuity -- Continuous compounding -- Different types of interest rates -- Nominal or stated interest rate -- Annual percentage rate -- Periodic rate -- Effective annual rate -- Different types of loans -- Pure discount loans -- Interest only loans -- Amortized loans -- Bond valuation and interest rates -- Basics of bonds -- Features of bond -- Indenture -- Bond terminology -- Par value -- Coupon and coupon rate -- Maturity date -- Yield to maturity (YTM) -- Current yield -- Yield to call -- Premium and discount bond -- Value of bond -- Interest rates -- Term structure of interest rates -- Bond ratings -- Bond pricing theorems -- Duration theorems -- Basics of stock valuation -- Summary highlights of stock valuation -- General method -- Constant growth method -- Nonconstant growth -- Two stage growth -- References -- 2 Risk and return -- 2.1 Introduction -- 2.2 Accounting and risk measures -- 2.3 Measures of returns -- 2.3.1 Total return -- 2.3.2 Historical rates of return -- 2.3.3 Average returns -- 2.3.4 Expected return -- 2.3.5 Portfolio returns -- 2.3.6 Determinants of rate of return -- 2.4 Risk premium…”

Tabla de Contenidos: “…3.5.2 Addiction challenge -- 3.5.3 Privacy and security challenge -- 3.5.4 Ethics and morality challenge -- 3.6 Conclusion -- References -- 4 Web-based brain tumor classification app using convolutional neural network -- 4.1 Introduction -- 4.2 Methodology -- 4.2.1 Data collection -- 4.2.2 Data augmentation -- 4.2.3 Splitting -- 4.2.4 Training settings -- 4.2.4.1 CNN network architecture -- 4.2.5 Performance -- 4.2.6 Hardware and software requirements -- 4.3 Results -- 4.3.1 Confusion matrix -- 4.3.2 Performance metric of model -- 4.4 Conclusion -- Acknowledgments -- References -- 5 Traffic management system using different Internet of Things devices: literature review -- 5.1 Introduction -- 5.1.1 Selecting a template -- 5.1.2 Types of implementation methods -- 5.1.2.1 Radio frequency identification -- 5.1.2.1.1 RFID controller -- 5.1.2.1.2 RFID tag -- 5.1.2.1.3 Applications for RFID sensor -- 5.1.2.2 Analysis of video data -- 5.1.2.2.1 Problems with video analysis include -- 5.1.2.3 Wireless sensor network -- 5.1.2.4 The IR sensor -- 5.1.2.4.1 Sensor types -- 5.2 Method of evaluation -- 5.2.1 IoT device communication and addressing -- 5.2.2 IoT device security (physical and virtual) -- 5.3 Results and discussion -- 5.4 Conclusion -- References -- 6 Wireless sensor networks DV-Hop positioning based on artificial intelligence in the IoT era -- 6.1 Introduction -- 6.2 Related work -- 6.3 Methodology -- 6.4 Results -- 6.5 Conclusion -- References -- 7 Cassava leave disease image classification based on deep convolutional neural network -- 7.1 Introduction -- 7.1.1 Objective of the research -- 7.1.2 Problem of the research -- 7.2 Work related -- 7.3 Methodology -- 7.3.1 Collection of data -- 7.3.2 Preprocessing and cleaning -- 7.3.2.1Data split ratio and augmentation -- 7.4 Results -- 7.4.1 Matrix of confusion and comparative analysis -- 7.5 Conclusion…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Factories of the Future -- 1.0 Introduction -- 1.1 Factory of the Future -- 1.1.1 Plant Structure -- 1.1.2 Plant Digitization -- 1.1.3 Plant Processes -- 1.1.4 Industry of the Future: A Fully Integrated Industry -- 1.2 Current Manufacturing Environment -- 1.3 Driving Technologies and Market Readiness -- 1.4 Connected Factory, Smart Factory, and Smart Manufacturing -- 1.4.1 Potential Benefits of a Connected Factory -- 1.5 Digital and Virtual Factory -- 1.5.1 Digital Factory -- 1.5.2 Virtual Factory -- 1.6 Advanced Manufacturing Technologies -- 1.6.1 Advantages of Advanced Manufacturing Technologies -- 1.7 Role of Factories of the Future (FoF) in Manufacturing Performance -- 1.8 Socio-Econo-Techno Justification of Factories of the Future -- References -- Chapter 2 Industry 5.0 -- 2.1 Introduction -- 2.1.1 Industry 5.0 for Manufacturing -- 2.1.1.1 Industrial Revolutions -- 2.1.2 Real Personalization in Industry 5.0 -- 2.1.3 Industry 5.0 for Human Workers -- 2.2 Individualized Human-Machine-Interaction -- 2.3 Industry 5.0 is Designed to Empower Humans, Not to Replace Them -- 2.4 Concerns in Industry 5.0 -- 2.5 Humans Closer to the Design Process of Manufacturing -- 2.5.1 Enablers of Industry 5.0 -- 2.6 Challenges and Enablers (Socio-Econo-Techno Justification) -- 2.6.1 Social Dimension -- 2.6.2 Governmental and Political Dimension -- 2.6.3 Interdisciplinarity -- 2.6.4 Economic Dimension -- 2.6.5 Scalability -- 2.7 Concluding Remarks -- References -- Chapter 3 Machine Learning - A Survey -- 3.1 Introduction -- 3.2 Machine Learning -- 3.2.1 Unsupervised Machine Learning -- 3.2.2 Variety of Unsupervised Learning -- 3.2.3 Supervised Machine Learning -- 3.2.4 Categories of Supervised Learning -- 3.3 Reinforcement Machine Learning -- 3.3.1 Applications of Reinforcement Learning…”

Tabla de Contenidos: “…References -- Chapter 5. Performance Measures in Edge Computing Using the Queuing Model -- 5.1. …”

Tabla de Contenidos: “…Building an implementation plan -- 2.1 WebSphere Business Integration Server overview -- 2.1.1 WebSphere MQ Workflow: long-running processes -- 2.1.2 WebSphere Interchange Server: objects and their interactions -- 2.1.3 WebSphere BI Message Broker: routing and reformatting -- 2.1.4 WebSphere Business Integration Adapters: connectivity -- 2.1.5 Base components -- 2.1.6 Bringing it all together -- 2.2 Business requirements -- 2.2.1 Implementation of use cases as required -- 2.2.2 Agility -- 2.2.3 Ability to integrate existing services -- 2.2.4 Business monitoring -- 2.3 Quality of service requirements -- 2.3.1 Performance -- 2.3.2 Availability -- 2.4 System design for redbook scenario -- 2.5 Planning considerations -- 2.5.1 WebSphere MQ Workflow -- 2.5.2 InterChange Server -- 2.5.3 WebSphere BI Message Broker -- 2.6 Planning documents -- Chapter 3. …”

Tabla de Contenidos: “…Common replacement considerations -- 7.1 How to read the example scenarios -- 7.2 Common assumptions in the sample scenarios -- 7.3 Simplifications in the sample scenarios -- 7.4 Security considerations -- 7.5 Performance considerations -- 7.6 Using an LDAP directory -- 7.6.1 LDAP security considerations -- 7.6.2 Availability and performance considerations -- 7.7 SSL implementation hints -- 7.7.1 SSL and TLS overview -- 7.7.2 Uses of SSL -- 7.7.3 Using SSL in the replacement scenarios -- 7.7.4 IBM GSKit -- 7.7.5 Authentication with certificates -- 7.7.6 Using self-signed certificates -- 7.7.7 Using certificates from a Certificate Authority (CA) -- 7.7.8 Additional hints and considerations -- Chapter 8. …”

Tabla de Contenidos: “…Intervention, Monitoring, and Model Performance -- 4.3.8.1. Intervention -- 4.3.8.2. Model monitoring and performance -- 4.4. …”

Tabla de Contenidos: “…9.2 Related Work -- 9.3 K-Means Algorithm -- 9.4 Data Partitioning -- 9.5 Experimental Results -- 9.5.1 Datasets -- 9.5.2 Performance Analysis -- 9.5.3 Approximation on Real-World Datasets -- 9.6 Conclusion -- Acknowledgments -- References -- Chapter 10 An Analysis on Detection and Visualization of Code Smells -- 10.1 Introduction -- 10.2 Literature Survey -- 10.2.1 Machine Learning-Based Techniques -- 10.2.2 Code Smell Characteristics in Different Computer Languages -- 10.3 Code Smells -- 10.4 Comparative Analysis -- 10.5 Conclusion -- References -- Chapter 11 Leveraging Classification Through AutoML and Microservices -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Observations -- 11.4 Conceptual Architecture -- 11.5 Analysis of Results -- 11.6 Results and Discussion -- References -- Part III: E-Learning Applications -- Chapter 12 Virtual Teaching Activity Monitor -- 12.1 Introduction -- 12.2 Related Works -- 12.3 Methodology -- 12.3.1 Head Movement -- 12.3.2 Drowsiness and Yawn Detection -- 12.3.3 Attendance System -- 12.3.4 Network Speed -- 12.3.5 Text Classification -- 12.4 Results and Discussion -- 12.5 Conclusions -- References -- Chapter 13 AI-Based Development of Student E-Learning Framework -- 13.1 Introduction -- 13.2 Objective -- 13.3 Literature Survey -- 13.4 Proposed Student E-Learning Framework -- 13.5 System Architecture -- 13.6 Working Module Description -- 13.6.1 Data Preprocessing -- 13.6.2 Driving Test Cases -- 13.6.3 System Analysis -- 13.7 Conclusion -- 13.8 Future Enhancements -- References -- Part IV: Networks Application -- Chapter 14 A Comparison of Selective Machine Learning Algorithms for Anomaly Detection in Wireless Sensor Networks -- 14.1 Introduction -- 14.1.1 Data Aggregation in WSNs -- 14.1.2 Anomalies -- 14.2 Anomaly Detection in WSN -- 14.2.1 Need for Anomaly Detection in WSNs…”

Tabla de Contenidos: “…3.2 Deep learning approaches for digital signal processing -- 3.3 Optical IM/DD systems based on deep learning -- 3.3.1 ANN receiver -- 3.3.1.1 PAM transmission -- 3.3.1.2 Sliding window FFNN processing -- 3.3.2 Auto-encoders -- 3.3.2.1 Auto-encoder design based on a feed-forward neural network -- 3.3.2.2 Auto-encoder design based on a recurrent neural network -- 3.3.3 Performance -- 3.3.4 Distance-agnostic transceiver -- 3.4 Implementation on a transmission link -- 3.4.1 Conventional PAM transmission with ANN-based receiver -- 3.4.2 Auto-encoder implementation -- 3.5 Outlook -- References -- 4 Machine learning techniques for passive optical networks -- 4.1 Background -- 4.2 The validation of NN effectiveness -- 4.3 NN for nonlinear equalization -- 4.4 End to end deep learning for optimal equalization -- 4.5 FPGA implementation of NN equalizer -- 4.6 Conclusions and perspectives -- References -- 5 End-to-end learning for fiber-optic communication systems -- 5.1 Introduction -- 5.2 End-to-end learning -- 5.3 End-to-end learning for fiber-optic communication systems -- 5.3.1 Direct detection -- 5.3.2 Coherent systems -- 5.3.2.1 Nonlinear phase noise channel -- 5.3.2.2 Perturbation models (NLIN and GN) -- 5.3.2.3 Split-step Fourier method (SSFM) -- 5.4 Gradient-free end-to-end learning -- 5.5 Conclusion -- Acknowledgments -- References -- 6 Deep learning techniques for optical monitoring -- 6.1 Introduction -- 6.2 Building blocks of deep learning-based optical monitors -- 6.2.1 Digital coherent reception as a data-acquisition method -- 6.2.2 Deep learning and representation learning -- 6.2.3 Combination of digital coherent reception and deep learning -- 6.3 Deep learning-based optical monitors -- 6.3.1 Training mode of DL-based optical monitors -- 6.3.2 Advanced topics for the training mode of DL-based optical monitors…”

Tabla de Contenidos: “…8.1 Introduction 179 -- 8.2 Scenario and Application 182 -- 8.2.1 Concept Definition 182 -- 8.2.2 Fog-enabled IoT Application 184 -- 8.2.3 Characteristics and Open Challenges 185 -- 8.2.4 Orchestration Requirements 187 -- 8.3 Architecture: A Software-Defined Perspective 188 -- 8.3.1 Solution Overview 188 -- 8.3.2 Software-Defined Architecture 189 -- 8.4 Orchestration 191 -- 8.4.1 Resource Filtering and Assignment 192 -- 8.4.2 Component Selection and Placement 194 -- 8.4.3 Dynamic Orchestration with Runtime QoS 195 -- 8.4.4 Systematic Data-Driven Optimization 196 -- 8.4.5 Machine-Learning for Orchestration 197 -- 8.5 Fog Simulation 198 -- 8.5.1 Overview 198 -- 8.5.2 Simulation for IoT Application in Fog 199 -- 8.5.3 Simulation for Fog Orchestration 201 -- 8.6 Early Experience 202 -- 8.6.1 Simulation-Based Orchestration 202 -- 8.6.2 Orchestration in Container-Based Systems 206 -- 8.7 Discussion 207 -- 8.8 Conclusion 208 -- Acknowledgment 208 -- References 208 -- 9 A Decentralized Adaptation System for QoS Optimization 213 /Nanxi Chen, Fan Li, Gary White, Siobh©Łn Clarke, and Yang Yang -- 9.1 Introduction 213 -- 9.2 State of the Art 217 -- 9.2.1 QoS-aware Service Composition 217 -- 9.2.2 SLA (Re-)negotiation 219 -- 9.2.3 Service Monitoring 221 -- 9.3 Fog Service Delivery Model and AdaptFog 224 -- 9.3.1 AdaptFog Architecture 224 -- 9.3.2 Service Performance Validation 227 -- 9.3.3 Runtime QoS Monitoring 232 -- 9.3.4 Fog-to-Fog Service Level Renegotiation 235 -- 9.4 Conclusion and Open Issues 240 -- References 240 -- 10 Efficient Task Scheduling for Performance Optimization 249 /Yang Yang, Shuang Zhao, Kunlun Wang, and Zening Liu -- 10.1 Introduction 249 -- 10.2 Individual Delay-minimization Task Scheduling 251 -- 10.2.1 System Model 251 -- 10.2.2 Problem Formulation 251 -- 10.2.3 POMT Algorithm 253 -- 10.3 Energy-efficient Task Scheduling 255 -- 10.3.1 Fog Computing Network 255 -- 10.3.2 Medium Access Protocol 257 -- 10.3.3 Energy Efficiency 257 -- 10.3.4 Problem Properties 258.…”