Energy Conservation Solutions for Fog-Edge Computing Paradigms (Lecture Notes on Data Engineering and Communications Technologies)

Preface
Contents
Editors and Contributors
Energy-Aware Resource Scheduling in FoG Environment for IoT-Based Applications
	1 Introduction
		1.1 Why FoG Computing?
		1.2 FoG Application Areas in Smart City
		1.3 Motivation
		1.4 Contributions
		1.5 Organization of Work
	2 Need of FoG Computing
		2.1 QoS Parameters
	3 Energy-Aware Survey in Green-IoT
	4 Related Work
		4.1 Resource Allocation and Provisioning in Cloud Computing
		4.2 Resource Allocation and Provisioning in Fog Computing
	5 Energy Resource Scheduling Algorithms and Intersection of Parameters in Smart City (Smart Homes)
	6 Future Scope
	References
DoSP: A Deadline-Aware Dynamic Service Placement Algorithm for Workflow-Oriented IoT Applications in Fog-Cloud Computing Environments
	1 Introduction
	2 Motivation Scenario
		2.1 Sensing Module
		2.2 Data Aggregation Module
		2.3 Data Analysis Module
		2.4 Decision Making
		2.5 Actuation Module
	3 Related Work
		3.1 Building and Deployed Fog-Based IoT Applications
		3.2 Resource Allocation for IoT Applications in Fog Environment
		3.3 Service Placement in Fog Environment
		3.4 A Qualitative Comparison
	4 System Model
		4.1 Application Prioritizing Phase
		4.2 Node Selection Phase
		4.3 IoT Application Placement Flow in Fog Environment
	5 Proposed Methodology
		5.1 Overview of the Proposed Work
		5.2 Functional Details of the Proposed Work
		5.3 Deadline-oriented Service Placement Algorithm (DoSP)
	6 Performance Evaluation
		6.1 System Setup and Parameters
		6.2 Experimental Results
	7 Conclusion and Future Work
	References
Improvement of Task Offloading for Latency Sensitive Tasks in Fog Environment
	1 Introduction
	2 Cloud, Edge and Fog Computing
	3 Literature Review
	4 Smart Flower Optimization Algorithm
	5 Application of SFOA to Task Offloading Problem
	6 Simulations and Results
	7 Conclusion
	References
A Sustainable Energy Efficient IoT-Based Solution for Real-Time Traffic Assistance Using Fog Computing
	1 Introduction
		1.1 Vehicle Tracking System
		1.2 Need of Vehicle Tracking
		1.3 Problem Statement
	2 Related Work
	3 Background
		3.1 IoT
		3.2 Cloud Computing
		3.3 Fog Computing
		3.4 Cloud, Fog, and IoT
	4 Proposed Architecture
		4.1 Problem Formulation and Solution
	5 Simulation and Results
	6 Conclusion
	References
Analysis on Application of Fog Computing in Industry 4.0 and Smart Cities
	1 Introduction
	2 Overview of Fog Computing
	3 Overview of Industry 4.0 and Smart Cities
	4 Related Studies
	5 A Fog Computing Enabled Smart City
	6 Facilitating Industry 4.0 Using Fog Computing Architecture
	7 Three-Layered Fog-Based IoT Architecture for Industry 4.0
	8 Advantages of Using Fog Computing for Industry 4.0 and Smart Cities
	9 Conclusion
	References
Fog-Computing: A Novel Approach for Cloud-Based Devices Using Perceptual Cloning Manifestation-PerColNif Taxonomy by Energy Optimization
	1 Introduction
		1.1 Fog as Eminent Computing Paradigm
	2 Related Works
		2.1 Cloud Computing Era in Health Care Services
		2.2 Cloud Computing for Vehicle Tracking–Accident Detection Systems
		2.3 Fog Computing Versus Cloud Computing for Time-Sensitive Applications
		2.4 Virtual Clusters—Fog Bridging Cloud with Edge Devices
	3 Tri-Layered Proposed Architecture
	4 Proposed Taxonomy—Fog-Computing–PerColNif-Taxonomy
		4.1 Perceptual Cloudlet Cloning Manifestation Process [PerColNif]
		4.2 PerColNif-Algorithm Implementation
		4.3 Replacement Services–Node–Node Communication
	5 Fog-Computing Implementation
		5.1 Algorithm: ADT-Relax Mode-Quick Mode Sensor Node-Accident Detection Unit
		5.2 Algorithm—Fog Node Execution
	6 Advantages of PerColNif Using Fog-Computation
	7 Energy Efficiency Performance Analysis
	8 Open Challenges on Future Fog-Era
	9 Conclusion
	References
Performance Evaluation and Energy Efficient VM Placement for Fog-Assisted IoT Environment
	1 Introduction
		1.1 Motivation
	2 Related Work
	3 System Model
	4 Performance Measures
	5 Profit and Revenue Analysis of the System
	6 The Energy Model of the Fog System
		6.1 VM Placement Problem Formulation
		6.2 Greedy Heuristic Algorithm for Energy Saving
		6.3 An Illustration
		6.4 Simulation Results
	7 Conclusion
	References
Load Balancing in Fog Computing Using QoS
	1 Introduction
		1.1 Fog Computing Architecture Layers
		1.2 Fog Computing Elementary Layers
		1.3 Communication Workflow in Fog Environment
	2 Related Work
		2.1 Cyber Foraging
		2.2 Cloudlet
	3 Proposed Framework
		3.1 Optimized Load Balancing Algorithm (OLBA)
		3.2 Environmental Setup
	4 Results and Discussions
		4.1 Turn Around Time Performance Test
		4.2 Fog Resources Resource Utilization
		4.3 Average Response Time
		4.4 Processing Delay
	5 Conclusion and Future Scope
	References
Fog Computing in Industry 4.0: Applications and Challenges—A Research Roadmap
	1 Introduction
		1.1 Fog Computing
		1.2 Industry 4.0
	2 Fog Architecture for Industrial Processes
	3 Fog Equipped Industrial IoT
		3.1 Transportation
		3.2 Smart Grids
		3.3 Mining
		3.4 Agriculture
		3.5 Food Industry
		3.6 Waste Management
		3.7 Parking
	4 Fog Computing in Industry 4.0
		4.1 Industrial Internet of Things
		4.2 Big Data
		4.3 Cloud Computing
		4.4 Advancement in Robotics
		4.5 Smart Manufacturing
		4.6 Flexibility in Machines
		4.7 Smart City Applications
		4.8 Smart Factory Applications
		4.9 Predictable Maintenance
		4.10 Augmentative Reality
	5 Research Challenges
		5.1 Heterogeneity
		5.2 Security
		5.3 Programmability
		5.4 Interoperability
		5.5 Energy Consumption
		5.6 Quality of Service (QoS)
		5.7 Cost
	6 Conclusions
	References
Fog Computing Based Architecture for Smart City Projects and Applications
	1 Introduction
		1.1 Fog Computing and IoT
		1.2 Fog Computing Versus Cloud Computing
		1.3 Contribution
	2 Related Work
	3 Smart City Projects in India
		3.1 Industry 4.0 and Smart City Projects
	4 Role of Fog Computing in Industry 4.0 for Smart City Projects
	5 Fog Computing Use Cases for Smart City Projects
		5.1 Smart Waste Management
		5.2 IoT-Based Smart Waste Management Systems
		5.3 Proposed Fog Computing Based Smart Waste Management Architecture
		5.4 Execution Flow of Proposed Smart Waste Management Architecture
		5.5 Smart Parking
		5.6 IoT Based Smart Parking Architectures
		5.7 Proposed Fog Computing Based Smart Parking Architecture
		5.8 Execution Flow of Proposed Architecture
	6 Conclusion
	7 Future Work
	References
Integration of Fog Computing and IoT-Based Energy Harvesting (EHIoT) Model for Wireless Sensor Network
	1 Introduction
	2 Background
		2.1 Medical Sensors
		2.2 IoT and Fog Computing in Smart Healthcare
		2.3 Energy Consumption Models
	3 Related Works
	4 Proposed Design
		4.1 Design of an IoT/Fog-Based WSN Model for Hospital Environment
		4.2 Design of the Energy Consumption Model
		4.3 Design of IoT-Based Energy Harvesting Model (EHIoT)
	5 Conclusion
	References
Design and Development of Efficient Secure Routing Mechanism for Wireless Sensor Network
	1 Background
	2 High-Level Techniques (HIT)
	3 Problem Description
	4 Study Objectives
	5 Literature Review
		5.1 Literature Survey Based on Hierarchical Routing Protocols
		5.2 Existing Research Work on Delay Concept in Routing for WSN
	6 Energy-Efficient and Security-Aware Routing Protocols
		6.1 FEESR Design Methodology
		6.2 FEESR Algorithm Design and Implementation
		6.3 Feesr Numerical Analysis and Outcome Comparison
		6.4 Result Analysis
	7 Conclusion
		7.1 Scope and Limitations of the Study
		7.2 Future Scope of Applicability
	References
Futuristic Communication Systems Using Mobile Edge Computing
	1 Introduction
	2 Outline of MEC and 5G
		2.1 Fundamentals of MEC
		2.2 Integration of MEC with 5G Systems
	3 Overview of MEC/5G Researches
		3.1 Internet of Things (IoT) Leveraging MEC
		3.2 MEC with NOMA
		3.3 MEC with Heterogeneous CRAN
		3.4 MEC with UAV Communications
		3.5 MEC with WPT and EH
	4 Conclusions
	References
Methodology to Ensure the Continuity of the Information Systems Service, Based on the Monitoring of Electrical Energy, Using IoT Technology
	1 Introduction
	2 Materials and Methods
		2.1 Literature Review
		2.2 Analysis of IoT Devices
		2.3 Choice of Devices to Implement the Methodology
		2.4 Device Configuration
		2.5 Performance Analysis
		2.6 Interpretation of Results
	3 Results
		3.1 Server Unit
		3.2 Power and Control Units
		3.3 Monitoring Units
		3.4 Communication Units
		3.5 Cooling Unit
		3.6 Acquisition Units
		3.7 IoT Units
		3.8 Display Unit
	4 Conclusions
	References