New Frontiers in Cloud Computing and Internet of Things

Preface
	The Book Organisation
		Cloud Computing
		Internet of Things (IoT)
Acknowledgements
Contents
Part I Cloud Computing
	1 Cloud Computing and Internet of Things: Recent Trends and Directions
		Contents
		1.1 Introduction
			1.1.1 Cloud Computing
			1.1.2 Internet of Things (IoT)
		1.2 Key Cloud Technologies and Services
			1.2.1 Infrastructure as a Service (IaaS)
			1.2.2 Container as a Service (CaaS)
			1.2.3 Platform as a Service (PaaS)
			1.2.4 Function as a Service (FaaS)
			1.2.5 Software as a Service (SaaS)
		1.3 Key IoT Technologies and Applications
			1.3.1 Things
			1.3.2 Communication Protocols
				1.3.2.1 IoT Data Protocols
				1.3.2.2 IoT Network Protocols
			1.3.3 IoT Frameworks
				1.3.3.1 Proprietary-Based
				1.3.3.2 Open Source
			1.3.4 Deployment Models
				1.3.4.1 Cloud-Centric IoT
				1.3.4.2 Edge/Fog-Centric IoT
			1.3.5 Applications
		1.4 Modeling and Simulation Toolkits
		1.5 Open Challenges
		1.6 Summary
		References
	2 Recent Advances in Energy-Efficient Resource Management Techniques in Cloud Computing Environments
		Contents
		2.1 Introduction
			2.1.1 Motivation and Contribution
		2.2 Related Work
		2.3 Proposed Taxonomy for Energy-Aware Resource Management Solutions in Cloud Environments
			2.3.1 Goals
			2.3.2 Workload
			2.3.3 Resources
			2.3.4 Dynamism
				2.3.4.1 Static Power Management (SPM)
				2.3.4.2 Dynamic Power Management (DPM)
		2.4 Virtualization Level
			2.4.1 Migration
				2.4.1.1 VM Migration Type
				2.4.1.2 Container Migration Type
				2.4.1.3 Migration Cost
			2.4.2 Consolidation
				2.4.2.1 Consolidation Level
				2.4.2.2 Consolidation Subproblems
		2.5 Comparing the State of the Art
		2.6 Future Scope and Conclusion
			2.6.1 Conclusion
		References
	3 Multi-objective Dynamic Virtual Machine Consolidation Algorithm for Cloud Data Centers with Highly Energy Proportional Servers and Heterogeneous Workload
		Contents
		3.1 Introduction
		3.2 Related Work
		3.3 Modelling Stochastic VM Release Time, Notations Used and Important Concepts
			3.3.1 Modelling Stochastic VM Release Time, PM Release Time and Notations Used
			3.3.2 Modelling Resource Utilization and Constraints
			3.3.3 Modelling Energy Consumption
			3.3.4 Objective Functions
		3.4 Proposed Solution
			3.4.1 Two Phases of SRTDVMC Algorithm
				3.4.1.1 The First Phase O-UPMs
				3.4.1.2 The Second Phase U-UPMs
			3.4.2 Characteristics of Proposed Algorithm
		3.5 Performance Evaluation
			3.5.1 Experimental Setup
			3.5.2 Performance Metrics and Workload Data
				3.5.2.1 Performance Metrics
				3.5.2.2 Workload Data
		3.6 Simulation Results Analysis
			3.6.1 Energy Consumption
				3.6.1.1 Normality Testing
				3.6.1.2 Parametric Hypothesis Testing and Test Error
			3.6.2 VM Migration
				3.6.2.1 Normality Testing
				3.6.2.2 Parametric Hypothesis Testing and Test Error
		3.7 Observations
		3.8 Conclusions and Future Work
			3.8.1 Conclusions
			3.8.2 Future Work
		References
	4 Energy-Efficient Resource Management of Virtual Machine in Cloud Infrastructure
		Contents
		4.1 Introduction
		4.2 Background
		4.3 Proposed Work
			4.3.1 Design of Cloud Environment
			4.3.2 Selection of Workload
			4.3.3 Allocation Policy
				4.3.3.1 Genetic Algorithm
				4.3.3.2 Shuffled Frog Leaping Algorithm
			4.3.4 Framework Monitor
			4.3.5 Migration of Virtual Instances
				4.3.5.1 High Loaded Host Detection
				4.3.5.2 Low Loaded Host Detection
			4.3.6 VM Selection Policy
				4.3.6.1 Minimum Time on Migration (MTM)
		4.4 Result and Analysis
			4.4.1 Experimental Results
			4.4.2 Analysis of Complexity of Time
		4.5 Conclusion and Future Work
		References
	5 Dynamic Resource-Efficient Scheduling in Data Stream Management Systems Deployed on Computing Clouds
		Contents
		5.1 Introduction
		5.2 Background
		5.3 Dynamic Resource-Efficient Scheduling
			5.3.1 Problem Formulation
			5.3.2 Heuristic-Based Scheduling Algorithm
			5.3.3 Complexity Analysis
		5.4 Implementation of D-Storm Prototype
		5.5 Performance Evaluation
			5.5.1 Experiment Setup
				5.5.1.1 Test Applications
				5.5.1.2 Parameter Selection and Evaluation Methodology
			5.5.2 Evaluation of Applicability
			5.5.3 Evaluation of Cost Efficiency
			5.5.4 Evaluation of Scheduling Overhead
		5.6 Related Work
		5.7 Conclusions and Future Work
		References
	6 Serverless Platforms on the Edge: A Performance Analysis
		Contents
		6.1 Introduction
		6.2 Background
			6.2.1 Motivation
			6.2.2 History of Serverless
			6.2.3 Serverless at the Edge
		6.3 Related Works
			6.3.1 AWS Lambda
			6.3.2 AWS Greengrass
			6.3.3 Azure Functions
			6.3.4 Apache OpenWhisk
			6.3.5 OpenFaaS
		6.4 Performance Evaluation
			6.4.1 Experimental Setup
				6.4.1.1 Testbed Setup
				6.4.1.2 Test Functions
			6.4.2 Results
				6.4.2.1 CPU-Intensive Functions
				6.4.2.2 Memory-Intensive Functions
				6.4.2.3 Disk-Intensive Functions
		6.5 Discussions
		6.6 Conclusions and Future Work
		Bibliography
	7 ITL: An Isolation-Tree-Based Learning of Features for Anomaly Detection in Networked Systems
		Contents
		7.1 Introduction
		7.2 Related Work
		7.3 Model Assumptions and an Overview on Isolation-Based Anomaly Detection
		7.4 ITL Approach
			7.4.1 Feature Refinement Process
		7.5 Performance Evaluation
			7.5.1 Experimental Settings
			7.5.2 Experiment Results
				7.5.2.1 ITL with Bagging of the Scores
				7.5.2.2 ITL with Reduced Features
				7.5.2.3 Time Complexity and Runtime Analysis
			7.5.3 Strength and Limitations of ITL Approach
		7.6 Conclusions and Future Work
		References
	8 Digital Twin of a Cloud Data Centre: An OpenStack Cluster Visualisation
		Contents
		8.1 Introduction
		8.2 Background
			8.2.1 A Taxonomy of Digital Twin Characteristics
				8.2.1.1 Digital Twin Usage
				8.2.1.2 Period of Production
				8.2.1.3 Connection Between Physical Twin and Digital Twin
				8.2.1.4 Building Blocks of a Digital Twin
		8.3 Digital Twin of a Cloud Data Centre Instance System
			8.3.1 Unity Model
				8.3.1.1 Main Components
				8.3.1.2 Digital Twin Interactive Features
			8.3.2 API Integration
		8.4 Qualitative Study
		8.5 Evaluation and Results
		8.6 Discussion and Future Directions
		8.7 Conclusions
		References
Part II Internet of Things
	9 Industrial IoT Technologies and Protocols
		Contents
		9.1 Introduction
			9.1.1 What Is IoT?
			9.1.2 The Paths for Data Transmission Through the Connectivity and Networks
			9.1.3 Baseline Technologies
			9.1.4 Connectivity Terminologies
				9.1.4.1 Connectivity Layers
		9.2 Network Topology
		9.3 Addressing Manners
		9.4 Sensors and Transducers
			9.4.1 Types of Sensors
		9.5 Components of IoT
			9.5.1 Communication Protocols
			9.5.2 IEEE 802.15.4
			9.5.3 Zigbee
			9.5.4 Wireless HART
			9.5.5 NFC Communication Protocol
			9.5.6 Bluetooth Communication Protocol
			9.5.7 Bluetooth Piconets
			9.5.8 Z-Wave
			9.5.9 ISA 100.11A
		9.6 Conclusion
		References
	10 IoT for Sustainability
		Contents
		10.1 Global Trends
			10.1.1 Climate Change
			10.1.2 Urbanisation
			10.1.3 Linear Business Models
			10.1.4 Technology and Automation
			10.1.5 Summary
		10.2 Sustainability
			10.2.1 UN Sustainable Development Goals
			10.2.2 Perspectives on Sustainability
				10.2.2.1 Developing Versus Developed Countries
				10.2.2.2 Urban Versus Rural
				10.2.2.3 Local Versus Global
				10.2.2.4 Internal Versus External Focus
			10.2.3 Computational Sustainability
		10.3 The IoT Fit
			10.3.1 Architecture
			10.3.2 Fog Computing
			10.3.3 IoT and Sustainability
			10.3.4 Smart Cities
		10.4 Taking Stock
			10.4.1 Selective Focus
			10.4.2 Top-Down Solutions
			10.4.3 Digital Colonialism
		10.5 Cellular Architectures
			10.5.1 Resilience
			10.5.2 Energy Distribution
			10.5.3 Local Clouds
			10.5.4 Smart Villages
		10.6 Current and Future Challenges
		10.7 Conclusion
		References
	11 Applications of IoT and Cloud Computing: A COVID-19 Disaster Perspective
		Contents
		11.1 Introduction
		11.2 Video Conferencing
		11.3 E-Gaming and Video Streaming
		11.4 Future Opportunities
		11.5 Application of IoT in Transportation
			11.5.1 Logistics
			11.5.2 Innovations
			11.5.3 Applications
				11.5.3.1 Inventory Tracking and Warehousing [6]
		11.6 Fleet Management [7, 8]
		11.7 Drone-Based Delivery (DBD) [10]
		11.8 Autonomous Vehicles [12]
		11.9 Future Opportunities
		11.10 Manufacturing Industry During COVID-19
		11.11 IoT-Enabled Automation for Remote Manufacturing and Businesses
		11.12 Monitoring Using IoT
			11.12.1 Vision-Based Control Systems
			11.12.2 KC 901 Smart Helmet
			11.12.3 Post-COVID-19 Opportunities
		11.13 Applications of IoT and Cloud Computing in Healthcare
			11.13.1 HealthCare
			11.13.2 New Innovations
			11.13.3 Applications
				11.13.3.1 Remote Healthcare
				11.13.3.2 Robotic Assistantship
		11.14 Post-COVID-19 Opportunities
		11.15 Applications of IoT and Cloud Computing in Agriculture
			11.15.1 Agriculture Monitoring
		11.16 Summary
		11.17 Conclusion
		References
	12 Analytics of IoT-Based System for Monitoring Students' Progress in Educational Environment
		Contents
		12.1 Introduction
		12.2 Review of Related Work
			12.2.1 Big Data
			12.2.2 The Internet of Things
			12.2.3 Cloud Computing
			12.2.4 RFID Sensing Technology
			12.2.5 Applications of RFID Technology
			12.2.6 Internet of Things and Education
		12.3 Methodology
		12.4 Use Case of the Proposed Framework
		12.5 Conclusion and Future Research Directions
		References
	13 Power System Protection on Smart Grid Monitoring Faults in the Distribution Network via IoT
		Contents
		13.1 Introduction
		13.2 Backgrounds and Literature Survey
			13.2.1 Internet of Things (IoT): Communication Technology
			13.2.2 Internet of Things (IoT): Machine: Machine Communication Technology
			13.2.3 Protection Relay
			13.2.4 Communication of IoT with Smart Grid
			13.2.5 Review of the Past Research Work
		13.3 Proposed Model
			13.3.1 Components of the Single Line Diagram of Test Distribution Network
			13.3.2 Design of the GSM Module to Be Integrated into the Tested Distribution Network
			13.3.3 Simulation of Faults
				13.3.3.1 Three-Phase Fault
				13.3.3.2 Fault Currents Result
				13.3.3.3 Relay Tripping Time
		13.4 Result and Discussion
		13.5 Recommendation
		13.6 Conclusion
		References
	14 Medical Data Analysis for IoT-Based Datasets in the Cloud Using Naïve Bayes Classifier for Prediction of Heart Disease
		Contents
		14.1 Introduction
		14.2 Background and Literature Review
		14.3 Materials and Methods
			14.3.1 Data Gathering
			14.3.2 Data Sample
			14.3.3 Data Preprocessing
			14.3.4 Proposed System
		14.4 Findings and Discussion
			14.4.1 Weka Software Environment
			14.4.2 Heart Disease Dataset Analysis
			14.4.3 Attribute Value Distribution for All Attributes
			14.4.4 K == 10-Fold Cross Validation Evaluation Technique
			14.4.5 Detailed Result of Final Analysis (11th Run)
			14.4.6 Performance Evaluation
			14.4.7 Discussion
		14.5 Conclusion and Future Works
		References
	15 The Internet of Things in Healthcare: Benefits, Use Cases, and Major Evolutions
		Contents
		15.1 Introduction
		15.2 Related Work
		15.3 IoT: Transforming Healthcare Industry
			15.3.1 Delivering Healthcare Solutions
				15.3.1.1 IoT for Patients
				15.3.1.2 IoT for Doctors
				15.3.1.3 IoT for Hospitals
				15.3.1.4 IoT for Health Insurance Organizations
			15.3.2 Salient Steps for Transforming the Healthcare Industry
		15.4 Promising Use Cases of IoT in Healthcare
			15.4.1 Reachability for Remote Patient Care
			15.4.2 Emergency Care for Patients
			15.4.3 Healthcare Management
			15.4.4 Augmenting Surgeries
			15.4.5 Risk-Based Hardware Management
			15.4.6 Drugs Management
			15.4.7 Wearables
		15.5 State of IoT in Healthcare
		15.6 Benefits of IoT in Healthcare
			15.6.1 Cost-Effective
			15.6.2 Quality-of-Treatment-Service
			15.6.3 Efficient-Diagnosis-System
			15.6.4 Action-Based Treatment
			15.6.5 Medical Resource Management
			15.6.6 Error Reduction
		15.7 Challengers of Healthcare IoT Deployments
			15.7.1 Reliable Connectivity
			15.7.2 Cybersecurity
			15.7.3 Scalable Platforms
			15.7.4 Cost
		15.8 Conclusion
		References
Index