Handbook of Integration of Cloud Computing, Cyber Physical Systems and Internet of Things

Preface
Contents
Contributors
Editors and Contributors
About the Editors
Context-Aware IoT-Enabled Cyber-Physical Systems: A Vision and Future Directions
1 Introduction
2 ICICLE: A Context-Aware IoT-Enabled Cyber-Physical System
3 Case Study: ICICLE for Emergency Management
4 Future Challenges and Directions
5 Conclusion
References
Trustworthy Service Selection for Potential Users in Cloud Computing Environment
1 Introduction
2 Trustworthiness Evaluation for Cloud Services
2.1 Definition of Trustworthy Cloud Services
2.2 Evaluating Trustworthiness of Cloud Service
2.3 Calculating Trustworthiness Value of Cloud Service
2.3.1 Direct Trustworthiness Value
2.3.2 Predicted Trustworthiness Value
3 Typical Approaches on Trustworthy Cloud Service Selection
3.1 Recommendation-Based Approaches
3.2 Prediction-Based Approaches
3.3 MCDM-Based Approaches
3.4 Reputation-Based Approaches
4 Metrics Indicators for Trustworthy Cloud Service Selection
4.1 Mean Absolute Error and Root-Mean Square Error
4.2 Difference Degree
5 User Feature-Aware Trustworthy Service Selection via Evidence Synthesis for Potential Users: A Case Study
5.1 Measuring User Features Similarity Between Users
5.2 Computing Weights of User Features Based on FAHP
5.3 Predicting Trustworthiness of Candidate Services for Potential User
5.4 Experiment
5.4.1 Experiment Setup
5.4.2 Experiment Result and Analysis
6 Summary and Further Research
References
Explorations of Game Theory Applied in Cloud Computing
1 Background and Motivation
2 Related Works
3 Strategy Configurations of Multiple Users Competition for Cloud Service Reservation
3.1 Model Formulation and Analyses
3.1.1 Request Profile Model
3.1.2 Load Billing Model
3.1.3 Cloud Service Model
3.1.4 Architecture Model
3.1.5 Problem Formulation
3.2 Game Formulation and Analyses
3.2.1 Game Formulation
3.2.2 Billing Parameters Analysis
3.2.3 Nash Equilibrium Analysis
3.2.4 An Iterative Proximal Algorithm
3.3 Performance Evaluation of IPA
4 A Framework of Price Bidding Configurations for Resource Usage in Cloud Computing
4.1 System Model and Problem Formulation
4.1.1 Bidding Strategy Model
4.1.2 Server Allocation Model
4.1.3 Cloud Service Model
4.1.4 Architecture Model
4.1.5 Problem Formulation
4.2 Game Formulation and Analyses
4.2.1 Game Formulation
4.2.2 Nash Equilibrium Existence Analysis
4.2.3 Nash Equilibrium Computation
4.2.4 A Near-Equilibrium Price Bidding Algorithm
4.3 Performance Evaluation
5 Conclusions
References
Approach to Assessing Cloud Computing Sustainability
1 Introduction
2 Sustainability
2.1 The Connection Between Sustainability and Cloud Computing?
2.2 Cloud Computing Sustainability Models – What the Others Have Done in This Field?
3 The Multi-objective Cloud Computing Sustainability Assessment Framework
3.1 The Principles of the Modelling and Integration of the Business Objectives
3.1.1 QoS Maximization
3.1.2 Security Provisioning
3.1.3 Resources Usage
3.1.4 Efficiency of the Resources Utilization
3.2 The MO Framework Methodology and Comparison to the UN Model
3.2.1 Comparison Details
3.2.2 Open Data, Big Data, and Smart Data in the Context of CC Sustainability
3.3 Sustainable Cloud Computing in Modern Technologies
3.3.1 Towards the Integration of the Internet of Things and Sustainable Cloud Computing
3.3.2 Cyber Physical in Sustainable Cloud Computing
4 Conclusion
References
Feasibility of Fog Computing
1 An Overview
2 Definition and Characteristics of Fog Computing
2.1 Definition
2.2 Characteristics
2.2.1 Vertical Scaling
2.2.2 Heterogeneity
2.2.3 Visibility and Accessibility
2.2.4 Volume
3 The Fog Computing Ecosystem
3.1 Computing Nodes
3.1.1 Traffic Routing Nodes
3.1.2 Capability Added Nodes
3.1.3 Peer Nodes
3.2 Workload Execution Models
3.2.1 Offloading Model
3.2.2 Aggregating Model
3.2.3 Sharing Model
3.2.4 Hybrid Model
3.3 Workload Deployment Technologies
3.3.1 Containers
3.3.2 Virtual Machines (VMs)
3.4 The Marketplace
3.4.1 Ownership
3.4.2 Pricing Models
3.4.3 Customers
3.5 Other Concepts to Consider
3.5.1 Priority-Based Multi-tenancy
3.5.2 Complex Management
3.5.3 Enhanced Security and Privacy
3.5.4 Lighter Benchmarking and Monitoring
4 Preliminary Results
5 Conclusions
References
Internet of Things and Deep Learning
1 Introduction
1.1 The Era of Big Data
1.2 Supervised Learning Algorithms
1.3 Unsupervised Learning Algorithms
1.4 Common Deep Learning Models
1.4.1 Extreme Machine Learning Model
1.4.2 Deep Neural Network System
2 Extreme Machine Learning Model
3 Convolutional Neural Network
3.1 Convolutional Layer
3.2 Contrast Normalization Layer
3.3 Maxing Pooling Layer
3.4 Softmax
4 Regularizations for Deep Learning
4.1 Dataset Augmentation
4.2 Noise Robustness
4.3 Dropout
4.4 Other Regularizations
5 Applications
5.1 Large Scale Deep Learning for Age Estimation
5.2 Large Scale Deep Learning for Gender Estimation
5.3 Natural Language Processing
5.4 Other Applications
References
Cloud, Context, and Cognition: Paving the Way for Efficient and Secure IoT Implementations
1 Introduction and Contents
2 Related Work
2.1 Existing Architectures
2.1.1 Classical Connectivity
2.1.2 Smart Hub Connectivity
2.1.3 Cloud Mirroring
2.2 Existing Approaches to Minimizing Resource Use and Improving Security
2.2.1 Resource Efficiency
2.2.2 Privacy and Security
3 Human-Inspired IoT
3.1 Varied Request Priorities
3.2 Data Synthesis
3.3 Multiple-Use of Replies
3.4 Malicious Request Blocking
3.5 Resource Safeguarding
3.6 Command Simulation
3.7 System Supervision
4 System Embodiment
4.1 Quality of Data
4.2 Security Layer
4.3 Cognitive Layer
4.4 Data Proxies
4.5 Application Agent
5 Proxy Models
5.1 State Space Models
5.1.1 Discrete Time State Observer
5.1.2 Discrete Time State Estimator
5.2 Modeling Resource Consumption
6 Machine Learning Based Modeling
7 Real-World Data Proxy Examples
7.1 Proxy Model
7.2 Costs
7.3 Objectives & Constraints
7.4 Results
8 Conclusions
8.1 Future
References
A Multi-level Monitoring Framework for Containerized Self-Adaptive Early Warning Applications
1 Introduction
2 Basic Framework of an Early Warning System
3 Monitoring Requirements
3.1 VM-Level Monitoring
3.2 P2P Link Quality Monitoring
3.3 Container-Level Monitoring
3.4 Application-Level Monitoring
4 Architecture of our Multi-Level Monitoring Framework
5 Architecture of our Adaptation Solution
6 Conclusion
References
Challenges in Deployment and Configuration Management in Cyber Physical System
1 Introduction
2 CPS Architecture
2.1 Data Analytics in CPS
3 Deployment and Configuration Management
3.1 Deployment and Configuration Management in CPS
3.2 Dimensions of Deployment and Configuration Management
4 Configuration Management Tools: State of the Art
5 Evaluation of Deployment and Configuration Management Tools
6 Conclusion
References
The Integration of Scheduling, Monitoring, and SLA in Cyber Physical Systems
1 Introduction
1.1 Motivation Example
2 Cyber-Physical Systems (CPS) Architecture
3 Studies Related to Scheduling, Monitoring, and End-to-End SLA (SMeSLA) in CPS
3.1 Scheduling in CPS
3.2 Monitoring in CPS
3.3 SLA in CPS
4 SMeSLA Technical-Research Challenges in CPS
4.1 Sensing Devices
4.2 Gateways
4.3 Big Data Analytics Tools
5 SMeSLA General-Research Challenges in CPS
5.1 The Heterogeneity and Random Distribution of IoT Devices
5.2 Lack of Standardization
5.3 Heterogeneity of Key QoS Metrics Across CPS Environments
5.4 Heterogeneity of Application Requirements
5.5 Lack of Methods for Collecting QoS Metrics
5.6 Selection of Datacentres
6 Design Goals of SMeSLA in CPS
7 Conclusion
References
Experiences and Challenges of Providing IoT-Based Care for Elderly in Real-Life Smart Home Environments
1 Introduction
2 Motivation
3 Scenario and Aims
4 iVOS System Design and Implementation
4.1 System Description and Deployment
4.2 Integration with SSR Platform
4.3 Participants and Sensor Installation
4.4 Deployment Challenges: Installation Experience, Challenges Faced and Lessons Learnt
4.4.1 Layout of the Apartment and Placement of Sensors
4.4.2 Motion Sensors
4.4.3 Door/Window Sensors
4.4.4 Wall-Plugs
4.4.5 Other Sensors
4.4.6 Fixed Installations and Limiting Accessibility of Elderly to Sensors
4.4.7 Concerns of Privacy
4.4.8 Elderly Comfort with Sensor Installations
4.4.9 Continuous Checks and Monitoring
5 Understanding Elderly Participant's Daily Routines Via Interviews
5.1 Notifications
5.2 Positive Notifications
5.3 Alerts
6 Feedback from Elderly and Relatives
7 Discussion and Conclusion
References
Internet of Things (IoT) and Cloud Computing Enabled Disaster Management
1 Introduction
1.1 Motivating Scenario: Mitigating Flood Disaster
1.2 The Problem
2 Background
2.1 Internet of Things (IoT)
2.1.1 Sensors and Mobile Phones
2.1.2 Delay Tolerant Networks
2.1.3 Transient Social Network
2.1.4 TSN over DTN
2.2 Cloud Computing
2.2.1 Essential Characteristics
2.2.2 Service Models
2.2.3 Deployment Models
2.3 Big Data
2.3.1 Big Data Lifecycle
3 Integrated Framework for Disaster Management
3.1 Application Components
3.2 System Architecture
4 The Application
4.1 Data Sources and their Integration
4.2 Event Ontologies and Event Detection
4.3 Mobile App for TSN over DTN
4.4 Scalable Cloud Computing
4.5 Security and Privacy
5 Conclusions and Future Work
References
EVOX-CPS: Turning Buildings into Green Cyber-Physical Systems Contributing to Sustainable Development
1 Introduction
2 Optimizing Building Operations with Data
2.1 Buildings as Cyber-Physical Systems
2.2 EVOX-CPS
3 Discussion, Results, Impact
3.1 EVOX-CPS as a Building-Specific CPS Methodology
3.2 Results from Experiments
3.3 Impact: Sustainability
3.3.1 General Sustainability Effects
3.3.2 Relation to the Sustainable Development Goals
3.3.3 Relation to the Ecosystem Services and Planetary Boundaries
3.3.4 The System Conditions Sustainable Development
3.3.5 Ethics, International Co-management, and the Tragedy of the Commons
3.4 Limitations: Implications of the Energy Mix, Sustainability Reasoning
4 Conclusion
References