Big Data Platforms and Applications: Case Studies, Methods, Techniques, and Performance Evaluation (Computer Communications and Networks)

Preface
Acknowledgments
Contents
About the Editors
1 Data Center for Smart Cities: Energy and Sustainability Issue
	1.1 Introduction
	1.2 State-of-The-Art Overview
	1.3 Methodology
		1.3.1 Data Center Facilities and Dataset Description
		1.3.2 Data Analysis
		1.3.3 Metrics
		1.3.4 Energy Waste Analysis
	1.4 Results: DC Cluster Energy Consumption
		1.4.1 Energy Use by Applications
		1.4.2 Energy Analysis of Queues of Jobs
		1.4.3 Energy Use by Parallel and Serial Jobs
		1.4.4 Assessment of Useful Work
		1.4.5 Assessment of Energy Waste
		1.4.6 Sustainability Analysis
	1.5 Discussion
		1.5.1 Energy Efficiency Benefits and Concerns of Jobs Execution in Parallel Mode
		1.5.2 Data Center Energy Efficiency Policies and Strategies
		1.5.3 Sustainability-Oriented DC
	1.6 Conclusion
	References
2 Apache Spark for Digitalization, Analysis and Optimization of Discrete Manufacturing Processes
	2.1 Introduction
	2.2 Background
		2.2.1 IoT for Smart Manufacturing Processes
		2.2.2 Machine Learning Approaches for Manufacturing Process Analysis
		2.2.3 Manufacturing Processes Optimization Literature Approaches
		2.2.4 Bio-Inspired Techniques for Tuning the Parameters of Machine Learning Models
		2.2.5 Approaches Used in Our Research for the Analysis of the Faults in Manufacturing Processes
	2.3 Materials and Methods
		2.3.1 Architectural Prototype for Simulating the Manufacturing of FL Series Regulators
		2.3.2 Machine Learning Methodology for Detecting Faulty Products in Discrete Manufacturing Processes
		2.3.3 Data Preprocessing in KNIME (Konstanz Information Miner)
		2.3.4 Discrete Manufacturing Processes Optimization Based on Big Data Technologies
	2.4 Results
		2.4.1 Description of the Datasets Used in Experiments
		2.4.2 Classification Results
	2.5 Discussion
	2.6 Conclusions
	References
3 An Empirical Study on Teleworking Among Slovakia’s Office-Based Academics
	3.1 Introduction
	3.2 Methodology
	3.3 Meaning of Telecommuting or Teleworking
		3.3.1 Teleworking in Slovakia
	3.4 Office-Based Teleworking Results
	3.5 Discussion
	3.6 Conclusions
	References
4 Data and Systems Heterogeneity: Analysis on Data, Processing, Workload, and Infrastructure
	4.1 Introduction
	4.2 Data Types, Formats, and Models
	4.3 Processing Models and Platforms
	4.4 Workload Types
	4.5 Infrastructure Types
	4.6 Conclusion
	References
5 exhiSTORY: Smart Self-organizing Exhibits
	5.1 Introduction
	5.2 The Stories Told by Exhibits
	5.3 The Smart Exhibit
		5.3.1 Centralized System Control
		5.3.2 Automated Exhibit Geolocation
		5.3.3 Security Aspects
		5.3.4 Selecting an Implementation Option for Smart Exhibits
	5.4 System Architecture
		5.4.1 The Smart Space
		5.4.2 The Knowledge Base
		5.4.3 The Intelligent Modules
	5.5 The exhiSTORY System in Operation
	5.6 Discussion and Conclusions
	References
6 IoT Cloud Security Design Patterns
	6.1 Introduction
	6.2 Design of IoT Architecture Layers
		6.2.1 Security Aspects
	6.3 IoT Network Design Patterns
		6.3.1 Security of IoT Networks
		6.3.2 Design Patterns for a Secure IoT Network
	6.4 IoT Cloud Platform Design Patterns
		6.4.1 Security Division Pattern
		6.4.2 Digital Twin Pattern
		6.4.3 Secure Design Through Microservices
		6.4.4 Push Notification Pattern
		6.4.5 Cloud and Smartphone Management Pattern
		6.4.6 Cloud-Assisted Network Access Pattern
	6.5 Discussion and Conclusion
	References
7 Cloud-Based mHealth Streaming IoT Processing
	7.1 Introduction
	7.2 Overview of Underlying Technology for mHealth Solutions
	7.3 Overview of IoT mHealth Solutions
	7.4 Cloud-Based Architectures
	7.5 Issues for Streaming mHealth IoT Solutions
	7.6 Architectures for Streaming mHealth IoT Solutions
	7.7 Discusion
		7.7.1 Comparison of Architectural Concepts
		7.7.2 Benefits
		7.7.3 Use Case: A Monitoring Center Based on Streaming IoT mHealth Solutions
	7.8 Conclusion
	References
8 A System for Monitoring Water Quality Parameters in Rivers. Challenges and Solutions
	8.1 Introduction
	8.2 Water Quality Monitoring Systems Challenges
		8.2.1 Water Quality Parameters Acquisition Using WSNs
		8.2.2 Pollution Detection
		8.2.3 Standards for Hydrographic and Monitoring Data
	8.3 A Service-Based System Architecture for Water Quality Monitoring
		8.3.1 Data Sources
		8.3.2 Data Storage, Processing and Data Provision Services
		8.3.3 Information Services
	8.4 A Pollution Detection System for Somes River
		8.4.1 Data Acquisitions and Storage
		8.4.2 Discharge Computation
		8.4.3 The Rule-Based Automatic Assessment of Water Quality and Pollution Alert Service
		8.4.4 Simulation of Pollutant Propagation
		8.4.5 The Water Quality Information Web Application
	8.5 Conclusions
	References
9 A Survey on Privacy Enhancements for Massively Scalable Storage Systems in Public Cloud Environments
	9.1 Introduction
	9.2 Cloud Storage Encryption Prerequisites
	9.3 Scalable Cloud Storage Encryption Schemes
	9.4 Technology Survey Regarding Service Providers
	9.5 Technology Survey Regarding Classic and Emerging Cryptographic Primitives
		9.5.1 Confidentiality Primitives
		9.5.2 Integrity Primitives
	9.6 Technology Survey Regarding Third-Party Applications
		9.6.1 Viivo
		9.6.2 AES Crypt
	9.7 Proposed Solution
		9.7.1 Architecture
		9.7.2 General Description
		9.7.3 The Java Card Applet
		9.7.4 Storage Layout and Data Structures
	References
10 Energy Efficiency of Arduino Sensors Platform Based on Mobile-Cloud: A Bicycle Lights Use-Case
	10.1 Introduction
	10.2 Mobile Cloud Computing
	10.3 The System for Energy Efficiency of Arduino Sensors
	10.4 Smart Bicycle Lighting Architecture
	10.5 Conclusions
	References
11 Cloud-Enabled Modeling of Sensor Networks in Educational Settings
	11.1 Introduction
	11.2 Related Work
		11.2.1 Sensor Cloud
		11.2.2 Education Cloud
	11.3 Sensor Network Modeling
		11.3.1 Language and Tools
		11.3.2 Extensions and Model Interpreters
	11.4 System Architecture
	11.5 Educational Service in Cloud
		11.5.1 Service Request and Handling
		11.5.2 The Provisioning Process
	11.6 Experimental Results
	11.7 Conclusion
	References
12 Methods and Techniques for Automatic Identification System Data Reduction
	12.1 Introduction
	12.2 Related Work
	12.3 AIS Technology
	12.4 Algorithm Analysis
		12.4.1 Analyzing the Data Set
	12.5 Experimental Evaluation
		12.5.1 Analyzed Data
		12.5.2 Data Reduction Applied on AIS Data Set
		12.5.3 Data Visualization
	12.6 Conclusion and Future Work
	References
13 Machine-to-Machine Model for Water Resource Sharing in Smart Cities
	13.1 Introduction
	13.2 Current Stage of Development in the Field
		13.2.1 EOMORES Project—Copernicus Platform
		13.2.2 AquaWatch Project
		13.2.3 SmartWater4Europe Project
		13.2.4 OPC UA with MEGA Model Architecture
		13.2.5 WATER-M Project
	13.3 Smart City Water Management Available Technologies
		13.3.1 GIS (Geographic Information System)
		13.3.2 IBM Water Management Platform
		13.3.3 TEMBOO Platform—IoT Applications
		13.3.4 RoboMQ
	13.4 Proposed Model and Possible Directions
	13.5 Possibilities of Implementation
		13.5.1 Message-Oriented Middleware—RabbitMQ
	13.6 Conclusions
	References
Index