Big Data-Enabled Internet of Things

Cover
Contents
Dedication
Foreword
About the editors
1 Introduction to big data-enabled Internet of Things
	1.1 Introduction
		1.1.1 Internet of Things
		1.1.2 Big data-enabled IoT
	1.2 Platforms for big data-enabled IoT
		1.2.1 Cloud computing
		1.2.2 Fog computing
		1.2.3 Edge computing
		1.2.4 MapReduce platforms
		1.2.5 Columnar database
	1.3 Applications of big data-enabled IoT
		1.3.1 Traffic applications
		1.3.2 Wearable IoT applications in health care
		1.3.3 Smart homes
		1.3.4 Smart cars
		1.3.5 Smart grids
	1.4 Challenges
		1.4.1 Real-time analysis
		1.4.2 Storage
		1.4.3 Quality of service
		1.4.4 Security challenges
	1.5 Recent studies in the field of big data-enabled IoT
	1.6 Conclusions
	References
2 Smarter big data analytics for traffic applications in developing countries
	2.1 Introduction
		2.1.1 Research challenges
		2.1.2 Contributions and paper structure
	2.2 Scenario and requirements
	2.3 Analytics system framework for traffic applications
		2.3.1 Design objectives
		2.3.2 Framework overview
		2.3.3 GPS data providers
		2.3.4 Offline analytics
		2.3.5 Data router and real-time analytics
		2.3.6 Decision maker
		2.3.7 Mobile and web applications
	2.4 Big data applications and challenges
		2.4.1 In-memory storage
		2.4.2 Filtering unusable data for real-time analytics
		2.4.3 Traffic monitoring and prediction
		2.4.4 Trip planning in city bus networks
	2.5 Related work
	2.6 Conclusions
	References
3 Using IoT-based big data generated inside school buildings
	3.1 Introduction
	3.2 Related work
	3.3 IoT and real-world data in education
		3.3.1 End-user requirements
		3.3.2 IoT platform design aspects
	3.4 Design aspects of an IoT platform targeting education activities
		3.4.1 End-device level
		3.4.2 IT service ecosystem level
		3.4.3 User involvement level
	3.5 The GAIA IoT platform
		3.5.1 Continuous computation engine
		3.5.2 Data access and acquisition
	3.6 Using IoT-generated big data in educational buildings
		3.6.1 High-level IoT data analysis
		3.6.2 Thermal comfort of classrooms
		3.6.3 Classroom thermal performance
	3.7 Conclusions
	Acknowledgments
	References
4 Autonomous collaborative learning in wearable IoT applications
	4.1 Transfer learning in wearable IoT
	4.2 Synchronous dynamic view learning
		4.2.1 Problem definition
		4.2.2 Problem formulation
		4.2.3 Overview of autonomous learning
	4.3 Minimum disagreement labeling
		4.3.1 Label refinement
	4.4 Experimental analysis
		4.4.1 Evaluation methodology
		4.4.2 Accuracy of transferred labels
		4.4.3 Accuracy of activity recognition
		4.4.4 Precision, recall, and F1-measure
	4.5 Summary
	References
5 A distributed approach to energy-efficient data confidentiality in the Internet of Things
	5.1 Introduction
	5.2 Data confidentiality in the IoT
	5.3 A distributed computation approach
	5.4 Arduino-based experimental analysis
		5.4.1 Testbed setup
		5.4.2 Experimental measurements
			5.4.2.1 Energy measurements
			5.4.2.2 Lifetime increase: a single node's perspective
			5.4.2.3 Lifetime increase: a multi-hop network perspective
			5.4.2.4 Battery discharging profile
	5.5 Zolertia-based simulation analysis
		5.5.1 Simulator setup
		5.5.2 Simulation results
	5.6 Conclusions and future work
	References
6 An assessment of the efficiency of smart city facilities in developing countries: the case ofYaoundé, Cameroon
	6.1 Introduction
	6.2 Background
		6.2.1 Smart city concept
			6.2.1.1 Characteristics and dimensions of smart city
			6.2.1.2 Definitions
		6.2.2 Smart cities applications
			6.2.2.1 Social aspects
			6.2.2.2 Environmental aspects
			6.2.2.3 Administrative aspects
		6.2.3 Evaluation of smart city performance
	6.3 Case study: the city of Yaoundé, Cameroon
		6.3.1 Presentation of the city and its problems
		6.3.2 Solutions and role of ICTs
		6.3.3 Smart city project in Yaoundé
	6.4 Evaluation of Yaoundé's performance as smart city with the revised triple helix framework
	6.5 Conclusion, implications, and future directions
	References
7 A comparative study of software programming platforms for the Internet of Things
	7.1 Introduction
		7.1.1 Device connectivity cloud
	7.2 Overview of IOT platforms
	7.3 Comparisons of IoT platforms
		7.3.1 Cloud-level platforms
			7.3.1.1 Common features of cloud-level platforms
			7.3.1.2 Comparisons of cloud-level platforms
		7.3.2 Device-level platforms
			7.3.2.1 Common features of device-level platforms
			7.3.2.2 Comparisons of device-level platforms
		7.3.3 Radio-level platforms
			7.3.3.1 Common features of radio-level platforms
			7.3.3.2 Comparisons of radio-level platforms
	7.4 Programming models in practice
		7.4.1 Device abstraction
			7.4.1.1 Device-functionality abstraction
			7.4.1.2 Device-addressing abstraction
		7.4.2 Device discovery
			7.4.2.1 Registration-based device attachment
			7.4.2.2 Hub-based device discovery
			7.4.2.3 Device-to-device discovery
		7.4.3 Communication pattern
		7.4.4 Device control
			7.4.4.1 Device control model
			7.4.4.2 Group control method
	7.5 Challenges and future directions
		7.5.1 Challenge 1: Massive scaling
		7.5.2 Challenge 2: Device connectivity
		7.5.3 Challenge 3: Control conflict
		7.5.4 Challenge 4: Data consistency
		7.5.5 Challenge 5: Communication model
	7.6 Conclusion
	Acknowledgment
	References
8 Fog computing-based complex event processing for Internet of Things
	8.1 Fog computing
		8.1.1 Architecture of fog computing
		8.1.2 Related terms
		8.1.3 Characteristics of fog computing
		8.1.4 Service level objectives
			8.1.4.1 Computation management
			8.1.4.2 Latency management
			8.1.4.3 Resource management
			8.1.4.4 Energy management
			8.1.4.5 Reliability management
			8.1.4.6 Security and privacy management
			8.1.4.7 Mobility management
		8.1.5 Application areas
			8.1.5.1 Health-care systems
			8.1.5.2 Smart grid/city environment
			8.1.5.3 Vehicular networks/smart traffic lights
			8.1.5.4 Augmented reality
			8.1.5.5 Pre-caching
		8.1.6 Limitations and challenges
		8.1.7 Incorporating fog computing with emerging technologies
			8.1.7.1 Fifth generation
			8.1.7.2 Software-defined networking
			8.1.7.3 Network function virtualization
			8.1.7.4 Named data networking
			8.1.7.5 Content delivery network
	8.2 Complex event processing
		8.2.1 Basic definitions
		8.2.2 CEP reference architecture
			8.2.2.1 Design time
			8.2.2.2 Run time
			8.2.2.3 Administration
		8.2.3 Event detection models
		8.2.4 Event-processing languages
			8.2.4.1 Stream-oriented
			8.2.4.2 Rule-oriented
			8.2.4.3 Imperative
		8.2.5 Algorithms used in CEP
			8.2.5.1 Data volume
			8.2.5.2 Data continuity
			8.2.5.3 Data bound
			8.2.5.4 Data evolution
			8.2.5.5 Singular classifier approach
			8.2.5.6 Ensemble classifier approach
			8.2.5.7 Single-pass algorithms
			8.2.5.8 Windowing approaches
		8.2.6 Application areas
			8.2.6.1 Transportation and traffic management
			8.2.6.2 Health
			8.2.6.3 Smart building
			8.2.6.4 Smart grid/smart city
			8.2.6.5 Other domains
		8.2.7 Complex-event-processing challenges
		8.2.8 Trends and future directions in event processing
	8.3 An example scenario: smart city
	8.4 Conclusion
	References
9 Ultra-narrow-band for IoT
	9.1 Introduction
	9.2 UNB system
		9.2.1 UNB definition
		9.2.2 Topology: single cell design
	9.3 UNB interference characterization
	9.4 UNB-associated MAC
		9.4.1 Performance of CR-FDMA and DR-FDMA
		9.4.2 Throughput of CR-FTDMA
	9.5 UNB performances for same received power at the BS
		9.5.1 One transmission
		9.5.2 Multiple transmissions
	9.6 UNB performances for diverse received power at the BS
		9.6.1 Rectangular interference shape and stochastic geometry
		9.6.2 Exact interference shape
		9.6.3 Validation and comparison
		9.6.4 Network spectral efficiency
	9.7 Interference cancellation
	9.8 Conclusion
	References
10 Fog-computing architecture: survey and challenges
	10.1 Introduction
	10.2 Fog-computing architecture
		10.2.1 Existing research on fog-computing architecture
			10.2.1.1 Fog-layered architecture
			10.2.1.2 Hierarchical fog architecture
			10.2.1.3 OpenFog architecture
			10.2.1.4 Fog network architecture
			10.2.1.5 Fog architecture for Internet of Energy
			10.2.1.6 Fog-computing architecture based on nervous system
			10.2.1.7 IFCIoT architecture
		10.2.2 High-level fog-computing layered architecture
			10.2.2.1 Fog-computing layer
			10.2.2.2 Data-generation layer
			10.2.2.3 Cloud-computing layer
	10.3 Limitation of the cloud to execute Big Data applications
		10.3.1 Exploding generation of sensor data
		10.3.2 Inefficient use of network bandwidth
		10.3.3 Latency awareness
		10.3.4 Location awareness
	10.4 Challenges faced when executing Big Data applications on fog
		10.4.1 Resource limited fog device
		10.4.2 Power limitation
		10.4.3 Selection of master node
		10.4.4 Connectivity
	10.5 Recent advances on Big Data application execution on fog
	10.6 Fog-computing products
		10.6.1 Cisco IOx
		10.6.2 LocalGrid's fog-computing platform
		10.6.3 Fog device and gateways
	10.7 Research issues
	10.8 Conclusion
	References
11 A survey on outlier detection in Internet of Things big data
	11.1 Introduction
	11.2 Outliers-detection techniques
	11.3 Requirements and performance metrics
	11.4 Statistical-based techniques
		11.4.1 Parametric based
			11.4.1.1 Gaussian model based
			11.4.1.2 Regression model based
		11.4.2 Nonparametric based
			11.4.2.1 Histograms
			11.4.2.2 Kernel functions
	11.5 Machine learning
		11.5.1 Unsupervised learning
			11.5.1.1 Partitioning-clustering methods
			11.5.1.2 Hierarchical-clustering methods
			11.5.1.3 Grid-based clustering methods
			11.5.1.4 Density-based clustering methods
		11.5.2 Supervised learning
			11.5.2.1 Support vector machines (SVMs) methods
			11.5.2.2 Isolation-forest methods
			11.5.2.3 Mahalanobis-distance methods
	11.6 Distance-based techniques
		11.6.1 Local neighborhood
		11.6.2 k-Nearest neighbors
	11.7 Density-based techniques
		11.7.1 Local outlier factor
		11.7.2 Connectivity-based outlier factor
		11.7.3 INFLuenced outlierness
		11.7.4 Multi-granularity deviation factor
	11.8 Conclusion
	References
12 Supporting Big Data at the vehicular edge
	12.1 Introduction and motivation
	12.2 The Internet of Things
	12.3 Big data processing
	12.4 Cloud computing and the datacenter
	12.5 A survey of recent work on vehicular clouds
	12.6 Our contributions
	12.7 The vehicle datacenter model
	12.8 The vehicle datacenter simulation
		12.8.1 Datacenter controller
		12.8.2 Resource manager
		12.8.3 Job manager
		12.8.4 Log manager
		12.8.5 Network
		12.8.6 Vehicles
	12.9 Empirical performance evaluation
		12.9.1 Simulation factors
			12.9.1.1 Size of parking lot
			12.9.1.2 Residency time of vehicles
			12.9.1.3 Network configuration
			12.9.1.4 Network throughput
			12.9.1.5 Percentage of vehicles tasked
			12.9.1.6 Number of worker objects
			12.9.1.7 Number of simultaneous jobs
			12.9.1.8 Size of jobs
		12.9.2 Response variables
	12.10 Simulation results
		12.10.1 Correlation of job completion times
		12.10.2 Performance between random and set job sizes
	12.11 Concluding remarks
	12.12 Looking into the crystal ball
	References
13 Big data-oriented unit and ubiquitous Internet of Things (BD-U2IoT) security
	13.1 Introduction
	13.2 Unit and ubiquitous Internet of Things
		13.2.1 Storage and resource management in U2IoT
			13.2.1.1 Resource management in unit IoT
			13.2.1.2 Resource management in ubiquitous IoT
		13.2.2 Security in big data-oriented U2IoT
			13.2.2.1 Physical security
			13.2.2.2 Information security
			13.2.2.3 Management security
	References
14 Confluence of Big Data and Internet of Things—relationship, synergization, and convergence
	14.1 Introduction
	14.2 Anatomy of Big Data and IoT
		14.2.1 Big Data
		14.2.2 Internet of Things
	14.3 Relationship model
		14.3.1 Independent
		14.3.2 Interconnecting
		14.3.3 Interacting
		14.3.4 Intertwined
	14.4 Model pillars
		14.4.1 Difference, implementation, similarity, and capability
			14.4.1.1 Difference
			14.4.1.2 Implementation
			14.4.1.3 Similarities
			14.4.1.4 Capability
		14.4.2 Composition, realization, atomicity, and multiplicity
			14.4.2.1 Composition
			14.4.2.2 Realization
			14.4.2.3 Atomicity
			14.4.2.4 Multiplicity
		14.4.3 Control, association, range, and dependency
			14.4.3.1 Control
			14.4.3.2 Association
			14.4.3.3 Range
			14.4.3.4 Dependency
		14.4.4 Touchpoints, integration, mapping, and enablement
			14.4.4.1 Touchpoints
			14.4.4.2 Interplay
			14.4.4.3 Mapping
			14.4.4.4 Enablement
	14.5 Application of relationship model
		14.5.1 Independent pillar
		14.5.2 Interconnecting pillar
		14.5.3 Interacting pillar
		14.5.4 Intertwined pillar
		14.5.5 Putting it all together
			14.5.5.1 Stepwise maturity
			14.5.5.2 Hybrid stack
			14.5.5.3 Adoption process
			14.5.5.4 Native application
	14.6 Conclusion
	References
15 Application of Internet of Things and big data for sustainability in water
	15.1 Introduction
	15.2 Sustainability in water
		15.2.1 Source
		15.2.2 Treatment
		15.2.3 Reservoirs
		15.2.4 Consumption
		15.2.5 Wastewater
	15.3 IoT and BD system architecture
		15.3.1 IoT device
		15.3.2 Communication technology
		15.3.3 Internet
		15.3.4 Big data processing
	15.4 Application of IoT and BD in water sustainability
		15.4.1 Smart metering
		15.4.2 Leakage detection
		15.4.3 Water pollution
		15.4.4 Prediction and forecasting
	15.5 Challenges
		15.5.1 Cyber security
		15.5.2 Data accuracy
		15.5.3 Policy and regulations
		15.5.4 Technology interoperability
	15.6 Conclusion
	References
16 IoT-based smart transportation system under real-time environment
	16.1 Introduction
		16.1.1 Challenges
		16.1.2 Objective
	16.2 Recent trends in IoT application for the real-time transportation system
	16.3 Data acquisition
	16.4 Data processing
		16.4.1 Data analysis
	16.5 Existing works on IoT in the real-time transportation system
	16.6 Conclusion
	16.7 Future scope
	References
17 Edge computing: a future trend for IoT and big data processing
	17.1 Definition of edge computing
	17.2 Deployment scenarios
	17.3 Service scenarios
	17.4 Case studies
	17.5 Business values
	17.6 Challenges
	17.7 Discussion
		17.7.1 The difference between cloud computing and edge computing
		17.7.2 The role of edge computing
		17.7.3 Driving force
		17.7.4 Current state of edge computing
	17.8 Conclusion
	References
18 Edge computing-based architectures for big data-enabled IoT
	18.1 Introduction
		18.1.1 Cloud-computing architecture
			18.1.1.1 Mobile cloud computing
			18.1.1.2 Edge computing
		18.1.2 Mobile cloud computing applications
		18.1.3 Edge-computing applications
			18.1.3.1 Cloudlet computing
			18.1.3.2 Fog computing
			18.1.3.3 Mobile edge computing
	18.2 Challenges faced by edge computing
		18.2.1 Offloading decision challenges
		18.2.2 Interoperability challenges
		18.2.3 Safety and security challenges
		18.2.4 Performance optimization challenges
	18.3 Big data-enabled IoT requirements and challenges for IoT and smart cities
		18.3.1 Edge computing requirements
		18.3.2 Edge computing challenges
	18.4 Edge computing-based architecture for big data-enabled IoT
		18.4.1 Distributed EC-based approaches
		18.4.2 Centralized EC-based approaches
		18.4.3 Peer-to-peer EC-based approaches
		18.4.4 Hybrid EC-based approaches
	18.5 Comparative analysis of edge computing-based approaches
	18.6 Conclusion
	References
19 Information-centric trust management for big data-enabled IoT
	19.1 Introduction
	19.2 Overview of trust management
		19.2.1 Definitions of trust
			19.2.1.1 Trust in social psychology
			19.2.1.2 Trust in philosophy
		19.2.2 Semantics of trust
		19.2.3 Elements of trust
	19.3 Trust-management systems
		19.3.1 Overview
		19.3.2 Trust sources
		19.3.3 Trust methods
	19.4 Trust management for big data-enabled IoT
		19.4.1 Information-centric trust-management systems
		19.4.2 Challenges of information-centric trust
			19.4.2.1 Data processing
			19.4.2.2 Security and privacy
			19.4.2.3 Interoperability
		19.4.3 Requirements for trust in big data-enabled IoT
	19.5 Recent advancements in information-centric trust management in big data-enabled IoT
		19.5.1 Trusted data processing
			19.5.1.1 Data sensing and collection
			19.5.1.2 Data fusion and mining
			19.5.1.3 Data transmission and communication
		19.5.2 Security and privacy-enabled trust management
		19.5.3 Trust frameworks for interoperability
	19.6 Discussion and future research
		19.6.1 Anticipated challenges and research trends
	19.7 Conclusion
	References
20 Dependability analysis of IoT systems using dynamic fault trees analysis
	20.1 Introduction
	20.2 Background
		20.2.1 IoT security
		20.2.2 IoT dependability
		20.2.3 Fault tree analysis
	20.3 Methodology
	20.4 Case study
	20.5 Conclusion
	References
Index
Back Cover