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
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