2.6 Structure of edgeOS in the smart home environment [3].Figure 2.7 Smart Traffic light system.Figure 2.8 Smart pipeline monitoring system architecture.
3 Chapter 3Figure 3.1 Illustration of differences between training and test images of t...Figure 3.2 Illustration of data sharing mechanism.Figure 3.3 Illustration of intermediate results of a DNN model. The size of ...
4 Chapter 4Figure 4.1 CCN router components.Figure 4.2 Tunnel-based redirection (TBR) scheme.
5 Chapter 5Figure 5.1 Fog computing enabled smart cities. (See color plate section for ...Figure 5.2 A generic fog enabled IoT environment. (See color plate section f...Figure 5.3 Internet of Things security phenomenon.Figure 5.4 Layered depiction of components of trust.Figure 5.5 Detailed taxonomy of threats.Figure 5.6 Semantic web technology stack by Tim Berners-Lee 2000 (http://w3c...Figure 5.7 With the appropriate sensors and wireless technology, several wir...Figure 5.8 Authentication methods taxonomy.Figure 5.9 Authorization frameworks and models.Figure 5.10 Taxonomy of the frameworks and models used for privacy.
6 Chapter 6Figure 6.1 Cloud-fog-IoT architecture. (See color plate section for the colo...Figure 6.2 Our proposed architecture for cloud-fog-IoT integration.Figure 6.3 Taxonomy for the classification of the communication layer.Figure 6.4 Taxonomy for the classification of the security and privacy layer...Figure 6.5 Taxonomy for the classification of the Internet of Things layer....Figure 6.6 Taxonomy for the classification of the data quality layer.Figure 6.7 Taxonomy for the classification of the cloudification layer.Figure 6.8 Taxonomy for the classification of the analytics and decision-mak...
7 Chapter 7Figure 7.1 The Computing Continuum: Cyberinfrastructure that spans every sca...Figure 7.2 Continuum Computing Research Areas: A pictorial depiction of the ...Figure 7.3 Continuum mapping and execution: Research is needed to explore te...
8 Chapter 8Figure 8.1 Fog system.Figure 8.2 Energy receiver.Figure 8.3 Time slot.
9 Chapter 9Figure 9.1 PPG sensors consisting of two light sources and one light sensor....Figure 9.2 Power spectral density (PSD) of one-minute PPG signal.Figure 9.3 PPG waveforms and the four features extracted for SpO2 calculatio...Figure 9.4 IoT system architecture.Figure 9.5 The high level system architecture.Figure 9.6 Modeling accuracy of measurements (e.g. ɛ(X, U)) in PP...Figure 9.7 Optimization algorithm implementation.Figure 9.8 Markov chain of battery states during charging and discharging.Figure 9.9 Markov chain of activities of an individual during one period.Figure 9.10 Markov chain of joint battery and activity states during period ...Figure 9.11 24-hour health monitoring of a healthy person. (a) User's activi...Figure 9.12 Average probability of error as a function of energy consumption...
10 Chapter 10Figure 10.1 An illustration of a typical fog network.Figure 10.2 An illustration of the problem's challenges.Figure 10.3 An illustration of the min-cost transformation.Figure 10.4 An illustration of latency updating procedure: (a) demand collec...Figure 10.5 The dynamic programming in the line topology.Figure 10.6 An illustration of the greedy algorithm: (a) greedy, (b) optimal...Figure 10.7 Server distribution.Figure 10.8 Latency-distance mapping: (a) greedy, (b) optimal.Figure 10.9 Performance comparison without data replication.Figure 10.10 Performance comparison with data replication.
11 Chapter 11Figure 11.1 A comparison of different simulators for fog computing environme...Figure 11.2 FogNetSim++ high-level architecture.Figure 11.3 FogNetSim++: Graphical user interface, showing static, mobile, a...Figure 11.4 FogNetSim++: showing the handover features managed through singl...Figure 11.5 FogNetSim++: Internal structure of broker node.Figure 11.6 GUI – Sample fog simulation.
12 Chapter 12Figure 12.1 Three-tier architecture for IoT (a) and two-tier cloud assisted ...Figure 12.2 IoT Data Challenges in three dimensions: generation, transmissio...Figure 12.3 A multilayer feed-forward neural network.Figure 12.4 Traditional machine learning (a) and deep learning (b) approache...Figure 12.5 Typical CNN architecture [25].Figure 12.6 OpenMV, a machine vision kit for IoT developers.Figure 12.7 NCS based on Intel Movidius Myriad 2 Vision Processing Unit (VPU...Figure 12.8 Fog topology with devices grouped by levels.Figure 12.9 Multilevel data fusion for video processing.
13 Chapter 13Figure 13.1 Comparison between the Smart Grid and the traditional grid.Figure 13.2 Time triggering of the major functions used in DMS.Figure 13.3 Feeder-based communication scheme for DMS using fog/cloud comput...Figure 13.4 Simplified communication scheme connecting MATLAB/Simulink and T...Figure 13.5 Distribution feeder topology with ThingSpeak channel assignments...Figure 13.6 Simulation test 1 – Three-phase tripping of network branch 571–6...Figure 13.7 Three-phase voltage profile of distribution feeder downstream fr...Figure 13.8 Simulation test 2 – “Heavy” loading of the distribution feeder, ...Figure 13.9 Three-phase voltage profile of distribution feeder downstream fr...
14 Chapter 14Figure 14.1 Our proposed three-tier IoT system architecture.Figure 14.2 Line chat with average value markers of different CCR values.Figure 14.3 100% stack column chat of different CCR values.
15 Chapter 15Figure 15.1 The process of developing location-independent MET estimation mo...Figure 15.2 The result of the cross-correlation function on magnitude (a) an...Figure 15.3 Three smart phones are placed on three different locations of ea...Figure 15.4 Performance comparison of the proposed transfer learning approac...Figure 15.5 Performance comparison of the proposed transfer learning approac...
16 Chapter 16Figure 16.1 Architecture of software-defined network (SDN). (See color plate...Figure 16.2 An OpenFlow switch communicate with controller over a secure con...Figure 16.3 Architecture of SDN-based wireless mesh network.Figure 16.4 Architecture of SDN-enabled wireless sensor network.
17 Chapter 17Figure 17.1 Fog computing for vehicular applications.Figure 17.2 Obstacle detection as an example of delay-critical application s...Figure 17.3 Timeliness perturbations.Figure 17.4 Coping with perturbation in DCVF.
18 Chapter 18Figure 18.1 Cloud based architecture for the communication between autonomou...Figure 18.2 Proposed fog-based architecture for the communication among auto...Figure 18.3 Realistic and practical view of the proposed fog-based architect...Figure 18.4 Box plot for the latency for both fog-basedarchitecture and clou...Figure 18.5 Box plot for network usage for both fog-based architecture and c...
19 Chapter 19Figure 19.1 Illustrative example of a visual data computing at network edges...Figure 19.2 Illustrative example of the function-centric fog/cloud computing...Figure 19.3 Illustrative example of the Panacea's Cloud setup: IoT device da...Figure 19.4 Overview of visual data processing stages in a facial recognitio...Figure 19.5 Illustrative example of a 3-D scene reconstruction with use of L...Figure 19.6 Overview of 3-D scene reconstruction stages with 2-D videos and ...Figure 19.7 Illustrative example of WAMI imagery ecosystem: tiled (TIFF) aer...Figure 19.8 Overview of object tracking stages in a typical WAMI analysis pi...Figure 19.9 Various physical obstacles including both man-made e.g. building...Figure 19.10 Joplin, MO satellite maps of Joplin Hospital (a, b) and Joplin ...Figure 19.11 To cope with deep learning functions complexity of the obstacle...Figure 19.12 Illustrative example of the augmented with metalinks physical n...Figure 19.13 System architecture of our Incident-Supporting Service Chain Or...
20 Chapter 20Figure 20.1 Evolution of wireless technologies.Figure 20.2 5G cellular architecture.Figure 20.3 5G IP-based architecture.Figure 20.4 Cloud-based architecture.Figure 20.5 Beam division multiple access (BDMA).Figure 20.6 Mixed bandwidth data path.Figure 20.7 Cellular network with the deployment of massive MIMO.Figure 20.8 Heterogeneous network.
21 Chapter 21Figure 21.1 Fog computing architecture tailored for bioinformatics sequencin...Figure 21.2 Fog computing architecture for bioinformatics applications.Figure 21.3 Fog computing for real time microorganism detection.
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