PRELIMS.pdf
Acknowledgements
Author biography
Shajulin Benedict
CH001.pdf
Chapter Edge intelligence
1.1 Edge computing
1.1.1 Objectives of edge computing
1.2 History of edge computing
1.3 Edge intelligence
1.3.1 Machine learning-based edge intelligence
1.3.2 Deep learning-based edge intelligence
1.3.3 Hype on edge intelligence
1.4 Advantages: edge intelligence
1.4.1 Low latency
1.4.2 Enhanced security
1.4.3 Bandwidth efficiency
1.5 Challenges: edge intelligence
1.6 Applications
1.6.1 Retail sector
1.6.2 Agriculture sector
1.6.3 Healthcare sector
1.6.4 Education sector
1.6.5 Industrial sector
1.6.6 Finance sector
1.6.7 Smart city sector
1.6.8 Transportation sector
1.6.9 Gaming sector
1.6.10 Personal sector
1.7 The need for this book
1.8 Potential readers
1.9 Organization of the book
References
CH002.pdf
Chapter Edge computing architectures
2.1 Internet of Things
2.1.1 Subdomains of the IoT
2.1.2 Service-level protocols
2.1.3 Communication-level protocols
2.2 IoT-enabled components
2.2.1 Sensors
2.2.2 Edge devices
2.2.3 Fog nodes
2.2.4 Cloud nodes
2.3 Learning techniques: machine learning
2.3.1 Machine learning: the history
2.3.2 Algorithmic views
2.4 Learning techniques: deep learning
2.5 Edge-AI architectures
2.5.1 Generic architecture
2.5.2 Layered architecture
2.5.3 Service oriented architecture
2.5.4 Interoperability architecture
2.5.5 Metric-oriented edge architecture
2.5.6 Open/closed architecture
2.5.7 Large/micro/macro-level architecture
2.5.8 Security-specific architecture
2.6 Conclusion
References
CH003.pdf
Chapter Edge OS and programming models
3.1 Operating systems
3.1.1 Hardware components
3.1.2 Functions of edge-based operating systems
3.2 Objectives of OS in edge devices (Edge OS)
3.3 Taxonomy of OS: toward edge
3.4 Edge OS: examples
3.4.1 QNX neutrino OS
3.4.2 FreeRTOS
3.4.3 Integrity OS
3.4.4 ThreadX
3.4.5 PikeOS
3.4.6 Chibi OS
3.4.7 Raspbian OS
3.4.8 TinyOS
3.4.9 Zephyr OS
3.4.10 Contiki-NG OS
3.4.11 mbed OS
3.4.12 eCoS OS
3.5 Process states in edge-OS
3.6 FreeRTOS: OS for embedded devices
3.6.1 FreeRTOS: ESP 32
3.6.2 Parallel tasks
3.7 Conclusion
References
CH004.pdf
Chapter Edge intelligence: learning techniques
4.1 Edge intelligence: a need
4.2 Federated learning
4.2.1 Federated learning architecture
4.2.2 Federated learning approaches
4.2.3 Federated learning challenges
4.3 DNN splitting
4.3.1 DNN splitting: architectural concepts
4.3.2 Types of split computing
4.3.3 Dynamic versus static
4.3.4 Advantages of split computing
4.3.5 Split computing: tools
4.4 Transfer learning
4.4.1 Traditional view
4.4.2 Transfer learning: pictorial representation
4.4.3 Transfer learning: stages
4.4.4 Transfer learning: types
4.4.5 Transfer learning: TensorFlow implementation
4.4.6 Edge-enabled transfer learning
4.5 Gossip learning
4.6 Conclusion
References
CH005.pdf
Chapter Inference/prediction techniques
5.1 Learning stages
5.2 Inference knowledge levels
5.3 Distributed inferences
5.3.1 Cooperative inference technique
5.3.2 Collaborative inference technique
5.3.3 Real-time inference technique
5.3.4 Metric-oriented distributed inferences
5.3.5 Scheduling resources/tasks
5.4 Distributed inferences: implementation strategies
5.4.1 Lightweight implementations
5.4.2 FPGA-based implementation
5.4.3 Clientβserver implementation
5.5 Interactive versus batched inferences
5.6 Partitioning distributed inferences
5.6.1 DNN inference partitioning
5.6.2 Real-time partitioning
5.6.3 Horizontal partitioning
5.6.4 Vertical partitioning
5.6.5 Stochastic partitioning
5.7 Accelerating distributed inferences: strategies
5.7.1 Hardware and software levels
5.7.2 Knowledge distillation
5.7.3 Model parallelism approach
5.7.4 Adaptive inference approach
5.7.5 Model compression
5.7.6 Caching inferences
5.7.7 Offloading inferences
5.8 Conclusion
References
CH006.pdf
Chapter Edge resources and accelerators
6.1 Edge resources: basics
6.1.1 Microprocessors
6.1.2 Microcontrollers
6.1.3 Differences
6.2 Micro-controller-level devices: an edge?
6.2.1 An edge?
6.2.2 Importance of Arduino
6.2.3 Arduino families
6.2.4 Arduino UNO board
6.2.5 Arduino programming
6.3 Micro-processor-level devices: general purpose
6.3.1 Raspberry Pi: an example
6.4 GPUs, TPUs, and FPGAs: special purpose
6.5 SoC, SoM, system-on-board
6.6 Edge accelerators
6.6.1 Low-power
6.6.2 Compute-intensive
6.6.3 Memory-intensive
6.6.4 Improved bandwidth
6.6.5 Extensible AI framework
6.6.6 Real-time assistance
6.7 Commercial edge accelerators
6.7.1 Intel-based edge accelerators
6.7.2 NVIDIA-based edge accelerators
6.7.3 Samsung-based accelerators
6.8 Examples and use-cases
6.8.1 Accelerators for CNNs
6.8.2 Accelerators for audio
6.9 Conclusion
References
CH007.pdf
Chapter Performance analysis of edge-enabled applications
7.1 Performance concerns
7.2 Model-specific performance concerns
7.3 Architecture-specific performance concerns
7.3.1 Performance concerns: hardware-dependent
7.3.2 Performance concerns: software-dependent
7.3.3 Performance concerns: integration-related
7.4 Algorithm-specific performance concerns
7.4.1 Compiler-specific refinements
7.4.2 Tuning applications
7.4.3 Selective packages
7.4.4 Programming models
7.5 Data-specific performance concerns
7.6 Performance monitoring: a need
7.7 Performance monitoring: metrics
7.8 Energy-efficiency methods
7.8.1 Energy measurements
7.8.2 Energy efficiency methods
7.9 Carbon efficiency methods
7.10 Workload scheduling and performance impacts
7.11 Performance monitoring tools
7.11.1 Tracing approach
7.11.2 Sandboxing approach
7.11.3 Modeling approach
7.11.4 Simulation approach
7.11.5 Online approach
7.12 Cloud/fog/edge-level performance monitoring
7.13 Conclusion
References
CH008.pdf
Chapter Security in edge-AI systems
8.1 Existing security challenges
8.2 Security attacks in edge-AI
8.2.1 Physical attacks
8.2.2 DDoS attacks
8.2.3 Side channel attack
8.2.4 Malware-injection attack
8.2.5 Authentication/authorization attack
8.2.6 Jamming attack
8.2.7 Forgery attack
8.2.8 Passive security attacks
8.3 Data-specific vulnerabilities
8.3.1 Data integrity-related issues
8.3.2 Data confidentiality-related issues
8.3.3 Data availability-related issues
8.3.4 Configuration mismatches
8.4 Security architectures in edge-AI
8.4.1 Security architecture: a layered perspective
8.4.2 Security architectures: user-centric
8.4.3 Security architectures: device-centric
8.4.4 Security architectures: application-specific
8.4.5 Deployment-specific security architectures
8.5 Preventing security breaches: strategies
8.5.1 Protocol-specific assistance
8.5.2 Reliable routing protocols
8.5.3 Combination of lightweight and computationally-intensive implementations
8.5.4 Firmware-level updates
8.5.5 Prior security testing
8.5.6 Encryption approaches
8.5.7 Newer technologies
8.6 Tools and solutions
8.6.1 FireEye
8.6.2 PaloAlto networks
8.6.3 Symantec solutions
8.6.4 Security: federated systems
8.7 Conclusion
References
CH009.pdf
Chapter Frameworks: edge-AI platforms
9.1 Essential characteristics
9.2 Types of framework
9.3 Resource-allocation frameworks
9.3.1 Real-time resource allocation framework
9.3.2 Cooperative resource allocation framework
9.3.3 Collaborative resource allocation framework
9.3.4 QoS-aware resource allocation framework
9.3.5 Optimal resource allocation framework
9.3.6 End-to-end resource allocation framework
9.4 Cloud-specific frameworks
9.4.1 Microsoft Azure-based frameworks
9.4.2 Google-based edge-AI frameworks
9.4.3 Amazon edge
9.5 Application-specific frameworks
9.5.1 Edge-AI in agriculture
9.6 Distributed federated learning frameworks
9.6.1 Federated learning
9.6.2 SplitFed
9.6.3 FedAdapt
9.6.4 FedLesScan
9.6.5 FATE framework
9.6.6 OpenFL framework
9.6.7 Tensorflow federated
9.6.8 FLAME tool
9.7 Conclusion
References
CH010.pdf
Chapter Orchestration platforms: computing continuum
10.1 Orchestration and integration
10.1.1 Characteristics
10.1.2 Approaches
10.2 Algorithmic/application orchestration
10.3 Workload orchestration
10.4 Hierarchical versus non-hierarchical orchestration
10.5 Adaptiveness in orchestration
10.6 Automation in orchestration
10.7 Metric-oriented orchestration
10.8 Orchestration frameworks
10.8.1 Oakestra framework
10.8.2 ORCH framework
10.8.3 Node-RED framework
10.8.4 KubeEdge framework
10.8.5 Tiny-MLOps framework
10.8.6 Pipeline framework
10.9 Integration platforms
10.10 Conclusion
References
CH011.pdf
Chapter Edge-AI applications
11.1 Applications
11.2 Edge-AI for healthcare
11.2.1 Remote healthcare monitoring and prediction
11.2.2 Personalized medicine
11.2.3 Stress release systems
11.3 Industrial applications using edge-AI
11.3.1 Predictive maintenance
11.3.2 Digital twin
11.3.3 Connected factories
11.3.4 Smart tools
11.3.5 Optimized productivity
11.3.6 Interactive production
11.4 Edge-AI for agriculture
11.4.1 Vegetation-related agriculture
11.4.2 Animal-related farming
11.4.3 Use-case: elephant emotion detection framework using deep learning
11.5 Edge-AI for forensics
11.5.1 Forensic applications
11.5.2 Edge-AI requirements
11.5.3 Edge-AI forensics: stages
11.6 Edge-AI for mobility/logistics
11.6.1 Smart mobility
11.6.2 Shared mobility intelligence
11.6.3 Logistics planning
11.6.4 Product placement
11.7 Conclusion
References
CH012.pdf
Chapter Business opportunities using edge-AI
12.1 Digital business
12.2 Economic impacting factors
12.2.1 Cloud/edge-specific costs
12.2.2 Memory-related cost improvements
12.3 Business opportunities: edge-AI platforms
12.3.1 Edge AI software: business opportunities
12.3.2 Edge AI hardware: business opportunities
12.4 Cost models
12.4.1 Cloud-based cost models
12.4.2 Serverless functions: costs
12.5 Economic simulators for FaaS implementation
12.5.1 Request-making component
12.5.2 Queues
12.5.3 Function instances
12.5.4 Monitoring server
12.6 Conclusion
References
CH013.pdf
Chapter Challenges and future directions
13.1 Edge-AI challenges
13.1.1 Energy consumption issues
13.1.2 Resource constraints
13.1.3 Performance efficiency
13.1.4 Secured learning
13.1.5 Legal and standards