EdgeAI for Algorithmic Government

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The book provides various EdgeAI concepts related to its architecture, key performance indicators, and enabling technologies after introducing algorithmic government, large-scale decision-making, and computing issues in the cloud and fog. With advancements in technology, artificial intelligence has permeated our personal lives and the fields of economy, socio-culture, and politics. The integration of artificial intelligence (AI) into decision-making for public services is changing how governments operate worldwide. This book discusses how algorithms help the government in various ways, including virtual assistants for busy civil servants, automated public services, and algorithmic decision-making processes. In such cases, the implementation of algorithms will occur on a massive scale and possibly affect the lives of entire communities. The cloud-centric architecture of artificial intelligence brings out challenges of latency, overhead communication, and significant privacy risks. Due to the sheer volume of data generated by IoT devices, the data analysis must be performed at the forefront of the network. This introduces the need for edge computing in algorithmic government. EdgeAI, the confluence of edge computing and AI, is the primary focus of this book. It also discusses how one can incorporate these concepts in algorithmic government through conceptual framework and decision points. Finally, the research work emphasizes some design challenges in edge computing from applications viewpoint. This book will be helpful for data engineers, data scientists, cloud engineers, data management experts, public policymakers, administrators, research scholars and academicians.


Author(s): Rajan Gupta, Sanjana Das, Saibal Kumar Pal
Publisher: Palgrave Pivot
Year: 2023

Language: English
Pages: 108
City: Singapore

Preface
Acknowledgements
Contents
About the Authors
Abbreviations
List of Figures
List of Tables
1 Algorithmic Government
1.1 Background
1.2 Motivation and Benefits
1.3 Large-Scale Decision-Making
1.4 Implementation of AI in LSDM
1.5 Computing Issues with Algorithmic Government
1.6 Summary
References
2 Edge Computing
2.1 Emergence of Edge Computing
2.2 Application of Edge Computing
2.3 Comparative Analysis of Cloud, Fog, and Edge Computing
2.4 AI Techniques for Edge Computing
Regression Models
Classification Models
Clustering Models
Deep Learning and Neural Networks
2.5 Summary
References
3 EdgeAI: Concept and Architecture
3.1 Concept
3.2 EdgeAI Approaches
3.3 Architecture of Edge Intelligence
Training Specific Architectures
Inference Specific Architectures
3.4 Evaluating AI Model Workflow at Edge
3.5 Enabling Technologies for Improving KPIs
Enabling Technologies for Model Training
Enabling Technologies for Model Inference
3.6 Comparative Analysis of EI Model Training and Inferencing at Edge
Architectures
Key Performance Indicators
Enabling Technologies
3.7 Summary
References
4 EdgeAI Use Cases for Algorithmic Government
4.1 Facial Recognition for Suspects at Public Places
4.2 Social Network Analysis (SNA) for Analyzing Citizen Behavior
4.3 AI in Healthcare
4.4 Voice Enabled AI-Based Personal Assistants
4.5 Industrial Safety Through Cameras and Sensors
4.6 EdgeAI for Border Security and Military Planning
4.7 Identifying Citizens Who Can be Victimized
4.8 Summary
5 Implications and Future Scope
5.1 Conceptual Framework
5.2 Challenges in Edge Computing: Network Integration and Resource Management
5.3 Challenges in Edge Computing: Cloud and Edge Coexistence
5.4 Challenges in Edge Computing: Reliability of Edge Devices
5.5 Ethical Issues in EdgeAI
5.6 Technological Implications
5.7 Emerging Hardware and Frameworks for EdgeAI Specific Applications
AI Accelerators, Field Programmable Gate Arrays, and Devices
5.8 Summary
5.9 Conclusion
References
Bibliography
Index