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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.

EdgeAI for Algorithmic Government

Chapter 1: Algorithmic Government

1.1 Concept of Algorithmic Government

1.2 Motivation & Benefits

1.3 Large Scale Decision Making

1.4 Computing Issues in Algorithmic Government

1.5 Technological Solutions

Chapter 2: Edge Computing

2.1 Concept of Edge Computing

2.2 Benefits of Edge Computing

2.4 AI for Edge Computing

2.5 Benefits of Edge Intelligence

Chapter 3: EdgeAI

3.1 Concept of EdgeAI

3.2 Levels of Edge Intelligence

3.3 Model Training at Edge

3.4 Model Inferencing at Edge

3.5 Comparative analysis of Model Training and Inferencing at Edge

4.1 Facial recognition for suspects at crowded places

4.2 Social Network Analysis for Citizen Behavior

4.3 Healthcare Management

4.4 Voice Enabled personal assistant

4.5 Industrial Safety and Planning

4.6 Border Security and Military planning

4.7 Citizen's safety and support

4.8 Summary of EdgeAI Techniques

Chapter 5: Design Challenges & Future Scope

5.1 Network Integration and Resource Management

5.2 Cloud & Edge Coexistence

5.3 Reliability of Edge Devices

5.4 Future Scope

5.5 Conclusion