Core Concepts
Utilizing Edge Intelligence and Digital Twins for Efficient Traffic Monitoring in Smart Cities.
Abstract
The content discusses the development of an Edge Intelligence-based Traffic Monitoring System (TMS) for smart cities. It highlights the importance of digital transformation, Artificial Intelligence (AI), and sensing techniques in enhancing Digital Twins (DTs) to collect and process data effectively. The paper presents a comprehensive approach focusing on Edge Intelligence (EI) to re-engineer large-scale distributed smart systems, emphasizing benefits like enhanced performance, reduced bandwidth consumption, and decreased latencies compared to cloud-centric approaches. The system architecture, hardware components, and software modules are optimized for efficient data collection, processing, and utilization.
Structure:
- Introduction to Urban Challenges & Digital Transformation
- Utilization of IoT & WSNs in Traffic Monitoring Applications
- Role of Digital Twins in Real-time Data Analysis & Optimization
- Proposed EI-based TMS Architecture & Components
- Comparative Analysis: Edge vs. Cloud Deployment Performance Evaluation
- Future Developments & Enhancements
Stats
"by 2030, nearly 28% of the world’s population will reside in cities with at least 1 million inhabitants"
"reducing traffic congestion by predicting driver intentions"
"transmission of 1,729,000 frames equates to 3.34Tb of data moving over the network per day"
Quotes
"Recent advancements in Artificial Intelligence (AI) and sensing techniques can elevate Digital Twins from digital copies of physical objects to effective platforms for data collection."
"Our approach prioritizes the placement of intelligence as close as possible to data sources."
"Moving computation to the edge provides benefits like enhanced inference performance and reduced latency."