Mobile Clustering Scheme for Pedestrian Contact Tracing: The COVID-19 Case Study

Area of Science:

• Computer Science
• Ubiquitous Computing
• Smart Cities

Background:

• Monitoring pedestrian proximity is crucial for smart city applications like access control and emergency response.
• Existing methods like GPS, video surveillance, and RFID have limitations in cost, accuracy, or range for indoor environments.
• The COVID-19 pandemic highlighted the need for effective close-range pedestrian tracking to control virus propagation.

Purpose of the Study:

• To propose a novel mobile clustering scheme for efficient monitoring of close pedestrian encounters in smart cities.
• To address the limitations of existing technologies in terms of cost, deployment, and effectiveness.
• To enhance crowd management, safety, and public health initiatives through improved proximity tracking.

Main Methods:

• A mobile clustering scheme is proposed where selected pedestrians (Cluster Heads) collect data from nearby individuals (Cluster Members).
• This approach minimizes data transmissions to a central control post, reducing collision probability.
• The system focuses on tracking individuals within a few meters of each other.

Main Results:

• The proposed scheme significantly increases the success packet transmission probability.
• It effectively reduces both collision probability and idle slot probability compared to direct transmission methods.
• The system demonstrates improved performance in registering people in close contact.

Conclusions:

• The mobile clustering scheme offers an efficient and cost-effective solution for monitoring pedestrian proximity in smart cities.
• This technology can be applied to various scenarios, including access control, emergency management, and pandemic response.
• The findings suggest a promising approach for enhancing situational awareness and safety in densely populated urban environments.