Develop an edge computing solution for retail stores that analyzes customer movement and purchasing behavior in real time. The system will process video and sensor data locally to generate actionable insights, optimize store layout, and reduce cloud bandwidth usage.
Study retail analytics applications using edge computing technologies.
Research IoT sensors and camera-based tracking systems used in smart stores.
Design a distributed edge architecture for in-store data processing.
Implement people-counting and heatmap generation using lightweight computer vision models.
Develop algorithms to detect customer dwell time and shopping patterns.
Configure edge devices for local analytics and data filtering.
Implement privacy-preserving techniques by anonymizing facial data at the edge.
Create a dashboard to visualize foot traffic trends and peak hours.
Optimize bandwidth by transmitting only summarized behavioral insights to the cloud.
Compare latency between edge and cloud-based retail analytics.
Conduct testing in simulated store environments.
Document improvements in decision-making efficiency and cost savings.
