A fast and scalable crowd sensing based trajectory tracking system

Crowd Sensing collects users' local knowledge such as local information, ambient context, and traffic conditions, etc., using their sensor-enabled devices. The collected information is further aggregated and transferred to the cloud for detailed analysis, such as places / friends recommendation, human behavior, criminal activities, etc. These tracking and monitoring systems must be scalable, fast, and easy to deploy to meet the requirements of a real-time system. In this paper, we propose a fast and scalable crowdsensing based trajectory tracking system which can track any person having the smartphone and can provide a complete analysis of her visited locations in a given time span. We use the Redis in-memory database and XMPP at the sensing units for fast data retrieval and exchange. When a person moves to a new location, WebSocket server updates that person's new location automatically among all sensing units to make the system analysis in real-time. We develop and deploy a real prototype testbed in IIT Roorkee campus and evaluate it extensively to demonstrate the efficiency and scalability of our proposed system.