Detecting and Tracking Criminals in the Real World through an IoT-Based System

Area of Science:

• Computer Science
• Criminology
• Internet of Things (IoT)

Background:

• Criminal activities pose significant prediction and management challenges for law enforcement.
• Current Police Force (PF) strategies often lack timeliness due to limited communication and citizen involvement in investigations.

Purpose of the Study:

• To propose an IoT social device-based system for real-world criminal detection and tracking.
• To enhance communication and collaboration between citizens and PFs in the criminal investigation process.
• To leverage Edge-based architecture for improved responsiveness, energy efficiency, and data processing.

Main Methods:

• Development of a system combining app-based technologies with an Edge-based architecture.
• Design of algorithms for criminal detection and tracking integrated into the system.
• Evaluation using a simulator for system logic and a user study with 30 participants for app functionality assessment.

Main Results:

• The system's logic was successfully demonstrated through simulation.
• User study confirmed the functionality and usability of the citizen-facing application.
• Comparative analysis indicated a reduction in intervention time compared to statistical benchmarks.

Conclusions:

• The proposed IoT system effectively supports criminal detection and tracking by fostering citizen-PF collaboration.
• Edge computing integration offers significant advantages in system responsiveness and efficiency.
• The system shows potential for improving law enforcement intervention times and overall effectiveness.