Effective Use of Heterogeneous Wireless Multimedia Sensor Networks for Surveillance Applications

This project focuses on the development of an efficient and scalable framework for Wireless Multimedia Sensor Networks (WMSNs), leveraging recent advances in embedded systems, low-power electronics, and multimedia sensing technologies. By integrating video, audio, and scalar sensor data, the proposed system aims to enhance surveillance and monitoring capabilities across a wide range of applications, including smart cities, healthcare, environmental monitoring, and intelligent transportation systems. The project addresses key limitations of existing approaches such as high energy consumption, bandwidth constraints, and quality of service (QoS) challenges by introducing a hierarchical and heterogeneous network architecture that combines cost-effective scalar sensor nodes with more capable multimedia sensor nodes.

The proposed framework incorporates novel machine learning–based event detection, efficient routing and clustering mechanisms, and edge-level data processing to optimize performance in terms of energy efficiency, latency, and detection accuracy. Two real-world case studies elderly health monitoring and wildfire detection are used to validate the approach, demonstrating its practical relevance and societal impact. By enabling accurate, real-time detection of critical events while minimizing data transmission and resource usage, the project contributes to the advancement of intelligent, multimodal sensing systems and supports more effective decision-making in safety-critical scenarios.