PROFALI, an advanced evolution of the WHIP platform

Challenges

The PROFAILI project aims to optimise the WHIP platform developed by partner TEKNEI, used by Law Enforcement Agencies for monitoring wireless devices, by incorporating artificial intelligence technologies and distributed computing through specialised hardware. This technical advancement expands the system’s capacity to identify and analyse the presence, movement, and connections between devices in complex environments.

Objectives

• Develop machine learning models capable of generating behavioural patterns from the correlation of multiple wireless data sources.
• Apply filtering techniques to distinguish relevant data from noise, improving the quality of processed information.
• Implement device identification mechanisms based on SSIDs and random MAC addresses to trace routes and relationships between devices.
• Integrate analysis algorithms into neuromorphic hardware to enable edge computing with low power consumption and fast response times.
• Design intuitive visualisation tools to support analysis by non-specialist users.

Solution

The proposed solution is an advanced evolution of the WHIP platform, combining artificial intelligence, data analysis, and neuromorphic computing. Machine learning models are being developed to detect behavioural patterns from data captured by wireless sensors, capable of identifying suspicious or recurring elements across different space-time contexts. Intelligent filters are also being designed to separate noise from useful data, enhancing system accuracy. The project also addresses the analysis of SSIDs and random MAC addresses using clustering and semantic recognition techniques, enabling the inference of device paths. Finally, the adaptation of algorithms for execution on custom hardware boards with neuromorphic chips is being explored, allowing decentralised, real-time analysis.