Predictive Edge Analytics for Sanitary Facility Monitoring

Project Overview

Develops distributed analytics that can move between edge, fog, and cloud nodes so hygiene services operate reliably without constant connectivity.

Sensors in remote sanitary facilities traditionally stream all data to the cloud, incurring bandwidth cost, latency, and outage risks. This project designs a context-aware architecture that dynamically orchestrates analytics across on-device, edge, and cloud resources so only summarised insights travel upstream.

The research reviews existing anomaly-detection approaches, builds self-organising orchestration algorithms, and evaluates how the flexible pipeline improves service quality while reducing dependency on mobile networks.

Team

Dominic Fonseca

Omer Rana

Nick Williams

Charith Perera

Funding

KESS 2 East Knowledge Economy Skills Scholarships pairs Welsh industry organisations with university researchers.

iPoint Provides transport and hygiene facility management solutions across multiple data platforms.

Partners

iPoint