CoIoTIA enables IoT devices to share micro-models and adapt to changing contexts, creating smarter, safer, and more efficient solutions.
Key Enablers of CoIoTIA
Last News
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Saadoon’s research stay at University of Cagliari
Our PhD student and CoIoTIA team member Sahibzada Saadoon Hammad is in Italy at the University of Cagliari for his research stay. Saadoon has been doing research on the application of TinyML for anomaly detection in environmental sensor networks. He has published and presented his work in various journals and conferences. He will spend three
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Paper Under Review: Detecting Anomalies in IoT Sensor Networks with Community-Based Models
Our work (under review) introduces a community-based approach for anomaly detection in IoT temperature sensors, grouping similar devices to share models and capture local patterns efficiently.
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CoIoTIA attends Sustainable Mobility Forum 26
In January 22 and 23, 2026, the Conference of Rectors of Universities in South-West Europe (Conferencia de Rectores de las Universidades del Suroeste Europeo – CRUSOE), partnering with SUMLAB research group, organised the 1st International Congress of Mobility and Intelligent Transport (Sustainable Mobility Forum 2026), held in the University of Cantabria in Santander, Spain. This
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Improving Indoor Positioning Through Specialized Wi-Fi Micromodels
What if indoor positioning didn’t require massive machine-learning systems? This post explores how Wi-Fi–based micromodels can outperform global models while using fewer resources.
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Work in progress: CoIoTIA main platform
The first prototype of the platform proposed by CoIoTIA project is here. Learn about its main features.
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Community-Based Anomaly Detection in IoT Sensor Networks
Can sensors learn from their neighbors? We grouped 43 temperature sensors in Castellón into communities based on their patterns and locations, then trained LSTM and MLP autoencoders to find out.
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Àngel Ruiz defends thesis on zone-based Graph Neural Networks for last-mile delivery
On July 24th, Àngel Ruiz defended his master thesis for the Master In Intelligent Systems. Its title is “Last mile routing with Graph Neural Network and Pointer Network: A comparison between global and zone-based training”, and it has been supervised by Dr. Sergi Trilles and Dr. Carlos Granell. The thesis tackles the last-mile routing use
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Saadoon presents IoT Anomaly Detection Work at DCAI Conference in Salamanca
GEOTEC member Sahibzada Saadoon Hammad attended the prestigious Conference on Distributed Computing and Artificial Intelligence (DCAI), held in Salamanca from June 26th to 28th. At this international conference, he presented his research titled “Anomaly Detection of Trust Management in Internet of Things Systems” during the Doctoral Consortium session. Hammad’s work is focused on enhancing the security and trustworthiness