The evolution of wireless networks gravitates towards connected intelligence, a concept that
envisions seamless interconnectivity among humans, objects, and intelligence in a hyper …

Technology solutions must effectively balance economic growth, social equity, and
environmental integrity to achieve a sustainable society. Notably, although the Internet of …

As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming
increasingly crucial to tackle the growing complexity of cyber threats. While traditional ML …

The last decade has demonstrated an exponential growth in connected devices, particularly
at the network edge, and this marked tendency still foresees a increase of the number of …

Ensuring the structural integrity of steel construction is critical, necessitating effective
methods for detecting bolt defects. Traditional inspection methods are reliable but require …

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers
for disease identification and medical diagnostics. In the context of diabetes mellitus, the …

Federated learning has recently been an emerging learning paradigm for training deep
neural networks for activity recognition on resource-limited portable devices such as …

Traditional IoT topologies involve access and core networks that share a common edge. On
this edge, border routers and gateways are responsible for converting protocols at different …

Developing deep learning models on tiny devices (eg Microcontroller units, MCUs) has
attracted much attention in various embedded IoT applications. However, it is challenging to …

Advances in Tiny Machine Learning (TinyML) have bolstered the creation of smart industry
solutions, including smart agriculture, healthcare and smart cities. Whilst related research …