Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to
efficiently manage the automated operation of the internet of things (IoT) nodes deployed in …

Most existing causal structure learning algorithms must have access to the entire feature set
of a dataset during the learning process. However, in many real-world applications, rather …

In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the
presence of active primary users, as a large number of applications demand for more …

An approach to scalable joint source decoding in large-scale sensor networks, based on
Markov-random filed (MRF) modeling of the spatio-temporal correlation in the observations …

Due to the advancements in technology and microelectromechanical systems, there is an
exceptional development in the capabilities of sensors and smart devices. Nowadays …

There has been considerable interest in distributed source coding within the compression
and sensor network research communities in recent years, primarily due to its potential …

Canonical distributed quantization schemes do not scale to large sensor networks due to the
exponential decoder storage complexity that they entail. Prior efforts to tackle this issue have …

There has been considerable interest in distributed source coding (DSC) in recent years,
primarily due to its potential contributions to low-power sensor networks. However, two …

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to
efficiently manage the automated operation of the internet of things (IoT) nodes deployed in …

Motivated by potential applications in wireless sensor networks, we consider the problem of
communicating a large number of correlated analog sources over a Gaussian multiple …