Under several emerging application scenarios, such as in smart cities, operational
monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous …

Abstract Machine Learning (ML) and Artificial Intelligence (AI) depend on data sources to
train, improve, and make predictions through their algorithms. With the digital revolution and …

When processing IoT data on a large scale, the cloud is no longer sufficient and it has been
proposed to move parts of the computation closer to the IoT devices-the so-called fog …

The proliferation of Internet-of-Things (IoT) devices is rapidly increasing the demands for
efficient processing of low latency stream data generated close to the edge of the network …

In the long term, the Internet of Things (IoT) is expected to become an integral part of
people's daily lives. In light of this technological advancement, an ever-growing number of …

Abstract Rise of Big Data techniques has led to the requirement for low latency analysis of
high-velocity continuous data streams in real time. Several solutions, including Stream …

In this paper, we try to address the question" is it worth processing a data stream on the
device that collected it or should we send it somewhere else?". As it is often the case in …

Mobile devices are pervasive data producers that bridges users and emerging network
services, eg, learning techniques. Today, mobile devices are continuously generating user …

Abstract Stream Processing (SP), ie, the processing of data in motion, as soon as it becomes
available, is a hot topic in cloud computing. Various SP stacks exist today, with applications …

Stream processing answers the need for quickly developing and deploying applications for
real-time processing of continually created data. While its ability to handle a high volume of …