Machine Learning for the Internet of Things: Challenges, Solutions and Future Directions

Dear Colleagues,

It is our pleasure to announce the opening of a new Special Issue in Sensors. The main topic of this Special Issue is machine learning on the edge-to-cloud continuum.

In the last few years, huge volumes of data have been generated by several sources, such as sensors, cameras, smart meters, mobile devices, and wearables, which are commonly referred to as Internet of Things (IoT) devices. Such huge volumes of data, coupled with the speed with which they are generated, pose new research challenges in regard to collecting, storing and analyzing them. To efficiently extract useful information and produce helpful knowledge for science, industry and public services, novel technologies, architectures and algorithms have been developed to capture and analyze these data.

In most cases, the applications used today for processing data from IoT devices are highly centralized and leverage cloud platforms to perform the main operations involving data collection, storing, processing and analysis. However, using only the cloud could generate significant inefficiencies in terms of network traffic, latency times and energy consumption. These issues become particularly critical for some kinds of applications, such as those in the medical and security fields, where it is essential to have low-latency response times to avoid serious problems such as fatal accidents.

Cooperation between cloud and edge devices becomes even more necessary when running applications that make use of machine learning algorithms, which usually require the availability of large computing resources, big data sets and long computation times for training models. In fact, IoT devices at the edge of the network usually are very limited in terms of computational resources, power supply, storage capacity and bandwidth, which makes them unsuitable to fully perform heavy learning tasks. For these reasons, several research efforts have been made for enabling machine learning algorithms to exploit cooperative training and inference on local data available on edge devices. However, this task presents many open issues, mainly related to the heterogeneity of hardware, software and protocols of edge devices, which makes integration and management difficult. In addition, data security, privacy and communication efficiency are other critical aspects to be considered during the execution of machine learning tasks, as data must be transferred and shared between different network nodes to be processed and analyzed in parallel.

From this perspective, this Special Issue aims to contribute to the field by presenting the most relevant advances in this research area.

Dr. Fabrizio Marozzo
Dr. Loris Belcastro
Guest Editors

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