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Special Issue "Machine Learning for the Internet of Things: Challenges, Solutions and Future Directions"
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 30 June 2023 | Viewed by 726
Special Issue Editors
Interests: big data analysis; social media analysis; cloud computing; data mining; machine learning; Internet of Things
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Interests: cloud computing; social media and big data analysis; distributed knowledge discovery; data mining; Internet of Things
Special Issue Information
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- programming models and algorithms for edge-to-cloud environments
- systems for data processing on cloud platforms
- data analysis workflows for distributed environments
- distributed data analytics
- big data management in IoT environments
- simulation and emulation tools for IoT applications
- industrial Internet of Things applications
- IoT systems for supporting decision making in smart cities
- health monitoring systems based on IoT
- programming models and scalable algorithms for big data
- big data analytics and applications of IoT data
- applications of machine learning in big data
- cloud-based data mining applications
- libraries, algorithms, and applications for big social data analysis
- machine learning applications for IoT environments
- techniques for distributing workloads on the edge-to-cloud continuum
- real-time IoT data analysis using machine learning techniques
- edge-to-cloud continuum system for IoT environments
- distributed intelligence on the edge-to-cloud continuum
- simulation scenarios for IoT systems
- privacy preserving in edge-to-cloud environments