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Sensors
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Signal Processing and AI in Sensor Networks and IoT
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Signal Processing and AI in Sensor Networks and IoT

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (25 September 2023) | Viewed by 18008

Special Issue Editors

Dr. Luca Vollero

E-Mail Website
Guest Editor
Computer Systems and Bioinformatics Lab, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
Interests: distributed systems; IoT; embedded systems design and firmware optimization; communication systems; DSP
Special Issues, Collections and Topics in MDPI journals
Dr. Mario Merone

E-Mail Website
Guest Editor
Computer Systems and Bioinformatics Lab, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
Interests: AI; machine learning; deep neural networks
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Sabatini

E-Mail Website
Guest Editor
Research Unit of Computer Systems and Bioinformatics, Department of Engineering, Campus Bio-Medico, University of Rome, Via Alvaro del Portillo 21, 00128 Rome, Italy
Interests: embedded systems; graph signal processing; electronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of algorithms with a low computational burden and increasingly high-performance hardware architectures have made it possible to apply complex processing routines, locally and in real-time, to data extracted from sensors. Such techniques, which heavily rely on AI logic, extend the scope of processing from simple denoising to the extraction of high-level information both in single sensors and irregular arrays of multispectral sensor sources.

This Special Issue aims to collect innovative and significant contributions on signal processing and AI applied to individual or cluster-organized sensory devices in contexts such as IoT applications.

Dr. Luca Vollero
Dr. Mario Merone
Dr. Anna Sabatini
Guest Editors

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 2600 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.

Keywords

  • sensor data processing
  • digital signal processing
  • IoT
  • embedded systems
  • tinyML
  • edge AI
  • time series analysis
  • deep neural networks
  • graph signal processing

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Published Papers (2 papers)

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15 pages, 684 KiB  
Article
A Practical Approach to the Analysis and Optimization of Neural Networks on Embedded Systems
by Mario Merone, Alessandro Graziosi, Valerio Lapadula, Lorenzo Petrosino, Onorato d’Angelis and Luca Vollero
Sensors 2022, 22(20), 7807; https://doi.org/10.3390/s22207807 - 14 Oct 2022
Cited by 3 | Viewed by 2140
Abstract
The exponential increase in internet data poses several challenges to cloud systems and data centers, such as scalability, power overheads, network load, and data security. To overcome these limitations, research is focusing on the development of edge computing systems, i.e., based on a [...] Read more.
The exponential increase in internet data poses several challenges to cloud systems and data centers, such as scalability, power overheads, network load, and data security. To overcome these limitations, research is focusing on the development of edge computing systems, i.e., based on a distributed computing model in which data processing occurs as close as possible to where the data are collected. Edge computing, indeed, mitigates the limitations of cloud computing, implementing artificial intelligence algorithms directly on the embedded devices enabling low latency responses without network overhead or high costs, and improving solution scalability. Today, the hardware improvements of the edge devices make them capable of performing, even if with some constraints, complex computations, such as those required by Deep Neural Networks. Nevertheless, to efficiently implement deep learning algorithms on devices with limited computing power, it is necessary to minimize the production time and to quickly identify, deploy, and, if necessary, optimize the best Neural Network solution. This study focuses on developing a universal method to identify and port the best Neural Network on an edge system, valid regardless of the device, Neural Network, and task typology. The method is based on three steps: a trade-off step to obtain the best Neural Network within different solutions under investigation; an optimization step to find the best configurations of parameters under different acceleration techniques; eventually, an explainability step using local interpretable model-agnostic explanations (LIME), which provides a global approach to quantify the goodness of the classifier decision criteria. We evaluated several MobileNets on the Fudan Shangai-Tech dataset to test the proposed approach. Full article
(This article belongs to the Special Issue Signal Processing and AI in Sensor Networks and IoT)
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Review

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24 pages, 783 KiB  
Review
Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and Future Directions
by Mohammed Ayalew Belay, Sindre Stenen Blakseth, Adil Rasheed and Pierluigi Salvo Rossi
Sensors 2023, 23(5), 2844; https://doi.org/10.3390/s23052844 - 6 Mar 2023
Cited by 21 | Viewed by 15137
Abstract
The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of [...] Read more.
The recent wave of digitalization is characterized by the widespread deployment of sensors in many different environments, e.g., multi-sensor systems represent a critical enabling technology towards full autonomy in industrial scenarios. Sensors usually produce vast amounts of unlabeled data in the form of multivariate time series that may capture normal conditions or anomalies. Multivariate Time Series Anomaly Detection (MTSAD), i.e., the ability to identify normal or irregular operative conditions of a system through the analysis of data from multiple sensors, is crucial in many fields. However, MTSAD is challenging due to the need for simultaneous analysis of temporal (intra-sensor) patterns and spatial (inter-sensor) dependencies. Unfortunately, labeling massive amounts of data is practically impossible in many real-world situations of interest (e.g., the reference ground truth may not be available or the amount of data may exceed labeling capabilities); therefore, robust unsupervised MTSAD is desirable. Recently, advanced techniques in machine learning and signal processing, including deep learning methods, have been developed for unsupervised MTSAD. In this article, we provide an extensive review of the current state of the art with a theoretical background about multivariate time-series anomaly detection. A detailed numerical evaluation of 13 promising algorithms on two publicly available multivariate time-series datasets is presented, with advantages and shortcomings highlighted. Full article
(This article belongs to the Special Issue Signal Processing and AI in Sensor Networks and IoT)
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