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Special Issue "Sensors and Smart Devices at the Edge: IoT Meets Edge Computing"

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

Deadline for manuscript submissions: 28 February 2021.

Special Issue Editors

Dr. Claudio Savaglio
Website
Guest Editor
CNR – National Research Council of Italy, Institute for High Performance Computing and Networking (ICAR) Via Pietro Bucci, 87036 Arcavacata, Rende CS, Italy
Interests: Internet of Things; development methodology; smart objects; multi-agent systems; wireless sensor networks
Prof. Dr. Salvatore Venticinque
Website
Guest Editor
Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, I-81031 Aversa (CE), Italy
Interests: Internet of things; cloud computing; multi-agent systems; smart grid; mobile computing
Prof. Dr. Atay Ozgovde
Website
Guest Editor
Department of Computer Engineering, Galatasaray University, 34349 Istanbul, Turkey
Interests: edge computing; ambient intelligence; internet of things; applied machine learning; Industry 4.0
Dr. Teemu Leppänen
Website
Guest Editor
Center for Ubiquitous Computing, University of Oulu, 90014 Oulu, Finland
Interests: Internet of things; edge computing; distributed artificial intelligence; multi-agent systems; ubiquitous computing

Special Issue Information

Dear Colleagues,

The rising demand for smart applications, as well as ballooning end-user expectations, have jointly focused huge industry investments and research interest on the network edge, with the staunch goal of unleashing the full potential of the internet of things (IoT) ecosystem. In order to establish a mutually beneficial, transparent, and inter-dependent continuum of (latency sensitive, reliable, cyber-physical, and opportunistic) IoT services, involving both heterogeneous devices and different stakeholders across different domains, the edge of things (EoT) has arisen as the seamless integration of the cloud, fog, and edge computing paradigms. Such promising synergy is still in its embryonic stage and, given the extensiveness and relative infancy of the research areas, several threats may lag EoT diffusion or overshadow its benefits. Indeed, the reliable and optimized management of processing and communication resources both at the device and infrastructural level, the dynamic trade-off between centralized and distributed approaches, and the overcoming of standardization barriers in the direction of full interoperability, are just some open issues which require novel approaches and advanced key techniques.

This Special Issue will investigate several topics under the EoT umbrella and welcomes paper submissions providing innovative research as well as technical contributions that foster the creation of a new value chain and an energized IoT ecosystem spanning from the network core to the edge.

Dr. Claudio Savaglio
Prof. Dr. Salvatore Venticinque
Prof. Dr. Atay Ozgovde
Dr. Teemu Leppänen
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 papers will be 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 2200 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

  • Internet of things ecosystems
  • Edge computing-oriented architectures, protocols and applications
  • Distributed artificial intelligence
  • Social, mobile, and secure smart objects
  • Big edge-of-things data

Published Papers (4 papers)

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Research

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Open AccessArticle
Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction
Sensors 2021, 21(4), 1064; https://doi.org/10.3390/s21041064 - 04 Feb 2021
Abstract
Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep [...] Read more.
Accurate air quality monitoring requires processing of multi-dimensional, multi-location sensor data, which has previously been considered in centralised machine learning models. These are often unsuitable for resource-constrained edge devices. In this article, we address this challenge by: (1) designing a novel hybrid deep learning model for hourly PM2.5 pollutant prediction; (2) optimising the obtained model for edge devices; and (3) examining model performance running on the edge devices in terms of both accuracy and latency. The hybrid deep learning model in this work comprises a 1D Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) to predict hourly PM2.5 concentration. The results show that our proposed model outperforms other deep learning models, evaluated by calculating RMSE and MAE errors. The proposed model was optimised for edge devices, the Raspberry Pi 3 Model B+ (RPi3B+) and Raspberry Pi 4 Model B (RPi4B). This optimised model reduced file size to a quarter of the original, with further size reduction achieved by implementing different post-training quantisation. In total, 8272 hourly samples were continuously fed to the edge device, with the RPi4B executing the model twice as fast as the RPi3B+ in all quantisation modes. Full-integer quantisation produced the lowest execution time, with latencies of 2.19 s and 4.73 s for RPi4B and RPi3B+, respectively. Full article
(This article belongs to the Special Issue Sensors and Smart Devices at the Edge: IoT Meets Edge Computing)
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Open AccessArticle
Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs
Sensors 2021, 21(1), 215; https://doi.org/10.3390/s21010215 - 31 Dec 2020
Abstract
With the rapid development of mobile communication and the sharp increase of smart mobile devices, wireless data traffic has experienced explosive growth in recent years, thus injecting tremendous traffic into the network. Fog Radio Access Network (F-RAN) is a promising wireless network architecture [...] Read more.
With the rapid development of mobile communication and the sharp increase of smart mobile devices, wireless data traffic has experienced explosive growth in recent years, thus injecting tremendous traffic into the network. Fog Radio Access Network (F-RAN) is a promising wireless network architecture to accommodate the fast growing data traffic and improve the performance of network service. By deploying content caching in F-RAN, fast and repeatable data access can be achieved, which reduces network traffic and transmission latency. Due to the capacity limit of caches, it is essential to predict the popularity of the content and pre-cache them in edge nodes. In general, the classic prediction approaches require the gathering of users’ personal information at a central unit, giving rise to users’ privacy issues. In this paper, we propose an intelligent F-RANs framework based on federated learning (FL), which does not require gathering user data centrally on the server for training, so it can effectively ensure the privacy of users. In the work, federated learning is applied to user demand prediction, which can accurately predict the content popularity distribution in the network. In addition, to minimize the total traffic cost of the network in consideration of user content requests, we address the allocation of storage resources and content placement in the network as an integrated model and formulate it as an Integer Linear Programming (ILP) problem. Due to the high computational complexity of the ILP problem, two heuristic algorithms are designed to solve it. Simulation results show that the performance of our proposed algorithm is close to the optimal solution. Full article
(This article belongs to the Special Issue Sensors and Smart Devices at the Edge: IoT Meets Edge Computing)
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Open AccessArticle
Detecting and Tracking Criminals in the Real World through an IoT-Based System
Sensors 2020, 20(13), 3795; https://doi.org/10.3390/s20133795 - 07 Jul 2020
Cited by 1
Abstract
Criminals and related illegal activities represent problems that are neither trivial to predict nor easy to handle once they are identified. The Police Forces (PFs) typically base their strategies solely on their intra-communication, by neglecting the involvement of third parties, such as the [...] Read more.
Criminals and related illegal activities represent problems that are neither trivial to predict nor easy to handle once they are identified. The Police Forces (PFs) typically base their strategies solely on their intra-communication, by neglecting the involvement of third parties, such as the citizens, in the investigation chain which results in a lack of timeliness among the occurrence of the criminal event, its identification, and intervention. In this regard, a system based on IoT social devices, for supporting the detection and tracking of criminals in the real world, is proposed. It aims to enable the communication and collaboration between citizens and PFs in the criminal investigation process by combining app-based technologies and embracing the advantages of an Edge-based architecture in terms of responsiveness, energy saving, local data computation, and distribution, along with information sharing. The proposed model as well as the algorithms, defined on the top of it, have been evaluated through a simulator for showing the logic of the system functioning, whereas the functionality of the app was assessed through a user study conducted upon a group of 30 users. Finally, the additional advantage in terms of intervention time was compared to statistical results. Full article
(This article belongs to the Special Issue Sensors and Smart Devices at the Edge: IoT Meets Edge Computing)
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Review

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Open AccessReview
Edge Machine Learning for AI-Enabled IoT Devices: A Review
Sensors 2020, 20(9), 2533; https://doi.org/10.3390/s20092533 - 29 Apr 2020
Cited by 8
Abstract
In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be [...] Read more.
In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be based on machine learning models to decode meaning and behavior behind sensors’ data, to implement accurate predictions and make decisions. The bottleneck will be the high level of connected things that could congest the network. Hence, the need to incorporate intelligence on end devices using machine learning algorithms. Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of this work is to provide a review of the main techniques that guarantee the execution of machine learning models on hardware with low performances in the Internet of Things paradigm, paving the way to the Internet of Conscious Things. In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is presented, with the main goal to define the state of the art and envisioning development requirements. Furthermore, an example of edge machine learning implementation on a microcontroller will be provided, commonly regarded as the machine learning “Hello World”. Full article
(This article belongs to the Special Issue Sensors and Smart Devices at the Edge: IoT Meets Edge Computing)
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