Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Sensors and Smart Devices at the Edge: IoT Meets Edge Computing

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 51095

Share This Special Issue

Special Issue Editors

Dr. Claudio Savaglio

E-Mail Website
Guest Editor

Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, Via Pietro Bucci, Arcavacata, 87036 Rende, CS, Italy
Interests: Internet of Things; development methodology; edge intelligence; multi-agent systems; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Salvatore Venticinque

E-Mail 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

E-Mail 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

E-Mail 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 Issues, Collections and Topics in MDPI journals

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 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

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 (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

22 pages, 3543 KiB

Open AccessArticle

Health-BlockEdge: Blockchain-Edge Framework for Reliable Low-Latency Digital Healthcare Applications

by Muneeb Ejaz, Tanesh Kumar, Ivana Kovacevic, Mika Ylianttila and Erkki Harjula

Sensors 2021, 21(7), 2502; https://doi.org/10.3390/s21072502 - 03 Apr 2021

Cited by 35 | Viewed by 4277

Abstract

The rapid evolution of technology allows the healthcare sector to adopt intelligent, context-aware, secure, and ubiquitous healthcare services. Together with the global trend of an aging population, it has become highly important to propose value-creating, yet cost-efficient digital solutions for healthcare systems. These solutions should provide effective means of healthcare services in both the hospital and home care scenarios. In this paper, we focused on the latter case, where the goal was to provide easy-to-use, reliable, and secure remote monitoring and aid for elderly persons at their home. We proposed a framework to integrate the capabilities of edge computing and blockchain technology to address some of the key requirements of smart remote healthcare systems, such as long operating times, low cost, resilience to network problems, security, and trust in highly dynamic network conditions. In order to assess the feasibility of our approach, we evaluated the performance of our framework in terms of latency, power consumption, network utilization, and computational load, compared to a scenario where no blockchain was used. Full article

(This article belongs to the Special Issue Sensors and Smart Devices at the Edge: IoT Meets Edge Computing)

► Show Figures

Figure 1

20 pages, 1172 KiB

Open AccessArticle

EDISON: An Edge-Native Method and Architecture for Distributed Interpolation

by Lauri Lovén, Tero Lähderanta, Leena Ruha, Ella Peltonen, Ilkka Launonen, Mikko J. Sillanpää, Jukka Riekki and Susanna Pirttikangas

Sensors 2021, 21(7), 2279; https://doi.org/10.3390/s21072279 - 24 Mar 2021

Cited by 7 | Viewed by 3468

Abstract

Spatio-temporal interpolation provides estimates of observations in unobserved locations and time slots. In smart cities, interpolation helps to provide a fine-grained contextual and situational understanding of the urban environment, in terms of both short-term (e.g., weather, air quality, traffic) or long term (e.g., crime, demographics) spatio-temporal phenomena. Various initiatives improve spatio-temporal interpolation results by including additional data sources such as vehicle-fitted sensors, mobile phones, or micro weather stations of, for example, smart homes. However, the underlying computing paradigm in such initiatives is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable, as when the spatial and temporal density of sensor data grows, the required transmission bandwidth and computational capacity become unfeasible. To address the scaling problem, we propose EDISON: algorithms for distributed learning and inference, and an edge-native architecture for distributing spatio-temporal interpolation models, their computations, and the observed data vertically and horizontally between device, edge and cloud layers. We demonstrate EDISON functionality in a controlled, simulated spatio-temporal setup with 1 M artificial data points. While the main motivation of EDISON is the distribution of the heavy computations, the results show that EDISON also provides an improvement over alternative approaches, reaching at best a 10% smaller RMSE than a global interpolation and 6% smaller RMSE than a baseline distributed approach. Full article

(This article belongs to the Special Issue Sensors and Smart Devices at the Edge: IoT Meets Edge Computing)

► Show Figures

Figure 1

28 pages, 18420 KiB

Open AccessArticle

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction

by I Nyoman Kusuma Wardana, Julian W. Gardner and Suhaib A. Fahmy

Sensors 2021, 21(4), 1064; https://doi.org/10.3390/s21041064 - 04 Feb 2021

Cited by 25 | Viewed by 3899

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 learning model for hourly PM_2.5 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 PM_2.5 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)

► Show Figures

Graphical abstract

20 pages, 571 KiB

Open AccessArticle

Joint Content Placement and Storage Allocation Based on Federated Learning in F-RANs

by Tuo Xiao, Taiping Cui, S. M. Riazul Islam and Qianbin Chen

Sensors 2021, 21(1), 215; https://doi.org/10.3390/s21010215 - 31 Dec 2020

Cited by 9 | Viewed by 2478

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 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)

► Show Figures

Figure 1

27 pages, 2797 KiB

Open AccessArticle

Detecting and Tracking Criminals in the Real World through an IoT-Based System

by Andrea Tundis, Humayun Kaleem and Max Mühlhäuser

Sensors 2020, 20(13), 3795; https://doi.org/10.3390/s20133795 - 07 Jul 2020

Cited by 14 | Viewed by 6658

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 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)

► Show Figures

Figure 1

Review

Jump to: Research

34 pages, 4149 KiB

Open AccessEditor’s ChoiceReview

Edge Machine Learning for AI-Enabled IoT Devices: A Review

by Massimo Merenda, Carlo Porcaro and Demetrio Iero

Sensors 2020, 20(9), 2533; https://doi.org/10.3390/s20092533 - 29 Apr 2020

Cited by 227 | Viewed by 29098

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 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)

► Show Figures

Journal Menu

Journal Browser

Sensors and Smart Devices at the Edge: IoT Meets Edge Computing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (6 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI