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Special Issue "Selected Papers from the Global IoT Summit GIoTS 2020"

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

Deadline for manuscript submissions: closed (15 February 2021).

Special Issue Editors

Prof. Dr. Antonio Skarmeta
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Guest Editor
Prof. Dr. Mirko Presser
E-Mail Website
Guest Editor
Department of Business Development and Technology, Aarhus University, Birk Centerpark 15, building 8001, Innovatorium, CBD 7400 Herning, Denmark
Interests: IoT; Industry 4.0; Business Model Innovation; Smart Cities; Urban Manufacturing
Special Issues and Collections in MDPI journals
Dr. Sébastien Ziegler
E-Mail Website
Guest Editor
Mandat International, International Cooperation Foundation and IoT Lab, 3 ch. du Champ-Baron, 1209 Geneva, Switzerland
Special Issues and Collections in MDPI journals
Prof. Dr. Srdjan Krčo
E-Mail Website
Guest Editor
DunavNET d.o.o., A. Čehova 1, 21000 Novi Sad, Serbia
Interests: IoT; smart agriculture/cities/manufacturing; digital transformations; business models
Special Issues and Collections in MDPI journals
Mr. Soumya Kanti Datta
E-Mail Website
Guest Editor
EURECOM, Campus SophiaTech, 450 route des Chappes, Biot 06410, France
Mr. Latif Ladid
E-Mail Website
Guest Editor
University of Luxembourg, Maison du Nombre, 6, avenue de la Fonte, L-4364 Esch-sur-Alzette, Luxembourg
Interests: IPv6; IoT; 5G
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The 2020 Global IoT Summit (GIoTS) http://www.globaliotsummit.org/ seeks contributions on how to nurture and cultivate IoT technologies and applications for the benefit of society.

The aim of this Special Issue is to include selected papers from the 2020 Global IoT Summit (GIoTS) describing researchers from both academia and industry and technical presentations on the recent advances in theory, application, and implementation of the Internet of Things concepts and IoT technologies and applications. Papers should be original and should emphasize current topics relevant to the IoT community on the latest research, engineering, standards, and business issues.

Prof. Dr. Antonio Fernando Skarmeta Gómez
Prof. Dr. Mirko Presser
Dr. Sébastien Ziegler
Prof. Dr. Srdjan Krčo
Mr. Soumya Kanti Datta
Mr. Latif Ladid
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

  • IoT Enabling Technologies
  • IoT Applications, Services and Real Implementations
  • IoT Multimedia, Societal Impacts and Sustainable Development
  • Security and Privacy for Internet of Things

Published Papers (8 papers)

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Research

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Open AccessArticle
Smart SDN Management of Fog Services to Optimize QoS and Energy
Sensors 2021, 21(9), 3105; https://doi.org/10.3390/s21093105 - 29 Apr 2021
Viewed by 292
Abstract
The short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are [...] Read more.
The short latency required by IoT devices that need to access specific services have led to the development of Fog architectures that can serve as a useful intermediary between IoT systems and the Cloud. However, the massive numbers of IoT devices that are being deployed raise concerns about the power consumption of such systems as the number of IoT devices and Fog servers increase. Thus, in this paper, we describe a software-defined network (SDN)-based control scheme for client–server interaction that constantly measures ongoing client–server response times and estimates network power consumption, in order to select connection paths that minimize a composite goal function, including both QoS and power consumption. The approach using reinforcement learning with neural networks has been implemented in a test-bed and is detailed in this paper. Experiments are presented that show the effectiveness of our proposed system in the presence of a time-varying workload of client-to-service requests, resulting in a reduction of power consumption of approximately 15% for an average response time increase of under 2%. Full article
(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)
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Open AccessArticle
Automated IoT Device Identification Based on Full Packet Information Using Real-Time Network Traffic
Sensors 2021, 21(8), 2660; https://doi.org/10.3390/s21082660 - 10 Apr 2021
Viewed by 330
Abstract
In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, [...] Read more.
In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, IoT device identification is the primary step toward a secure IoT system. An appropriate device identification approach can counteract malicious activities such as sending false data that trigger irreparable security issues in vital or emergency situations. Recent research indicates that primary identity metrics such as Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient due to their instability or easy accessibility. Thus, to identify an IoT device, analysis of the header information of packets by the sensors is of imperative consideration. This paper proposes a combination of sensor measurement and statistical feature sets in addition to a header feature set using a classification-based device identification framework. Various machine Learning algorithms have been adopted to identify different combinations of these feature sets to provide enhanced security in IoT devices. The proposed method has been evaluated through normal and under-attack circumstances by collecting real-time data from IoT devices connected in a lab setting to show the system robustness. Full article
(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)
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Open AccessArticle
Secure LoRa Firmware Update with Adaptive Data Rate Techniques
Sensors 2021, 21(7), 2384; https://doi.org/10.3390/s21072384 - 30 Mar 2021
Viewed by 291
Abstract
Internet of Things (IoT) devices rely upon remote firmware updates to fix bugs, update embedded algorithms, and make security enhancements. Remote firmware updates are a significant burden to wireless IoT devices that operate using low-power wide-area network (LPWAN) technologies due to slow data [...] Read more.
Internet of Things (IoT) devices rely upon remote firmware updates to fix bugs, update embedded algorithms, and make security enhancements. Remote firmware updates are a significant burden to wireless IoT devices that operate using low-power wide-area network (LPWAN) technologies due to slow data rates. One LPWAN technology, Long Range (LoRa), has the ability to increase the data rate at the expense of range and noise immunity. The optimization of communications for maximum speed is known as adaptive data rate (ADR) techniques, which can be applied to accelerate the firmware update process for any LoRa-enabled IoT device. In this paper, we investigate ADR techniques in an application that provides remote monitoring of cattle using small, battery-powered devices that transmit data on cattle location and health using LoRa. In addition to issues related to firmware update speed, there are significant concerns regarding reliability and security when updating firmware on mobile, energy-constrained devices. A malicious actor could attempt to steal the firmware to gain access to embedded algorithms or enable faulty behavior by injecting their own code into the device. A firmware update could be subverted due to cattle moving out of the LPWAN range or the device battery not being sufficiently charged to complete the update process. To address these concerns, we propose a secure and reliable firmware update process using ADR techniques that is applicable to any mobile or energy-constrained LoRa device. The proposed system is simulated and then implemented to evaluate its performance and security properties. Full article
(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)
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Open AccessArticle
Distributed Architecture for Unmanned Vehicle Services
Sensors 2021, 21(4), 1477; https://doi.org/10.3390/s21041477 - 20 Feb 2021
Viewed by 421
Abstract
The demand for online services is increasing. Services that would require a long time to understand, use and master are becoming as transparent as possible to the users, that tend to focus only on the final goals. Combined with the advantages of the [...] Read more.
The demand for online services is increasing. Services that would require a long time to understand, use and master are becoming as transparent as possible to the users, that tend to focus only on the final goals. Combined with the advantages of the unmanned vehicles (UV), from the unmanned factor to the reduced size and costs, we found an opportunity to bring to users a wide variety of services supported by UV, through the Internet of Unmanned Vehicles (IoUV). Current solutions were analyzed and we discussed scalability and genericity as the principal concerns. Then, we proposed a solution that combines several services and UVs, available from anywhere at any time, from a cloud platform. The solution considers a cloud distributed architecture, composed by users, services, vehicles and a platform, interconnected through the Internet. Each vehicle provides to the platform an abstract and generic interface for the essential commands. Therefore, this modular design makes easier the creation of new services and the reuse of the different vehicles. To confirm the feasibility of the solution we implemented a prototype considering a cloud-hosted platform and the integration of custom-built small-sized cars, a custom-built quadcopter, and a commercial Vertical Take-Off and Landing (VTOL) aircraft. To validate the prototype and the vehicles’ remote control, we created several services accessible via a web browser and controlled through a computer keyboard. We tested the solution in a local network, remote networks and mobile networks (i.e., 3G and Long-Term Evolution (LTE)) and proved the benefits of decentralizing the communications into multiple point-to-point links for the remote control. Consequently, the solution can provide scalable UV-based services, with low technical effort, for anyone at anytime and anywhere. Full article
(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)
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Open AccessArticle
Hybrid Blockchain for IoT—Energy Analysis and Reward Plan
Sensors 2021, 21(1), 305; https://doi.org/10.3390/s21010305 - 05 Jan 2021
Viewed by 805
Abstract
Blockchain technology has brought significant advantages for security and trustworthiness, in particular for Internet of Things (IoT) applications where there are multiple organisations that need to verify data and ensure security of shared smart contracts. Blockchain technology offers security features by means of [...] Read more.
Blockchain technology has brought significant advantages for security and trustworthiness, in particular for Internet of Things (IoT) applications where there are multiple organisations that need to verify data and ensure security of shared smart contracts. Blockchain technology offers security features by means of consensus mechanisms; two key consensus mechanisms are, Proof of Work (PoW) and Practical Byzantine Fault Tolerance (PBFT). While the PoW based mechanism is computationally intensive, due to the puzzle solving, the PBFT consensus mechanism is communication intensive due to the all-to-all messages; thereby, both may result in high energy consumption and, hence, there is a trade-off between the computation and the communication energy costs. In this paper, we propose a hybrid-blockchain (H-chain) framework appropriate for scenarios where multiple organizations exist and where the framework enables private transaction verification and public transaction sharing and audit, according to application needs. In particular, we study the energy consumption of the hybrid consensus mechanisms in H-chain. Moreover, this paper proposes a reward plan to incentivize the blockchain agents so that they make contributions to the H-chain while also considering the energy consumption. While the work is generally applicable to IoT applications, the paper illustrates the framework in a scenario which secures an IoT application connected using a software defined network (SDN). The evaluation results first provide a method to balance the public and private parts of the H-chain deployment according to network conditions, computation capability, verification complexity, among other parameters. The simulation results demonstrate that the reward plan can incentivize the blockchain agents to contribute to the H-chain considering the energy consumption of the hybrid consensus mechanism, this enables the proposed H-chain to achieve optimal social welfare. Full article
(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)
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Open AccessArticle
Analysis of Copernicus’ ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction
Sensors 2020, 20(21), 6381; https://doi.org/10.3390/s20216381 - 09 Nov 2020
Cited by 1 | Viewed by 777
Abstract
Knowledge of phenological events and their variability can help to determine final yield, plan management approach, tackle climate change, and model crop development. THe timing of phenological stages and phases is known to be highly correlated with temperature which is therefore an essential [...] Read more.
Knowledge of phenological events and their variability can help to determine final yield, plan management approach, tackle climate change, and model crop development. THe timing of phenological stages and phases is known to be highly correlated with temperature which is therefore an essential component for building phenological models. Satellite data and, particularly, Copernicus’ ERA5 climate reanalysis data are easily available. Weather stations, on the other hand, provide scattered temperature data, with fragmentary spatial coverage and accessibility, as such being scarcely efficacious as unique source of information for the implementation of predictive models. However, as ERA5 reanalysis data are not real temperature measurements but reanalysis products, it is necessary to verify whether these data can be used as a replacement for weather station temperature measurements. The aims of this study were: (i) to assess the validity of ERA5 data as a substitute for weather station temperature measurements, (ii) to test different machine learning models for the prediction of phenological phases while using different sets of features, and (iii) to optimize the base temperature of olive tree phenological model. The predictive capability of machine learning models and the performance of different feature subsets were assessed when comparing the recorded temperature data, ERA5 data, and a simple growing degree day phenological model as benchmark. Data on olive tree phenology observation, which were collected in Tuscany for three years, provided the phenological phases to be used as target variables. The results show that ERA5 climate reanalysis data can be used for modelling phenological phases and that these models provide better predictions in comparison with the models trained with weather station temperature measurements. Full article
(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)
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Open AccessArticle
Addressing Conceptual Randomness in IoT-Driven Business Ecosystem Research
Sensors 2020, 20(20), 5842; https://doi.org/10.3390/s20205842 - 15 Oct 2020
Viewed by 630
Abstract
During the almost 27 years of its existence, the business ecosystem research has developed a substantial level of ambiguity and multifacetedness. Because to the technological advancements that promote interconnectedness and value co-creation, the field has consequently spun off into more domain-specific branches, such [...] Read more.
During the almost 27 years of its existence, the business ecosystem research has developed a substantial level of ambiguity and multifacetedness. Because to the technological advancements that promote interconnectedness and value co-creation, the field has consequently spun off into more domain-specific branches, such as the arena of digital business ecosystems that are driven by Internet of Things (IoT). Nonetheless, despite the efforts to mend the theoretical foundations and to close the gap between academia and empirical practice, the absolute majority of IoT-driven digital business ecosystem literature follows the trend of conceptual randomness while expanding the volume of publications exponentially. Therefore, in order to address this unfavourable increase in random adoption of distinct concepts that ultimately refer to the same subject matter, the author encourages other scholars involved in the research field of IoT-driven digital business ecosystems to make extended efforts and support the external validity of their research (as well as the relevance of the research stream as a whole) by bounding the IoT-driven digital business ecosystems on a rigorous basis through deploying the extant theory in a careful and appropriate manner. Via a thorough examination of the theoretical fundaments that underpin the concept of IoT-driven digital business ecosystem, and based on a concise thematic review of corresponding literature published until September 2020, this article articulates logic for viewing the conceptual hierarchy within the business ecosystem research and proposes six literature-based recommendations for developing further IoT-driven digital business ecosystem (DBE) research in a rigorous way. Full article
(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)
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Review

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Open AccessReview
Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review
Sensors 2021, 21(6), 2152; https://doi.org/10.3390/s21062152 - 19 Mar 2021
Viewed by 392
Abstract
This paper summarises a literature review on the applications of Internet of Things (IoT) with the aim of enhancing building energy use and reducing greenhouse gas emissions (GHGs). A detailed assessment of contemporary practical reviews and works was conducted to understand how different [...] Read more.
This paper summarises a literature review on the applications of Internet of Things (IoT) with the aim of enhancing building energy use and reducing greenhouse gas emissions (GHGs). A detailed assessment of contemporary practical reviews and works was conducted to understand how different IoT systems and technologies are being developed to increase energy efficiencies in both residential and commercial buildings. Most of the reviewed works were invariably related to the dilemma of efficient heating systems in buildings. Several features of the central components of IoT, namely, the hardware and software needed for building controls, are analysed. Common design factors across the many IoT systems comprise the selection of sensors and actuators and their powering techniques, control strategies for collecting information and activating appliances, monitoring of actual data to forecast prospect energy consumption and communication methods amongst IoT components. Some building energy applications using IoT are provided. It was found that each application presented has the potential for significant energy reduction and user comfort improvement. This is confirmed in two case studies summarised, which report the energy savings resulting from implementing IoT systems. Results revealed that a few elements are user-specific that need to be considered in the decision processes. Last, based on the studies reviewed, a few aspects of prospective research were recommended. Full article
(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)
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