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Selected Papers from the Global IoT Summit GIoTS 2020

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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) | Viewed by 52408

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Prof. Dr. Antonio Skarmeta

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Department of Information and Communication Engineering, Faculty of Computer Science, University of Murcia, 30100 Murcia, Spain
Interests: Internet of Things; smart agriculture/cities/manufacturing; security; privacy; 5G
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Prof. Dr. Mirko Presser

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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
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Dr. Sébastien Ziegler

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Mandat International, International Cooperation Foundation and IoT Lab, 3 ch. du Champ-Baron, 1209 Geneva, Switzerland
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Prof. Dr. Srdjan Krčo

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DunavNET d.o.o., A. Čehova 1, 21000 Novi Sad, Serbia
Interests: IoT; smart agriculture/cities/manufacturing; digital transformations; business models
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Mr. Soumya Kanti Datta

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EURECOM, Campus SophiaTech, 450 route des Chappes, Biot 06410, France

Mr. Latif Ladid

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University of Luxembourg, Maison du Nombre, 6, avenue de la Fonte, L-4364 Esch-sur-Alzette, Luxembourg
Interests: IPv6; IoT; 5G
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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 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

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

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Published Papers (10 papers)

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27 pages, 6767 KiB

Open AccessArticle

Climate-Aware and IoT-Enabled Selection of the Most Suitable Stone Fruit Tree Variety

by Juan A. López-Morales, Juan A. Martínez, Manuel Caro, Manuel Erena and Antonio F. Skarmeta

Sensors 2021, 21(11), 3867; https://doi.org/10.3390/s21113867 - 3 Jun 2021

Cited by 7 | Viewed by 3858

Abstract

The application of new technologies such as the Internet of Things offers the opportunity to improve current agricultural development, facilitate daily tasks, and turn farms into efficient and sustainable production systems. The use of these new technologies enables the digital transformation process demanded by the sector and provides agricultural collectives with more optimized analysis and prediction tools. Due to climate change, one of the farm industry’s problems is the advance or decay in the cycle of stone fruit trees. The objective is to recommend whether a specific area meets the minimum climatic requirements for planting certain stone fruit trees based on climatic data and bioclimatic indicators. The methodology used implements a large amount of meteorological data to generate information on specific climatic conditions and interactions on crops. In this work, a pilot study has been carried out in the Region of Murcia using an IoT platform. We simulate scenarios for the development of stone fruit varieties better adapted to the environment. Based on the standard, open interfaces, and protocols, the platform integrates heterogeneous information sources and interoperability with other third-party solutions to exchange and exploit such information. Full article

(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)

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27 pages, 2985 KiB

Open AccessArticle

PyFF: A Fog-Based Flexible Architecture for Enabling Privacy-by-Design IoT-Based Communal Smart Environments

by Fatima Zohra Benhamida, Joan Navarro, Oihane Gómez-Carmona, Diego Casado-Mansilla, Diego López-de-Ipiña and Agustín Zaballos

Sensors 2021, 21(11), 3640; https://doi.org/10.3390/s21113640 - 24 May 2021

Cited by 6 | Viewed by 3367

Abstract

The advent of the Internet of Things (IoT) and the massive growth of devices connected to the Internet are reshaping modern societies. However, human lifestyles are not evolving at the same pace as technology, which often derives into users’ reluctance and aversion. Although it is essential to consider user involvement/privacy while deploying IoT devices in a human-centric environment, current IoT architecture standards tend to neglect the degree of trust that humans require to adopt these technologies on a daily basis. In this regard, this paper proposes an architecture to enable privacy-by-design with human-in-the-loop IoT environments. In this regard, it first distills two IoT use-cases with high human interaction to analyze the interactions between human beings and IoT devices in an environment which had not previously been subject to the Internet of People principles.. Leveraging the lessons learned in these use-cases, the Privacy-enabling Fog-based and Flexible (PyFF) human-centric and human-aware architecture is proposed which brings together distributed and intelligent systems are brought together. PyFF aims to maintain end-users’ privacy by involving them in the whole data lifecycle, allowing them to decide which information can be monitored, where it can be computed and the appropriate feedback channels in accordance with human-in-the-loop principles. Full article

(This article belongs to the Special Issue Selected Papers from the Global IoT Summit GIoTS 2020)

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The energy consumption data flow from the Ethernet-based Arduino microcontroller board to the remote server where the data were stored for later processing and analysis [<a href="#B26-sensors-21-03640" class="html-bibr">26</a>]. Full article ">Figure 2
System architecture of the Coffee Machine use-case [<a href="#B26-sensors-21-03640" class="html-bibr">26</a>]. Full article ">Figure 3
GreenSoul Reference Architecture [<a href="#B28-sensors-21-03640" class="html-bibr">28</a>]. Full article ">Figure 4
The GreenSoul Persuasion Treatments with the associated technology to deliver them (post-its, mobile app, physical devices and all the treatments together) [<a href="#B28-sensors-21-03640" class="html-bibr">28</a>]. Full article ">Figure 5
The proposed PyFF system architecture. Full article ">Figure 6
Abstraction of the PyFF architecture to address energy efficiency and user comfort in a smart workplace environment. Full article ">Figure 7
Implementation of the PyFF architecture in a smart workplace. Full article ">

18 pages, 2494 KiB

Open AccessArticle

Smart SDN Management of Fog Services to Optimize QoS and Energy

by Piotr Fröhlich, Erol Gelenbe, Jerzy Fiołka, Jacek Chęciński, Mateusz Nowak and Zdzisław Filus

Sensors 2021, 21(9), 3105; https://doi.org/10.3390/s21093105 - 29 Apr 2021

Cited by 13 | Viewed by 3052

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 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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17 pages, 1189 KiB

Open AccessEditor’s ChoiceArticle

Automated IoT Device Identification Based on Full Packet Information Using Real-Time Network Traffic

by Narges Yousefnezhad, Avleen Malhi and Kary Främling

Sensors 2021, 21(8), 2660; https://doi.org/10.3390/s21082660 - 10 Apr 2021

Cited by 30 | Viewed by 8056

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, 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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17 pages, 2580 KiB

Open AccessArticle

Secure LoRa Firmware Update with Adaptive Data Rate Techniques

by Derek Heeger, Maeve Garigan, Eirini Eleni Tsiropoulou and Jim Plusquellic

Sensors 2021, 21(7), 2384; https://doi.org/10.3390/s21072384 - 30 Mar 2021

Cited by 17 | Viewed by 3667

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 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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33 pages, 10375 KiB

Open AccessArticle

Distributed Architecture for Unmanned Vehicle Services

by João Ramos, Roberto Ribeiro, David Safadinho, João Barroso, Carlos Rabadão and António Pereira

Sensors 2021, 21(4), 1477; https://doi.org/10.3390/s21041477 - 20 Feb 2021

Cited by 7 | Viewed by 3650

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 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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21 pages, 1856 KiB

Open AccessArticle

Hybrid Blockchain for IoT—Energy Analysis and Reward Plan

by Jiejun Hu, Martin J. Reed, Mays Al-Naday and Nikolaos Thomos

Sensors 2021, 21(1), 305; https://doi.org/10.3390/s21010305 - 5 Jan 2021

Cited by 17 | Viewed by 5183

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 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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Graphical abstract
Full article ">Figure 1
Illustration of scenario and hybrid-chain architecture. Full article ">Figure 2
Total energy consumption per block with respect to the number of BCAs. <math display="inline"><semantics> <mrow> <msup> <mi>γ</mi> <mo>*</mo> </msup> <mo>:</mo> <msup> <mi>γ</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>100</mn> <mo>:</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>. Full article ">Figure 3
Total energy consumption with respect of the inter-organisation extra cost factor and the number of BCAs. <math display="inline"><semantics> <mrow> <msup> <mi>γ</mi> <mo>*</mo> </msup> <mo>:</mo> <msup> <mi>γ</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>100</mn> <mo>:</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>D</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>. Full article ">Figure 4
Total energy consumption with respect to the difficulty factor. Full article ">Figure 5
Total energy consumption with respect to the blocksize. With 5 BCAs in 3 organisations for Proof of Work (PoW), and 5 BCAs each organisation for Practical Byzantine Fault Tolerance (PBFT). Full article ">Figure 6
Proportion of the private blocks in respect to the number of BCAs and the resource of winning miner. Full article ">Figure 7
Social welfare and rewards. Full article ">

22 pages, 707 KiB

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

by Noelia Oses, Izar Azpiroz, Susanna Marchi, Diego Guidotti, Marco Quartulli and Igor G. Olaizola

Sensors 2020, 20(21), 6381; https://doi.org/10.3390/s20216381 - 9 Nov 2020

Cited by 39 | Viewed by 6414

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 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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30 pages, 1168 KiB

Open AccessArticle

Addressing Conceptual Randomness in IoT-Driven Business Ecosystem Research

by Fabien Rezac

Sensors 2020, 20(20), 5842; https://doi.org/10.3390/s20205842 - 15 Oct 2020

Cited by 7 | Viewed by 3915

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 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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40 pages, 8198 KiB

Open AccessReview

Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review

by Wahiba Yaïci, Karthik Krishnamurthy, Evgueniy Entchev and Michela Longo

Sensors 2021, 21(6), 2152; https://doi.org/10.3390/s21062152 - 19 Mar 2021

Cited by 59 | Viewed by 9165

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 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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