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Emerging Trends in Wireless Sensor Networks
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Emerging Trends in Wireless Sensor Networks

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 24255

Special Issue Editors

Dr. Constantinos Marios Angelopoulos

E-Mail Website
Guest Editor
Faculty of Science and Technology, Department of Computing and Informatic, Bournemouth University, Bournemouth, UK
Interests: Internet of Things; crowdsourced systems; smart circular economy
Prof. Dr. Sotiris Nikoletseas

E-Mail Website
Guest Editor
Department of Computer Engineering and Informatics, University of Patras and CTI, Patras, Greece
Interests: algorithmic aspects of wireless sensor networks; wireless power transfer
Dr. Theofanis P. Raptis

E-Mail Website
Guest Editor
Institute of Informatics and Telematics, and National Research Council of Italy, Pisa, Italy
Interests: industry 4.0; IoT; wireless power transfer
Special Issues, Collections and Topics in MDPI journals
Dr. Eirini Eleni Tsiropoulou

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Interests: network modeling and optimization; IoT; cyber–physical systems; smart grid systems; network economics; wireless networks; social networks; cybersecurity; resource management; reinforcement learning; human behavior modeling; concentrated solar power systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue aims to address a few complementary aspects of emerging trends in the distributed sensor systems research. The overall aim is to highlight selected driving technologies, approaches and principles that will enable the next generation of Internet of Things systems and their effective take up in smart society and economy.

Such novel technologies include the wireless provisioning of power as well as the effective harvesting of energy towards improving the efficiency of wireless communication systems and networks. Wireless power transfer (WPT) is the driving technology that will enable the next generation of distributed sensor systems, including battery-less sensors, passive RFID, as well as IoT, 5G and M2M solutions. Distributed WPT-enabled devices can be powered by harvesting energy from the surroundings (for example, electromagnetic energy), leading to a novel communication systems networks paradigm, the wirelessly powered systems and networks. Both tools and techniques to design (or improve) relevant distributed operations using WPT, as well as results from the analysis of data obtained through this enabler, are welcome in the Special Issue.

Moreover, 5G and beyond networks are evolving towards a competitive environment, where users have access to various resources, while their behaviors become strongly interdependent. This fact motivates the development of user-centric resource management frameworks, which enable users’ self-optimization and autonomy. This vision is further supported by the convergence of various emerging technologies enabling cyber-physical systems operation, including 5G/B5G technologies, Internet of Things (IoT), multi-access edge computing and software-defined networking, all targeting flexibility and efficiency.

A final perspective is to investigate how such emerging technologies can underpin the design and development of sustainable and highly efficient socio-technical systems, eventually enabling the transition to a smart circular economy. Future and emerging technologies, such as 5G networks, Internet of Things, distributed ledger technologies, and crowdsourced systems will play an important role in the transition to a circular economy, by enabling resources to be monitored, shared, reused, and repurposed in the context of innovative  business and operation models. Relevant research outcomes, experiences and lessons learnt from academia, industry and local communities are welcome in this Special Issue.

Dr. Constantinos Marios Angelopoulos
Prof. Dr. Sotiris Nikoletseas
Dr. Theofanis P. Raptis
Prof. Dr. Eirini Eleni Tsiropoulou
Guest Editors

Manuscript Submission Information

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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
  • wireless power transfer
  • energy harvesting
  • smart circular economy
  • 5G networking
  • advanced wireless networks
  • multi-access edge computing
  • software defined networking
  • crowdsourced systems

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Published Papers (7 papers)

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12 pages, 4284 KiB  
Article
LC Passive Wireless Sensor System Based on Two Switches for Detection of Triple Parameters
by Muhammad Mustafa, Mian Rizwan, Muhammad Kashif, Tahir Khan, Muhammad Waseem and Andres Annuk
Sensors 2022, 22(8), 3024; https://doi.org/10.3390/s22083024 - 14 Apr 2022
Cited by 5 | Viewed by 3340
Abstract
This paper presents the LC-type passive wireless sensing system for the simultaneous and independent detection of triple parameters, featuring three different capacitive sensors controlled by two mechanical switches. The sensor coil was connected with three different capacitors in parallel and two mechanical switches [...] Read more.
This paper presents the LC-type passive wireless sensing system for the simultaneous and independent detection of triple parameters, featuring three different capacitive sensors controlled by two mechanical switches. The sensor coil was connected with three different capacitors in parallel and two mechanical switches were in series between every two capacitors, which made the whole system have three resonant frequencies. The readout coil was magnetically coupled with the sensor coil to interrogate the sensor wirelessly. The circuit was simulated advanced design system (ADS) software, and the LC sensor system was mathematically analyzed by MATLAB. Results showed that the proposed LC sensing system could test three different capacitive sensors by detecting three different resonant frequencies. The sensitivity of sensors could be determined by the capacitance calculated from the detected resonant frequencies, and the resolution of capacitance was 0.1 PF and 0.2 PF when using the proposed sensor system in practical applications. To validate the proposed scheme, a PCB inductor and three variable capacitors were constructed with two mechanical switches to realize the desired system. Experimental results closely verified the simulation outputs. Full article
(This article belongs to the Special Issue Emerging Trends in Wireless Sensor Networks)
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<p>Scheme of LC passive wireless sensor inductive link.</p> Full article ">Figure 2
<p>Graphical representation of LC passive wireless sensor system.</p> Full article ">Figure 3
<p>Theoretical model demonstration (<b>a</b>) LC triple parameters monitoring system integrated with two relay switches. (<b>b</b>) Representation of working principle.</p> Full article ">Figure 4
<p>Layout simulations setup using ADS and results. (<b>a</b>) Simulation setup when both switches were in off-state. (<b>b</b>) Detected resonant frequencies by applying sweep parameter at <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>1</mn> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Simulation curves of resonant frequencies versus three capacitors, respectively (<b>a</b>) Resonant frequencies versus <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>1</mn> </msub> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>2</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>3</mn> </msub> </mrow> </semantics></math> were fixed at 50 pF; (<b>b</b>) resonant frequencies versus <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>2</mn> </msub> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>3</mn> </msub> </mrow> </semantics></math> were fixed at 50 pF; (<b>c</b>) resonant frequencies versus <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>3</mn> </msub> <mo> </mo> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>2</mn> </msub> </mrow> </semantics></math> were fixed at 50 pF.</p> Full article ">Figure 6
<p>Schematic diagram of experimental setup.</p> Full article ">Figure 7
<p>Experiments of the LC sensor system. (<b>a</b>) Experimental platform of readout coil and monitoring sensor system. (<b>b</b>) PCB planar square copper inductor.</p> Full article ">Figure 8
<p>Experimental results of detected resonant frequency versus S11 parameter of proposed LC sensor system, (<b>a</b>) combined representation of detected frequencies for maximum and minimum values of three capacitors, (<b>b</b>) curves for variation in <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>1</mn> </msub> </mrow> </semantics></math>, (<b>c</b>) curves for variation in <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">C</mi> <mn>2</mn> </msub> </mrow> </semantics></math>, (<b>d</b>) curves for variation in <math display="inline"><semantics> <mrow> <msub> <mrow> <mrow> <mi mathvariant="normal">C</mi> </mrow> </mrow> <mn>3</mn> </msub> </mrow> </semantics></math>.</p> Full article ">
21 pages, 1301 KiB  
Article
Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches
by Theofanis P. Raptis, Andrea Formica, Elena Pagani, Andrea Passarella and Marco Conti
Sensors 2022, 22(7), 2533; https://doi.org/10.3390/s22072533 - 25 Mar 2022
Viewed by 2187
Abstract
Data distribution is a cornerstone of efficient automation for intelligent machines in Industry 4.0. Although in the recent literature there have been several comparisons of relevant methods, we identify that most of those comparisons are either theoretical or based on abstract simulation tools, [...] Read more.
Data distribution is a cornerstone of efficient automation for intelligent machines in Industry 4.0. Although in the recent literature there have been several comparisons of relevant methods, we identify that most of those comparisons are either theoretical or based on abstract simulation tools, unable to uncover the specific, detailed impacts of the methods to the underlying networking infrastructure. In this respect, as a first contribution of this paper, we develop more detailed and fine-tuned solutions for robust data distribution in smart factories on stationary and mobile scenarios of wireless industrial networking. Using the technological enablers of WirelessHART, RPL and the methodological enabler of proxy selection as building blocks, we compose the protocol stacks of four different methods (both centralized and decentralized) for data distribution in wireless industrial networks over the IEEE 802.15.4 physical layer. We implement the presented methods in the highly detailed OMNeT++ simulation environment and we evaluate their performance via an extensive simulation analysis. Interestingly enough, we demonstrate that the careful selection of a limited set of proxies for data caching in the network can lead to an increased data delivery success rate and low data access latency. Next, we describe two test cases demonstrated in an industrial smart factory environment. First, we show the collaboration between robotic elements and wireless data services. Second, we show the integration with an industrial fog node which controls the shop-floor devices. We report selected results in much larger scales, obtained via simulations. Full article
(This article belongs to the Special Issue Emerging Trends in Wireless Sensor Networks)
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<p>Square grid topology scheme.</p> Full article ">Figure 2
<p>Flowchart representing the adopted methodology.</p> Full article ">Figure 3
<p>Superframe slot numbers.</p> Full article ">Figure 4
<p>Performance for variable number of nodes. (<b>a</b>) Success rate; (<b>b</b>) Average latency; (<b>c</b>) Maximum latency; (<b>d</b>) Maximum average latency; (<b>e</b>) Number of proxies.</p> Full article ">Figure 5
<p>Traffic heatmaps. (<b>a</b>) C1 (RPL in non-storing mode); (<b>b</b>) D1 (RPL in storing mode); (<b>c</b>) D2 (proxy selection algorithm).</p> Full article ">Figure 6
<p>Performance for variable number of consumers. (<b>a</b>) Success rate; (<b>b</b>) average latency; (<b>c</b>) maximum latency; (<b>d</b>) maximum average latency.</p> Full article ">Figure 7
<p>First test case set-up. (<b>a</b>) Backbone network operational; (<b>b</b>) backbone network not operational.</p> Full article ">Figure 8
<p>Second test case set-up.</p> Full article ">Figure 9
<p>Scalability performance in simulations.</p> Full article ">
18 pages, 700 KiB  
Article
Local Trust in Internet of Things Based on Contract Theory
by Georgios Fragkos, Cyrus Minwalla, Jim Plusquellic and Eirini Eleni Tsiropoulou
Sensors 2022, 22(6), 2393; https://doi.org/10.3390/s22062393 - 20 Mar 2022
Cited by 2 | Viewed by 2040
Abstract
Autonomous trust mechanisms enable Internet of Things (IoT) devices to function cooperatively in a wide range of ecosystems, from vehicle-to-vehicle communications to mesh sensor networks. A common property desired in such networks is a mechanism to construct a secure, authenticated channel between any [...] Read more.
Autonomous trust mechanisms enable Internet of Things (IoT) devices to function cooperatively in a wide range of ecosystems, from vehicle-to-vehicle communications to mesh sensor networks. A common property desired in such networks is a mechanism to construct a secure, authenticated channel between any two participating nodes to share sensitive information, nominally a challenging proposition for a large, heterogeneous network where node participation is constantly in flux. This work explores a contract-theoretic framework that exploits the principles of network economics to crowd-source trust between two arbitrary nodes based on the efforts of their neighbors. Each node in the network possesses a trust score, which is updated based on useful effort contributed to the authentication step. The scheme functions autonomously on locally adjacent nodes and is proven to converge onto an optimal solution based on the available nodes and their trust scores. Core building blocks include the use of Stochastic Learning Automata to select the participating nodes based on network and social metrics, and the formulation of a Bayesian trust belief distribution from the past behavior of the selected nodes. An effort-reward model incentivizes selected nodes to accurately report their trust scores and contribute their effort to the authentication process. Detailed numerical results obtained via simulation highlight the proposed framework’s efficacy and performance. The performance achieved near-optimal results despite incomplete information regarding the IoT nodes’ trust scores and the presence of malicious or misbehaving nodes. Comparison metrics demonstrate that the proposed approach maximized the overall social welfare and achieved better performance compared to the state of the art in the domain. Full article
(This article belongs to the Special Issue Emerging Trends in Wireless Sensor Networks)
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<p>General Architecture.</p> Full article ">Figure 2
<p>Stochastic Learning Automata operation and performance evaluation. (<b>a</b>) Action Probability vs. Iterations, (<b>b</b>) Average Trustworthiness &amp; Network Overhead vs. Iterations, (<b>c</b>) Average Personalized Feedback vs. Iterations, (<b>d</b>) Average Personalized Feedback and Convergence Time vs. <span class="html-italic">b</span>.</p> Full article ">Figure 3
<p>Bayesian trust belief evaluation (<span class="html-italic">S</span>: positive, <span class="html-italic">F</span>: negative evaluations). (<b>a</b>) Trust Belief vs. Interactions, (<b>b</b>) Evaluations vs. Interactions.</p> Full article ">Figure 4
<p>Offline contract-theoretic crowdsourcing—operation and performance evaluation. (<b>a</b>) Nodes’ Scores vs. Interactions, (<b>b</b>) Alice’s Belief vs. Interactions, (<b>c</b>) Effort vs. Nodes, (<b>d</b>) Reward vs. Nodes, (<b>e</b>) Nodes’ Payoff vs. Nodes, (<b>f</b>) Nodes’ Payoff vs. Nodes IDs, (<b>g</b>) Alice’s Payoff vs. Nodes, (<b>h</b>) Social Welfare vs. Nodes.</p> Full article ">Figure 5
<p>Behavioral change evaluation. (<b>a</b>) Alice’s belief vs. interactions, (<b>b</b>) average reward vs. behaviors.</p> Full article ">Figure 6
<p>Offline contract-theoretic crowdsourcing—a comparative evaluation.</p> Full article ">
23 pages, 2407 KiB  
Article
Design, Development, and Evaluation of 5G-Enabled Vehicular Services: The 5G-HEART Perspective
by Grigorios Kakkavas, Maria Diamanti, Adamantia Stamou, Vasileios Karyotis, Faouzi Bouali, Jarno Pinola, Olli Apilo, Symeon Papavassiliou and Klaus Moessner
Sensors 2022, 22(2), 426; https://doi.org/10.3390/s22020426 - 6 Jan 2022
Cited by 19 | Viewed by 4293
Abstract
The ongoing transition towards 5G technology expedites the emergence of a variety of mobile applications that pertain to different vertical industries. Delivering on the key commitment of 5G, these diverse service streams, along with their distinct requirements, should be facilitated under the same [...] Read more.
The ongoing transition towards 5G technology expedites the emergence of a variety of mobile applications that pertain to different vertical industries. Delivering on the key commitment of 5G, these diverse service streams, along with their distinct requirements, should be facilitated under the same unified network infrastructure. Consequently, in order to unleash the benefits brought by 5G technology, a holistic approach towards the requirement analysis and the design, development, and evaluation of multiple concurrent vertical services should be followed. In this paper, we focus on the Transport vertical industry, and we study four novel vehicular service categories, each one consisting of one or more related specific scenarios, within the framework of the “5G Health, Aquaculture and Transport (5G-HEART)” 5G PPP ICT-19 (Phase 3) project. In contrast to the majority of the literature, we provide a holistic overview of the overall life-cycle management required for the realization of the examined vehicular use cases. This comprises the definition and analysis of the network Key Performance Indicators (KPIs) resulting from high-level user requirements and their interpretation in terms of the underlying network infrastructure tasked with meeting their conflicting or converging needs. Our approach is complemented by the experimental investigation of the real unified 5G pilot’s characteristics that enable the delivery of the considered vehicular services and the initial trialling results that verify the effectiveness and feasibility of the presented theoretical analysis. Full article
(This article belongs to the Special Issue Emerging Trends in Wireless Sensor Networks)
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<p>Overview of the proposed methodology.</p> Full article ">Figure 2
<p>High-level overview of the advanced use cases expected to be supported by 5G V2X.</p> Full article ">Figure 3
<p>Radar chart of transport use cases.</p> Full article ">Figure 4
<p>KPIs of Transport use cases and respective scenarios.</p> Full article ">Figure 5
<p>KPIs of the Transport vertical and related 5G service types.</p> Full article ">Figure 6
<p>The 5G test facility architecture with estimated backhaul and fronthaul link distances.</p> Full article ">
17 pages, 660 KiB  
Article
IDS for Industrial Applications: A Federated Learning Approach with Active Personalization
by Vasiliki Kelli, Vasileios Argyriou, Thomas Lagkas, George Fragulis, Elisavet Grigoriou and Panagiotis Sarigiannidis
Sensors 2021, 21(20), 6743; https://doi.org/10.3390/s21206743 - 11 Oct 2021
Cited by 29 | Viewed by 4021
Abstract
Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. [...] Read more.
Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries. Full article
(This article belongs to the Special Issue Emerging Trends in Wireless Sensor Networks)
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<p>The proposed methodology combining FL for global model formation and AL for model personalization.</p> Full article ">Figure 2
<p>The proposed DNN architecture, receiving <span class="html-italic">V</span> features as an input and producing <span class="html-italic">K</span> outputs.</p> Full article ">Figure 3
<p>FL Training. <span class="html-italic">X</span>-axis: Federated Rounds, <span class="html-italic">Y</span>-axis: value of accuracy(blue and loss (red). (<b>a</b>) Worker 1 (W1) FL Training Metrics; (<b>b</b>) Worker 2 (W2) FL Training Metrics; (<b>c</b>) Worker 3 (W3) FL Training Metrics.</p> Full article ">Figure 3 Cont.
<p>FL Training. <span class="html-italic">X</span>-axis: Federated Rounds, <span class="html-italic">Y</span>-axis: value of accuracy(blue and loss (red). (<b>a</b>) Worker 1 (W1) FL Training Metrics; (<b>b</b>) Worker 2 (W2) FL Training Metrics; (<b>c</b>) Worker 3 (W3) FL Training Metrics.</p> Full article ">Figure 4
<p>Accuracy of W1, W2 and W3’s models personalized with 20% AL dataset bias (<span class="html-italic">Y</span>-axis) per Query (<span class="html-italic">X</span>-axis), evaluated using their corresponding evaluation datasets. The FL accuracy for each worker is represented by the horizontal line of the worker’s respective color.</p> Full article ">Figure 5
<p>W1 Confusion Matrices. (<b>a</b>) W1’s evaluation of global FL model after <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> using its evaluation dataset; (<b>b</b>) W1’s evaluation of customized model by AL, after Q = 10 queries, using its evaluation dataset.</p> Full article ">Figure 6
<p>Accuracy of W1, W2 and W3’s models personalized with 50% AL dataset bias (<span class="html-italic">Y</span>-axis) per Query (<span class="html-italic">X</span>-axis), evaluated using their corresponding evaluation datasets. The FL accuracy for each worker is represented by the horizontal line of the worker’s respective color.</p> Full article ">Figure 7
<p>W3 Confusion Matrices. (<b>a</b>) W3’s evaluation of global FL model after <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> using its evaluation dataset; (<b>b</b>) W3’s evaluation of customized model by AL, after Q = 10 queries, using its evaluation dataset.</p> Full article ">Figure 8
<p>Accuracy of W1, W2 and W3’s models personalized with 70% AL dataset bias (<span class="html-italic">Y</span>-axis) per Query (<span class="html-italic">X</span>-axis), evaluated using their corresponding evaluation datasets. The FL accuracy for each worker is represented by the horizontal line of the worker’s respective color.</p> Full article ">Figure 9
<p>W1 Confusion Matrices. (<b>a</b>) W1’s evaluation of global FL model after <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math> using its evaluation dataset; (<b>b</b>) W1’s evaluation of customized model by AL, after Q = 20 queries, using its evaluation dataset.</p> Full article ">Figure 10
<p>Accuracy of W1, W2 and W3’s models personalized with balanced AL dataset (<span class="html-italic">Y</span>-axis) per Query (<span class="html-italic">X</span>-axis), evaluated using their corresponding evaluation datasets. The FL accuracy for each worker is represented by the horizontal line of the worker’s respective color.</p> Full article ">
19 pages, 894 KiB  
Article
Firmware Update Using Multiple Gateways in LoRaWAN Networks
by Christia Charilaou, Spyros Lavdas, Ala Khalifeh, Vasos Vassiliou and Zinon Zinonos
Sensors 2021, 21(19), 6488; https://doi.org/10.3390/s21196488 - 28 Sep 2021
Cited by 9 | Viewed by 3295
Abstract
The remarkable evolution of the IoT raised the need for an efficient way to update the device’s firmware. Recently, a new process was released summarizing the steps for firmware updates over the air (FUOTA) on top of the LoRaWAN protocol. The FUOTA process [...] Read more.
The remarkable evolution of the IoT raised the need for an efficient way to update the device’s firmware. Recently, a new process was released summarizing the steps for firmware updates over the air (FUOTA) on top of the LoRaWAN protocol. The FUOTA process needs to be completed quickly to reduce the systems’ interruption and, at the same time, to update the maximum number of devices with the lowest power consumption. However, as the literature showed, a single gateway cannot optimize the FUOTA procedure and offer the above mentioned goals since various trade-offs arise. In this paper, we conducted extensive experiments via simulation to investigate the impact of multiple gateways during the firmware update process. To achieve that, we extended the FUOTAsim simulation tool to support multiple gateways. The results revealed that several gateways could eliminate the trade-offs that appeared using a single gateway. Full article
(This article belongs to the Special Issue Emerging Trends in Wireless Sensor Networks)
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<p>FUOTA architecture.</p> Full article ">Figure 2
<p>Gateway Clusters (<b>a</b>) 4 Gateways, (<b>b</b>) 10 Gateways, (<b>c</b>) 17 Gateways, and (<b>d</b>) 30 Gateways.</p> Full article ">Figure 3
<p>Network energy using multiple firmware size.</p> Full article ">Figure 4
<p>Update time using multiple GWs and firmware size.</p> Full article ">Figure 5
<p>Fragments lost using different firmware sizes.</p> Full article ">Figure 6
<p>Effect of multiple GWs and firmware size in corrupted fragments.</p> Full article ">Figure 7
<p>Update efficiency using 10,000 nodes and different firmware size.</p> Full article ">Figure 8
<p>Update efficiency vs. multiple GWs and number of nodes.</p> Full article ">Figure 9
<p>Update time vs. multiple Gws and number of nodes.</p> Full article ">Figure 10
<p>Effect of redundant fragments in update efficiency.</p> Full article ">Figure 11
<p>Effect of redundant fragments in network energy.</p> Full article ">
15 pages, 1616 KiB  
Article
A Cyber-Physical Data Collection System Integrating Remote Sensing and Wireless Sensor Networks for Coffee Leaf Rust Diagnosis
by David Velásquez, Alejandro Sánchez, Sebastián Sarmiento, Camilo Velásquez, Mauricio Toro, Edwin Montoya, Helmuth Trefftz, Mikel Maiza and Basilio Sierra
Sensors 2021, 21(16), 5474; https://doi.org/10.3390/s21165474 - 13 Aug 2021
Cited by 2 | Viewed by 2804
Abstract
Coffee Leaf Rust (CLR) is a fungal epidemic disease that has been affecting coffee trees around the world since the 1980s. The early diagnosis of CLR would contribute strategically to minimize the impact on the crops and, therefore, protect the farmers’ profitability. In [...] Read more.
Coffee Leaf Rust (CLR) is a fungal epidemic disease that has been affecting coffee trees around the world since the 1980s. The early diagnosis of CLR would contribute strategically to minimize the impact on the crops and, therefore, protect the farmers’ profitability. In this research, a cyber-physical data-collection system was developed, by integrating Remote Sensing and Wireless Sensor Networks, to gather data, during the development of the CLR, on a test bench coffee-crop. The system is capable of automatically collecting, structuring, and locally and remotely storing reliable multi-type data from different field sensors, Red-Green-Blue (RGB) and multi-spectral cameras (RE and RGN). In addition, a data-visualization dashboard was implemented to monitor the data-collection routines in real-time. The operation of the data collection system allowed to create a three-month size dataset that can be used to train CLR diagnosis machine learning models. This result validates that the designed system can collect, store, and transfer reliable data of a test bench coffee-crop towards CLR diagnosis. Full article
(This article belongs to the Special Issue Emerging Trends in Wireless Sensor Networks)
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<p>Black box representation of the cyber-physical data collection system.</p> Full article ">Figure 2
<p>Final concept sketch for the data collection system: (<b>a</b>) of the physical part; (<b>b</b>) of the cybernetic part.</p> Full article ">Figure 3
<p>Final 3D CAD of the data collection system’s physical part.</p> Full article ">Figure 4
<p>Final design of the data collection system’s cybernetic part.</p> Full article ">Figure 5
<p>Example of generated files after data collection routine: (<b>a</b>) RGN image from lot 1; (<b>b</b>) RE image from lot 1; (<b>c</b>) RGB image from plant 3, lot 3; (<b>d</b>) RGB image from plant 4, lot 3.</p> Full article ">
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