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5 pages, 194 KiB

Open AccessEditorial

Wireless Sensing and Networking for the Internet of Things

by Zihuai Lin and Wei Xiang

Sensors 2023, 23(3), 1461; https://doi.org/10.3390/s23031461 - 28 Jan 2023

Cited by 2 | Viewed by 2430

Abstract

In recent years, we have witnessed the exponential proliferation of the Internet of Things (IoT)-based networks of physical devices, vehicles, and appliances, as well as other items embedded with electronics, software, sensors, actuators, and connectivity, which enable these objects to connect and exchange [...] Read more.

In recent years, we have witnessed the exponential proliferation of the Internet of Things (IoT)-based networks of physical devices, vehicles, and appliances, as well as other items embedded with electronics, software, sensors, actuators, and connectivity, which enable these objects to connect and exchange data [...] Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

Research

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18 pages, 3328 KiB

Open AccessArticle

AI Based Digital Twin Model for Cattle Caring

by Xue Han, Zihuai Lin, Cameron Clark, Branka Vucetic and Sabrina Lomax

Sensors 2022, 22(19), 7118; https://doi.org/10.3390/s22197118 - 20 Sep 2022

Cited by 11 | Viewed by 3047

Abstract

In this paper, we develop innovative digital twins of cattle status that are powered by artificial intelligence (AI). The work is built on a farm IoT system that remotely monitors and tracks the state of cattle. A digital twin model of cattle based [...] Read more.

In this paper, we develop innovative digital twins of cattle status that are powered by artificial intelligence (AI). The work is built on a farm IoT system that remotely monitors and tracks the state of cattle. A digital twin model of cattle based on Deep Learning (DL) is generated using the sensor data acquired from the farm IoT system. The physiological cycle of cattle can be monitored in real time, and the state of the next physiological cycle of cattle can be anticipated using this model. The basis of this work is the vast amount of data that is required to validate the legitimacy of the digital twins model. In terms of behavioural state, this digital twin model has high accuracy, and the loss error of training reach about 0.580 and the loss error of predicting the next behaviour state of cattle is about 5.197 after optimization. The digital twins model developed in this work can be used to forecast the cattle’s future time budget. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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19 pages, 5359 KiB

Open AccessArticle

An Application of a LPWAN for Upgrading Proximal Soil Sensing Systems

by Yonghui Tu, Haoye Tang and Wenyou Hu

Sensors 2022, 22(12), 4333; https://doi.org/10.3390/s22124333 - 8 Jun 2022

Cited by 2 | Viewed by 1896

Abstract

In recent years, the Internet of Things (IoT), based on low-power wide-area network (LPWAN) wireless communication technology, has developed rapidly. On the one hand, the IoT makes it possible to conduct low-cost, low-power, wide-coverage, and real-time soil monitoring in fields. On the other [...] Read more.

In recent years, the Internet of Things (IoT), based on low-power wide-area network (LPWAN) wireless communication technology, has developed rapidly. On the one hand, the IoT makes it possible to conduct low-cost, low-power, wide-coverage, and real-time soil monitoring in fields. On the other hand, many proximal soil sensor devices designed based on conventional communication methods that are stored in an inventory face elimination. Considering the idea of saving resources and costs, this paper applied LPWAN technology to an inventoried proximal soil sensor device, by designing an attachment hardware system (AHS) and realizing technical upgrades. The results of the experimental tests proved that the sensor device, after upgrading, could work for several years with only a battery power supply, and the effective wireless communication coverage was nearly 1 km in a typical suburban farming environment. Therefore, the new device not only retained the original mature sensing technology of the sensor device, but also exhibited ultralow power consumption and long-distance transmission, which are advantages of the LPWAN; gave full play to the application value and economic value of the devices stored in inventory; and saved resources and costs. The proposed approach also provides a reference for applying LPWAN technology to a wider range of inventoried sensor devices for technical upgrading. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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18 pages, 972 KiB

Open AccessArticle

Designing a Reliable and Low-Latency LoRaWAN Solution for Environmental Monitoring in Factories at Major Accident Risk

by Dinesh Tamang, Alessandro Pozzebon, Lorenzo Parri, Ada Fort and Andrea Abrardo

Sensors 2022, 22(6), 2372; https://doi.org/10.3390/s22062372 - 19 Mar 2022

Cited by 12 | Viewed by 2730

Abstract

In this article, we propose a reliable and low-latency Long Range Wide Area Network (LoRaWAN) solution for environmental monitoring in factories at major accident risk (FMAR). In particular, a low power wearable device for sensing the toxic inflammable gases inside an industrial plant [...] Read more.

In this article, we propose a reliable and low-latency Long Range Wide Area Network (LoRaWAN) solution for environmental monitoring in factories at major accident risk (FMAR). In particular, a low power wearable device for sensing the toxic inflammable gases inside an industrial plant is designed with the purpose of avoiding peculiar risks and unwanted accidents to occur. Moreover, the detected data have to be urgently and reliably delivered to remote server to trigger preventive immediate actions so as to improve the machine operation. In these settings, LoRaWAN has been identified as the most proper communications technology to the needs owing to the availability of off the shelf devices and software. Hence, we assess the technological limits of LoRaWAN in terms of latency and reliability and we propose a fully LoRaWAN compliant solution to overcome these limits. The proposed solution envisages coordinated end device (ED) transmissions through the use of Downlink Control Packets (DCPs). Experimental results validate the proposed method in terms of service requirements for the considered FMAR scenario. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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18 pages, 2268 KiB

Open AccessArticle

Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors

by Khalid Haseeb, Amjad Rehman, Tanzila Saba, Saeed Ali Bahaj and Jaime Lloret

Sensors 2022, 22(6), 2115; https://doi.org/10.3390/s22062115 - 9 Mar 2022

Cited by 19 | Viewed by 2395

Abstract

Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT [...] Read more.

Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT devices are cooperative and allow the collection of unpredictable factors from the observing field. However, the constraint resources of distributed battery-powered sensors decrease the energy efficiency of the IoT network and increase the delay in receiving the network data on users’ devices. It is observed that many solutions are proposed to overcome the energy deficiency in smart applications; though, due to the mobility of the nodes, lots of communication incurs frequent data discontinuity, compromising the data trust. Therefore, this work introduces a D2D multi-criteria learning algorithm for IoT networks using secured sensors, which aims to improve the data exchange without imposing additional costs and data diverting for mobile sensors. Moreover, it reduces the compromising threats in the presence of anonymous devices and increases the trustworthiness of the IoT-enabled communication system with the support of machine learning. The proposed work was tested and analyzed using broad simulation-based experiments and demonstrated the significantly improved performance of the packet delivery ratio by 17%, packet disturbances by 31%, data delay by 22%, energy consumption by 24%, and computational complexity by 37% for realistic network configurations. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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17 pages, 715 KiB

Open AccessArticle

Unpacking the ‘15-Minute City’ via 6G, IoT, and Digital Twins: Towards a New Narrative for Increasing Urban Efficiency, Resilience, and Sustainability

by Zaheer Allam, Simon Elias Bibri, David S. Jones, Didier Chabaud and Carlos Moreno

Sensors 2022, 22(4), 1369; https://doi.org/10.3390/s22041369 - 10 Feb 2022

Cited by 68 | Viewed by 11908

Abstract

The ‘15-minute city’ concept is emerging as a potent urban regeneration model in post-pandemic cities, offering new vantage points on liveability and urban health. While the concept is primarily geared towards rethinking urban morphologies, it can be furthered via the adoption of Smart [...] Read more.

The ‘15-minute city’ concept is emerging as a potent urban regeneration model in post-pandemic cities, offering new vantage points on liveability and urban health. While the concept is primarily geared towards rethinking urban morphologies, it can be furthered via the adoption of Smart Cities network technologies to provide tailored pathways to respond to contextualised challenges through the advent of data mining and processing to better inform urban decision-making processes. We argue that the ‘15-minute city’ concept can value-add from Smart City network technologies in particular through Digital Twins, Internet of Things (IoT), and 6G. The data gathered by these technologies, and processed via Machine Learning techniques, can unveil new patterns to understand the characteristics of urban fabrics. Collectively, those dimensions, unpacked to support the ‘15-minute city’ concept, can provide new opportunities to redefine agendas to better respond to economic and societal needs as well as align more closely with environmental commitments, including the United Nations’ Sustainable Development Goal 11 and the New Urban Agenda. This perspective paper presents new sets of opportunities for cities arguing that these new connectivities should be explored now so that appropriate protocols can be devised and so that urban agendas can be recalibrated to prepare for upcoming technology advances, opening new pathways for urban regeneration and resilience crafting. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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16 pages, 669 KiB

Open AccessArticle

Closed-Form UAV LoS Blockage Probability in Mixed Ground- and Rooftop-Mounted Urban mmWave NR Deployments

by Vyacheslav Begishev, Dmitri Moltchanov, Anna Gaidamaka and Konstantin Samouylov

Sensors 2022, 22(3), 977; https://doi.org/10.3390/s22030977 - 27 Jan 2022

Cited by 7 | Viewed by 2338

Abstract

Unmanned aerial vehicles (UAV) are envisioned to become one of the new types of fifth/sixth generation (5G/6G) network users. To support advanced services for UAVs such as video monitoring, one of the prospective options is to utilize recently standardized New Radio (NR) technology [...] Read more.

Unmanned aerial vehicles (UAV) are envisioned to become one of the new types of fifth/sixth generation (5G/6G) network users. To support advanced services for UAVs such as video monitoring, one of the prospective options is to utilize recently standardized New Radio (NR) technology operating in the millimeter-wave (mmWave) frequency band. However, blockage of propagation paths between NR base stations (BS) and UAV by buildings may lead to frequent outage situations. In our study, we use the tools of integral geometry to characterize connectivity properties of UAVs in terrestrial urban deployments of mmWave NR systems using UAV line-of-sight (LoS) blockage probability as the main metric of interest. As opposed to other studies, the use of the proposed approach allows us to get closed-form approximation for LoS blockage probability as a function of city and network deployment parameters. As one of the options to improve connectivity we also consider rooftop-mounted mmWave BSs. Our results illustrate that the proposed model provides an upper bound on UAV LoS blockage probability, and this bound becomes more accurate as the density of mmWave BS in the area increases. The closed-form structure allows for identifying of the street width, building block and BS heights, and UAV altitude as the parameters providing the most impact on the considered metric. We show that rooftop-mounted mmWave BSs allow for the drastic improvement of LoS blockage probability, i.e., depending on the system parameters the use of one rooftop-mounted mmWave BS is equivalent to 6–12 ground-mounted mmWave BSs. Out of all considered deployment parameters the street width is the one most heavily affecting the UAV LoS blockage probability. Specifically, the deployment with street width of 20 m is characterized by 50% lower UAV LoS blockage probability as compared to the one with 10 m street width. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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24 pages, 7453 KiB

Open AccessArticle

Fast and Accurate Approach to RF-DC Conversion Efficiency Estimation for Multi-Tone Signals

by Janis Eidaks, Romans Kusnins, Ruslans Babajans, Darja Cirjulina, Janis Semenjako and Anna Litvinenko

Sensors 2022, 22(3), 787; https://doi.org/10.3390/s22030787 - 20 Jan 2022

Cited by 6 | Viewed by 3114

Abstract

The paper presents a computationally efficient and accurate numerical approach to evaluating RF–DC power conversion efficiency (PCE) for energy harvesting circuits in the case of multi-tone power-carrying signal with periodic envelopes. This type of signal has recently received considerable attention in the literature. [...] Read more.

The paper presents a computationally efficient and accurate numerical approach to evaluating RF–DC power conversion efficiency (PCE) for energy harvesting circuits in the case of multi-tone power-carrying signal with periodic envelopes. This type of signal has recently received considerable attention in the literature. It has been shown that their use may result in a higher PCE than the conventional sine wave signal for low to medium input power levels. This reason motivated the authors to develop a fast and accurate two-frequency harmonic balance method (2F-HB), as fast PCE calculation might appreciably expedite the converter circuit optimization process. In order to demonstrate the computational efficiency of the 2F-HB, a comparative study is performed. The results of this study show that the 2F-HB significantly outperforms such extensively used methods as the transient analysis (TA), the harmonic balance method (HB), and the multidimensional harmonic balance method (MHB). The method also outperforms the commercially available non-linear circuit simulator Keysight ADS employing both HB and MHB. Furthermore, the proposed method can be readily integrated into commonly used commercially available non-linear circuit simulation software, including the Keysight ADS, Ansys HFSS, just to name a few—minor modifications are required. In addition, to increase the correctness and reliability of the proposed method, the influence of PCB is considered by calculating Y parameters of its 3D model. The widely employed voltage doubler-based RF–DC converter for energy harvesting and wireless power transfer (WPT) in sub-GHz diapason is chosen to validate the proposed method experimentally. This RF–DC converter is chosen for its simplicity and capability to provide sufficiently high PCE. The measurements of the PCE for a voltage doubler prototype employing different multi-tone waveform signals were performed in laboratory conditions. Various combinations of the matching circuit element values were considered to find the optimal one in both—theoretical model and experimental prototype. The measured PCE is in very good agreement with the PCE calculated numerically, which attests to the validity of the proposed approach. The proposed PCE estimation method is not limited to one selected RF–DC conversion circuit and can also be applied to other circuits and frequency bands. The comparison of the PCE obtained by means of the proposed approach and the measured one shows very good agreement between them. The PCE estimation error reaches as low as 0.37%, and the maximal estimation error is 32.65%. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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20 pages, 2309 KiB

Open AccessArticle

IoTactileSim: A Virtual Testbed for Tactile Industrial Internet of Things Services

by Muhammad Zubair Islam, Shahzad, Rashid Ali, Amir Haider and Hyungseok Kim

Sensors 2021, 21(24), 8363; https://doi.org/10.3390/s21248363 - 15 Dec 2021

Cited by 8 | Viewed by 3498

Abstract

With the inclusion of tactile Internet (TI) in the industrial sector, we are at the doorstep of the tactile Industrial Internet of Things (IIoT). This provides the ability for the human operator to control and manipulate remote industrial environments in real-time. The TI [...] Read more.

With the inclusion of tactile Internet (TI) in the industrial sector, we are at the doorstep of the tactile Industrial Internet of Things (IIoT). This provides the ability for the human operator to control and manipulate remote industrial environments in real-time. The TI use cases in IIoT demand a communication network, including ultra-low latency, ultra-high reliability, availability, and security. Additionally, the lack of the tactile IIoT testbed has made it more severe to investigate and improve the quality of services (QoS) for tactile IIoT applications. In this work, we propose a virtual testbed called IoTactileSim, that offers implementation, investigation, and management for QoS provisioning in tactile IIoT services. IoTactileSim utilizes a network emulator Mininet and robotic simulator CoppeliaSim to perform real-time haptic teleoperations in virtual and physical environments. It provides the real-time monitoring of the implemented technology parametric values, network impairments (delay, packet loss), and data flow between operator (master domain) and teleoperator (slave domain). Finally, we investigate the results of two tactile IIoT environments to prove the potential of the proposed IoTactileSim testbed. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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19 pages, 5237 KiB

Open AccessArticle

Intelligent Electromagnetic Sensors for Non-Invasive Trojan Detection

by Ethan Chen, John Kan, Bo-Yuan Yang, Jimmy Zhu and Vanessa Chen

Sensors 2021, 21(24), 8288; https://doi.org/10.3390/s21248288 - 11 Dec 2021

Cited by 8 | Viewed by 3208

Abstract

Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, [...] Read more.

Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, including power consumption and electromagnetic (EM) emissions. This study presents a holistic self-testing approach incorporating nanoscale EM sensing devices and an energy-efficient learning module to detect security threats and malicious attacks directly at the front-end sensors. The built-in threat detection approach using the intelligent EM sensors distributed on the power lines is developed to detect abnormal data activities without degrading the performance while achieving good energy efficiency. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection system to predict malicious attacks rapidly in the front line. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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15 pages, 1872 KiB

Open AccessArticle

Geometric Algebra-Based ESPRIT Algorithm for DOA Estimation

by Rui Wang, Yue Wang, Yanping Li, Wenming Cao and Yi Yan

Sensors 2021, 21(17), 5933; https://doi.org/10.3390/s21175933 - 3 Sep 2021

Cited by 13 | Viewed by 4056

Abstract

Direction-of-arrival (DOA) estimation plays an important role in array signal processing, and the Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm is one of the typical super resolution algorithms for direction finding in an electromagnetic vector-sensor (EMVS) array; however, existing ESPRIT algorithms [...] Read more.

Direction-of-arrival (DOA) estimation plays an important role in array signal processing, and the Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm is one of the typical super resolution algorithms for direction finding in an electromagnetic vector-sensor (EMVS) array; however, existing ESPRIT algorithms treat the output of the EMVS array either as a “long vector”, which will inevitably lead to loss of the orthogonality of the signal components, or a quaternion matrix, which may result in some missing information. In this paper, we propose a novel ESPRIT algorithm based on Geometric Algebra (GA-ESPRIT) to estimate 2D-DOA with double parallel uniform linear arrays. The algorithm combines GA with the principle of ESPRIT, which models the multi-dimensional signals in a holistic way, and then the direction angles can be calculated by different GA matrix operations to keep the correlations among multiple components of the EMVS. Experimental results demonstrate that the proposed GA-ESPRIT algorithm is robust to model errors and achieves less time complexity and smaller memory requirements. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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36 pages, 36030 KiB

Open AccessEditor’s ChoiceArticle

Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case

by Paula Fraga-Lamas, Sérgio Ivan Lopes and Tiago M. Fernández-Caramés

Sensors 2021, 21(17), 5745; https://doi.org/10.3390/s21175745 - 26 Aug 2021

Cited by 191 | Viewed by 22638

Abstract

Internet of Things (IoT) can help to pave the way to the circular economy and to a more sustainable world by enabling the digitalization of many operations and processes, such as water distribution, preventive maintenance, or smart manufacturing. Paradoxically, IoT technologies and paradigms [...] Read more.

Internet of Things (IoT) can help to pave the way to the circular economy and to a more sustainable world by enabling the digitalization of many operations and processes, such as water distribution, preventive maintenance, or smart manufacturing. Paradoxically, IoT technologies and paradigms such as edge computing, although they have a huge potential for the digital transition towards sustainability, they are not yet contributing to the sustainable development of the IoT sector itself. In fact, such a sector has a significant carbon footprint due to the use of scarce raw materials and its energy consumption in manufacturing, operating, and recycling processes. To tackle these issues, the Green IoT (G-IoT) paradigm has emerged as a research area to reduce such carbon footprint; however, its sustainable vision collides directly with the advent of Edge Artificial Intelligence (Edge AI), which imposes the consumption of additional energy. This article deals with this problem by exploring the different aspects that impact the design and development of Edge-AI G-IoT systems. Moreover, it presents a practical Industry 5.0 use case that illustrates the different concepts analyzed throughout the article. Specifically, the proposed scenario consists in an Industry 5.0 smart workshop that looks for improving operator safety and operation tracking. Such an application case makes use of a mist computing architecture composed of AI-enabled IoT nodes. After describing the application case, it is evaluated its energy consumption and it is analyzed the impact on the carbon footprint that it may have on different countries. Overall, this article provides guidelines that will help future developers to face the challenges that will arise when creating the next generation of Edge-AI G-IoT systems. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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29 pages, 8598 KiB

Open AccessArticle

Landmark-Assisted Compensation of User’s Body Shadowing on RSSI for Improved Indoor Localisation with Chest-Mounted Wearable Device

by Md Abdulla Al Mamun, David Vera Anaya, Fan Wu and Mehmet Rasit Yuce

Sensors 2021, 21(16), 5405; https://doi.org/10.3390/s21165405 - 10 Aug 2021

Cited by 2 | Viewed by 3133

Abstract

Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance [...] Read more.

Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance. The broad aim of this study is to mitigate the user’s body shadowing effect on RSSI to improve localisation accuracy. Firstly, this study examines the effect of the user’s body on RSSI. Then, an angle estimation method is proposed by leveraging the concept of landmark. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. After that, a compensation model is proposed to correct the RSSI. Finally, the unknown location is estimated using the nearest neighbour method. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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28 pages, 9839 KiB

Open AccessEditor’s ChoiceArticle

Monitoring Soil and Ambient Parameters in the IoT Precision Agriculture Scenario: An Original Modeling Approach Dedicated to Low-Cost Soil Water Content Sensors

by Pisana Placidi, Renato Morbidelli, Diego Fortunati, Nicola Papini, Francesco Gobbi and Andrea Scorzoni

Sensors 2021, 21(15), 5110; https://doi.org/10.3390/s21155110 - 28 Jul 2021

Cited by 79 | Viewed by 9957

Abstract

A low power wireless sensor network based on LoRaWAN protocol was designed with a focus on the IoT low-cost Precision Agriculture applications, such as greenhouse sensing and actuation. All subsystems used in this research are designed by using commercial components and free or [...] Read more.

A low power wireless sensor network based on LoRaWAN protocol was designed with a focus on the IoT low-cost Precision Agriculture applications, such as greenhouse sensing and actuation. All subsystems used in this research are designed by using commercial components and free or open-source software libraries. The whole system was implemented to demonstrate the feasibility of a modular system built with cheap off-the-shelf components, including sensors. The experimental outputs were collected and stored in a database managed by a virtual machine running in a cloud service. The collected data can be visualized in real time by the user with a graphical interface. The reliability of the whole system was proven during a continued experiment with two natural soils, Loamy Sand and Silty Loam. Regarding soil parameters, the system performance has been compared with that of a reference sensor from Sentek. Measurements highlighted a good agreement for the temperature within the supposed accuracy of the adopted sensors and a non-constant sensitivity for the low-cost volumetric water contents (VWC) sensor. Finally, for the low-cost VWC sensor we implemented a novel procedure to optimize the parameters of the non-linear fitting equation correlating its analog voltage output with the reference VWC. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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16 pages, 3481 KiB

Open AccessArticle

SEMPANet: A Modified Path Aggregation Network with Squeeze-Excitation for Scene Text Detection

by Shuangshuang Li and Wenming Cao

Sensors 2021, 21(8), 2657; https://doi.org/10.3390/s21082657 - 9 Apr 2021

Cited by 5 | Viewed by 2491

Abstract

Recently, various object detection frameworks have been applied to text detection tasks and have achieved good performance in the final detection. With the further expansion of text detection application scenarios, the research value of text detection topics has gradually increased. Text detection in [...] Read more.

Recently, various object detection frameworks have been applied to text detection tasks and have achieved good performance in the final detection. With the further expansion of text detection application scenarios, the research value of text detection topics has gradually increased. Text detection in natural scenes is more challenging for horizontal text based on a quadrilateral detection box and for curved text of any shape. Most networks have a good effect on the balancing of target samples in text detection, but it is challenging to deal with small targets and solve extremely unbalanced data. We continued to use PSENet to deal with such problems in this work. On the other hand, we studied the problem that most of the existing scene text detection methods use ResNet and FPN as the backbone of feature extraction, and improved the ResNet and FPN network parts of PSENet to make it more conducive to the combination of feature extraction in the early stage. A SEMPANet framework without an anchor and in one stage is proposed to implement a lightweight model, which is embodied in the training time of about 24 h. Finally, we selected the two most representative datasets for oriented text and curved text to conduct experiments. On ICDAR2015, the improved network’s latest results further verify its effectiveness; it reached 1.01% in F-measure compared with PSENet-1s. On CTW1500, the improved network performed better than the original network on average. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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22 pages, 3608 KiB

Open AccessArticle

On the Optimal Lawful Intercept Access Points Placement Problem in Hybrid Software-Defined Networks

by Xiaosa Xu, Wen-Kang Jia, Yi Wu and Xufang Wang

Sensors 2021, 21(2), 428; https://doi.org/10.3390/s21020428 - 9 Jan 2021

Cited by 1 | Viewed by 2188

Abstract

For the law enforcement agencies, lawful interception is still one of the main means to intercept a suspect or address most illegal actions. Due to its centralized management, however, it is easy to implement in traditional networks, but the cost is high. In [...] Read more.

For the law enforcement agencies, lawful interception is still one of the main means to intercept a suspect or address most illegal actions. Due to its centralized management, however, it is easy to implement in traditional networks, but the cost is high. In view of this restriction, this paper aims to exploit software-defined network (SDN) technology to contribute to the next generation of intelligent lawful interception technology, i.e., to optimize the deployment of intercept access points (IAPs) in hybrid software-defined networks where both SDN nodes and non-SDN nodes exist simultaneously. In order to deploy IAPs, this paper puts forward an improved equal-cost multi-path shortest path algorithm and accordingly proposes three SDN interception models: T interception model, ECMP-T interception model and Fermat-point interception model. Considering the location relevance of all intercepted targets and the operation and maintenance cost of operators from the global perspective, by the way, we further propose a restrictive minimum vertex cover algorithm (RMVCA) in hybrid SDN. Implementing different SDN interception algorithms based RMVCA in real-world topologies, we can reasonably deploy the best intercept access point and intercept the whole hybrid SDN with the least SDN nodes, as well as significantly optimize the deployment efficiency of IAPs and improve the intercept link coverage in hybrid SDN, contributing to the implementation of lawful interception. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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Review

Jump to: Editorial, Research

20 pages, 3131 KiB

Open AccessReview

Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles

by Georgios Fevgas, Thomas Lagkas, Vasileios Argyriou and Panagiotis Sarigiannidis

Sensors 2022, 22(3), 1235; https://doi.org/10.3390/s22031235 - 6 Feb 2022

Cited by 48 | Viewed by 9779

Abstract

The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned [...] Read more.

The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems. This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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13 pages, 282 KiB

Open AccessReview

A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms

by Abebe Diro, Naveen Chilamkurti, Van-Doan Nguyen and Will Heyne

Sensors 2021, 21(24), 8320; https://doi.org/10.3390/s21248320 - 13 Dec 2021

Cited by 56 | Viewed by 9460

Abstract

The Internet of Things (IoT) consists of a massive number of smart devices capable of data collection, storage, processing, and communication. The adoption of the IoT has brought about tremendous innovation opportunities in industries, homes, the environment, and businesses. However, the inherent vulnerabilities [...] Read more.

The Internet of Things (IoT) consists of a massive number of smart devices capable of data collection, storage, processing, and communication. The adoption of the IoT has brought about tremendous innovation opportunities in industries, homes, the environment, and businesses. However, the inherent vulnerabilities of the IoT have sparked concerns for wide adoption and applications. Unlike traditional information technology (I.T.) systems, the IoT environment is challenging to secure due to resource constraints, heterogeneity, and distributed nature of the smart devices. This makes it impossible to apply host-based prevention mechanisms such as anti-malware and anti-virus. These challenges and the nature of IoT applications call for a monitoring system such as anomaly detection both at device and network levels beyond the organisational boundary. This suggests an anomaly detection system is strongly positioned to secure IoT devices better than any other security mechanism. In this paper, we aim to provide an in-depth review of existing works in developing anomaly detection solutions using machine learning for protecting an IoT system. We also indicate that blockchain-based anomaly detection systems can collaboratively learn effective machine learning models to detect anomalies. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

27 pages, 6313 KiB

Open AccessReview

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

by Kenneth E. Schackart III and Jeong-Yeol Yoon

Sensors 2021, 21(16), 5519; https://doi.org/10.3390/s21165519 - 17 Aug 2021

Cited by 54 | Viewed by 8494

Abstract

Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor’s signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. [...] Read more.

Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor’s signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data. Full article

(This article belongs to the Special Issue Wireless Sensing and Networking for the Internet of Things)

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