Journal of Sensor and Actuator Networks

49 pages, 3741 KB

Open AccessEditor’s ChoiceReview

Optimal Sensor Placement for Structural Health Monitoring: A Comprehensive Review

by Zhiyan Sun, Mojtaba Mahmoodian, Amir Sidiq, Sanduni Jayasinghe, Farham Shahrivar and Sujeeva Setunge

J. Sens. Actuator Netw. 2025, 14(2), 22; https://doi.org/10.3390/jsan14020022 - 20 Feb 2025

Cited by 13 | Viewed by 6593

Abstract

The structural health monitoring (SHM) of bridge infrastructure has become essential for ensuring safety, serviceability, and long-term functionality amid aging structures and increasing load demands. SHM leverages sensor networks to enable real-time data acquisition, damage detection, and predictive maintenance, offering a more reliable [...] Read more.

The structural health monitoring (SHM) of bridge infrastructure has become essential for ensuring safety, serviceability, and long-term functionality amid aging structures and increasing load demands. SHM leverages sensor networks to enable real-time data acquisition, damage detection, and predictive maintenance, offering a more reliable alternative to traditional visual inspection methods. A key challenge in SHM is optimal sensor placement (OSP), which directly impacts monitoring accuracy, cost-efficiency, and overall system performance. This review explores recent advancements in SHM techniques, sensor technologies, and OSP methodologies, with a primary focus on bridge infrastructure. It evaluates sensor configuration strategies based on criteria such as the modal assurance criterion (MAC) and mean square error (MSE) while examining optimisation approaches like the Effective Independence (EI) method, Kinetic Energy Optimisation (KEO), and their advanced variants. Despite these advancements, several research gaps remain. Future studies should focus on scalable OSP strategies for large-scale bridge networks, integrating machine learning (ML) and artificial intelligence (AI) for adaptive sensor deployment. The implementation of digital twin (DT) technology in SHM can enhance predictive maintenance and real-time decision-making, improving long-term infrastructure resilience. Additionally, research on sensor robustness against environmental noise and external disturbances, as well as the integration of edge computing and wireless sensor networks (WSNs) for efficient data transmission, will be critical in advancing SHM applications. This review provides critical insights and recommendations to bridge the gap between theoretical innovations and real-world implementation, ensuring the effective monitoring and maintenance of bridge infrastructure in modern civil engineering. Full article

(This article belongs to the Section Actuators, Sensors and Devices)

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28 pages, 4045 KB

Open AccessEditor’s ChoiceArticle

Advanced Digital Solutions for Food Traceability: Enhancing Origin, Quality, and Safety Through NIRS, RFID, Blockchain, and IoT

by Matyas Lukacs, Fruzsina Toth, Roland Horvath, Gyula Solymos, Boglárka Alpár, Peter Varga, Istvan Kertesz, Zoltan Gillay, Laszlo Baranyai, Jozsef Felfoldi, Quang D. Nguyen, Zoltan Kovacs and Laszlo Friedrich

J. Sens. Actuator Netw. 2025, 14(1), 21; https://doi.org/10.3390/jsan14010021 - 17 Feb 2025

Cited by 7 | Viewed by 5936

Abstract

The rapid growth of the human population, the increase in consumer needs regarding food authenticity, and the sub-par synchronization between agricultural and food industry production necessitate the development of reliable track and tracing solutions for food commodities. The present research proposes a simple [...] Read more.

The rapid growth of the human population, the increase in consumer needs regarding food authenticity, and the sub-par synchronization between agricultural and food industry production necessitate the development of reliable track and tracing solutions for food commodities. The present research proposes a simple and affordable digital system that could be implemented in most production processes to improve transparency and productivity. The system combines non-destructive, rapid quality assessment methods, such as near infrared spectroscopy (NIRS) and computer/machine vision (CV/MV), with track and tracing functionalities revolving around the Internet of Things (IoT) and radio frequency identification (RFID). Meanwhile, authenticity is provided by a self-developed blockchain-based solution that validates all data and documentation “from farm to fork”. The system is introduced by taking certified Hungarian sweet potato production as a model scenario. Each element of the proposed system is discussed in detail individually and as a part of an integrated system, capable of automatizing most production flows while maintaining complete transparency and compliance with authority requirements. The results include the data and trust model of the system with sequence diagrams simulating the interactions between participants. The study lays the groundwork for future research and industrial applications combining digital tools to improve the productivity and authenticity of the agri-food industry, potentially increasing the level of trust between participants, most importantly for the consumers. Full article

(This article belongs to the Topic Trends and Prospects in Security, Encryption and Encoding)

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38 pages, 2985 KB

Open AccessEditor’s ChoiceSystematic Review

Generative Artificial Intelligence and the Evolving Challenge of Deepfake Detection: A Systematic Analysis

by Reza Babaei, Samuel Cheng, Rui Duan and Shangqing Zhao

J. Sens. Actuator Netw. 2025, 14(1), 17; https://doi.org/10.3390/jsan14010017 - 6 Feb 2025

Cited by 10 | Viewed by 21261

Abstract

Deepfake technology, which employs advanced generative artificial intelligence to create hyper-realistic synthetic media, poses significant challenges across various sectors, including security, entertainment, and education. This literature review explores the evolution of deepfake generation methods, ranging from traditional techniques to state-of-the-art models such as [...] Read more.

Deepfake technology, which employs advanced generative artificial intelligence to create hyper-realistic synthetic media, poses significant challenges across various sectors, including security, entertainment, and education. This literature review explores the evolution of deepfake generation methods, ranging from traditional techniques to state-of-the-art models such as generative adversarial networks and diffusion models. We navigate through the effectiveness and limitations of various detection approaches, including machine learning, forensic analysis, and hybrid techniques, while highlighting the critical importance of interpretability and real-time performance in detection systems. Furthermore, we discuss the ethical implications and regulatory considerations surrounding deepfake technology, emphasizing the need for comprehensive frameworks to mitigate risks associated with misinformation and manipulation. Through a systematic review of the existing literature, our aim is to identify research gaps and future directions for the development of robust, adaptable detection systems that can keep pace with rapid advancements in deepfake generation. Full article

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38 pages, 14791 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Online High-Definition Map Construction for Autonomous Vehicles: A Comprehensive Survey

by Hongyu Lyu, Julie Stephany Berrio Perez, Yaoqi Huang, Kunming Li, Mao Shan and Stewart Worrall

J. Sens. Actuator Netw. 2025, 14(1), 15; https://doi.org/10.3390/jsan14010015 - 2 Feb 2025

Viewed by 6783

Abstract

High-definition (HD) maps aim to provide detailed road information with centimeter-level accuracy, essential for enabling precise navigation and safe operation of autonomous vehicles (AVs). Traditional offline construction methods involve several complex steps, such as data collection, point cloud generation, and feature extraction, but [...] Read more.

High-definition (HD) maps aim to provide detailed road information with centimeter-level accuracy, essential for enabling precise navigation and safe operation of autonomous vehicles (AVs). Traditional offline construction methods involve several complex steps, such as data collection, point cloud generation, and feature extraction, but these methods are resource-intensive and struggle to keep pace with the rapidly changing road environments. In contrast, online HD map construction leverages onboard sensor data to dynamically generate local HD maps, offering a bird’s-eye view (BEV) representation of the surrounding road environment. This approach has the potential to improve adaptability to spatial and temporal changes in road conditions while enhancing cost-efficiency by reducing the dependency on frequent map updates and expensive survey fleets. This survey provides a comprehensive analysis of online HD map construction, including the task background, high-level motivations, research methodology, key advancements, existing challenges, and future trends. We systematically review the latest advancements in three key sub-tasks: map segmentation, map element detection, and lane graph construction, aiming to bridge gaps in the current literature. We also discuss existing challenges and future trends, covering standardized map representation design, multitask learning, and multi-modality fusion, while offering suggestions for potential improvements. Full article

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))

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47 pages, 14403 KB

Open AccessEditor’s ChoiceReview

Chemical Detection Using Mobile Platforms and AI-Based Data Processing Technologies

by Daegwon Noh and Eunsoon Oh

J. Sens. Actuator Netw. 2025, 14(1), 6; https://doi.org/10.3390/jsan14010006 - 13 Jan 2025

Cited by 4 | Viewed by 3922

Abstract

The development of reliable gas sensors is very important in many fields such as safety, environment, and agriculture, and is especially essential for industrial waste and air pollution monitoring. As the performance of mobile platforms equipped with sensors such as smartphones and drones [...] Read more.

The development of reliable gas sensors is very important in many fields such as safety, environment, and agriculture, and is especially essential for industrial waste and air pollution monitoring. As the performance of mobile platforms equipped with sensors such as smartphones and drones and the technologies supporting them (wireless communication, battery performance, data processing technology, etc.) are spreading and improving, a lot of efforts are being made to perform these tasks by using portable systems such as smartphones or installing them on unmanned wireless platforms such as drones. For example, research is continuously being conducted on chemical sensors for field monitoring using smartphones and rapid monitoring of air pollution using unmanned aerial vehicles (UAVs). In this paper, we review the measurement results of various chemical sensors available on mobile platforms including drones and smartphones, and the analysis of detection results using machine learning. This topic covers a wide range of specialized fields such as materials engineering, aerospace engineering, physics, chemistry, environmental engineering, electrical engineering, and machine learning, and it is difficult for experts in one field to grasp the entire content. Therefore, we have explained various concepts with relatively simple pictures so that experts in various fields can comprehensively understand the overall topics. Full article

(This article belongs to the Section Big Data, Computing and Artificial Intelligence)

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35 pages, 2580 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of UAV-UGV Collaboration: Advancements and Challenges

by Isuru Munasinghe, Asanka Perera and Ravinesh C. Deo

J. Sens. Actuator Netw. 2024, 13(6), 81; https://doi.org/10.3390/jsan13060081 - 28 Nov 2024

Cited by 32 | Viewed by 18460

Abstract

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) have rapidly evolved, becoming integral to various applications such as environmental monitoring, disaster response, and precision agriculture. This paper provides a comprehensive review of the advancements and the challenges in UAV-UGV collaboration and its [...] Read more.

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) have rapidly evolved, becoming integral to various applications such as environmental monitoring, disaster response, and precision agriculture. This paper provides a comprehensive review of the advancements and the challenges in UAV-UGV collaboration and its potential applications. These systems offer enhanced situational awareness and operational efficiency, enabling complex tasks that are beyond the capabilities of individual systems by leveraging the complementary strengths of UAVs and UGVs. Key areas explored in this review include multi-UAV and multi-UGV systems, collaborative aerial and ground operations, and the communication and coordination mechanisms that support these collaborative efforts. Furthermore, this paper discusses potential limitations, challenges and future research directions, and considers issues such as computational constraints, communication network instability, and environmental adaptability. The review also provides a detailed analysis of how these issues impact the effectiveness of UAV-UGV collaboration. Full article

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30 pages, 4047 KB

Open AccessEditor’s ChoiceArticle

Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps

by Dong-Yun Kim, Akeem Bayo Kareem, Daryl Domingo, Baek-Cheon Shin and Jang-Wook Hur

J. Sens. Actuator Netw. 2024, 13(5), 60; https://doi.org/10.3390/jsan13050060 - 25 Sep 2024

Cited by 4 | Viewed by 4846

Abstract

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and [...] Read more.

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and signal stretching (SS), with advanced models, including Long Short-Term Memory (LSTM) networks, Autoencoders (AE), and Generative Adversarial Networks (GANs). Our approach significantly improves the robustness and accuracy of machine learning (ML) models for fault detection and classification. Key findings demonstrate a marked reduction in false positives and a substantial increase in fault detection rates, particularly in complex operational scenarios where traditional statistical methods may fall short. The experimental results underscore the effectiveness of combining these augmentation techniques, achieving up to a 30% improvement in fault detection accuracy and a 25% reduction in false positives compared to baseline models. These improvements highlight the practical value of the proposed framework in ensuring reliable operation and the predictive maintenance of centrifugal pumps in diverse industrial environments. Full article

(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)

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23 pages, 16203 KB

Open AccessEditor’s ChoiceArticle

Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications

by Luca Patanè, Francesca Sapuppo, Giuseppe Napoli and Maria Gabriella Xibilia

J. Sens. Actuator Netw. 2024, 13(5), 49; https://doi.org/10.3390/jsan13050049 - 27 Aug 2024

Cited by 7 | Viewed by 2111

Abstract

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary [...] Read more.

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data. Full article

(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)

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23 pages, 496 KB

Open AccessEditor’s ChoiceArticle

AI and Computing Horizons: Cloud and Edge in the Modern Era

by Nasif Fahmid Prangon and Jie Wu

J. Sens. Actuator Netw. 2024, 13(4), 44; https://doi.org/10.3390/jsan13040044 - 9 Aug 2024

Cited by 24 | Viewed by 8789

Abstract

Harnessing remote computation power over the Internet without the need for expensive hardware and making costly services available to mass users at a marginal cost gave birth to the concept of cloud computing. This survey provides a concise overview of the growing confluence [...] Read more.

Harnessing remote computation power over the Internet without the need for expensive hardware and making costly services available to mass users at a marginal cost gave birth to the concept of cloud computing. This survey provides a concise overview of the growing confluence of cloud computing, edge intelligence, and AI, with a focus on their revolutionary impact on the Internet of Things (IoT). The survey starts with a fundamental introduction to cloud computing, overviewing its key parts and the services offered by different service providers. We then discuss how AI is improving cloud capabilities through its indigenous apps and services and is creating a smarter cloud. We then focus on the impact of AI in one of the popular cloud paradigms called edge cloud and discuss AI on Edge and AI for Edge. We discuss how AI implementation on edge devices is transforming edge and IoT networks by pulling cognitive processing closer to where the data originates, improving efficiency and response. We also discuss major cloud providers and their service offerings within the ecosystem and their respective use cases. Finally, this research looks ahead at new trends and future scopes that are now becoming possible at the confluence of the cloud, edge computing, and AI in IoT. The purpose of this study is to demystify edge intelligence, including cloud computing, edge computing, and AI, and to focus on their synergistic role in taking IoT technologies to new heights. Full article

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26 pages, 2233 KB

Open AccessEditor’s ChoiceReview

Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for Smart Crop Management

by Fernando Fuentes-Peñailillo, Karen Gutter, Ricardo Vega and Gilda Carrasco Silva

J. Sens. Actuator Netw. 2024, 13(4), 39; https://doi.org/10.3390/jsan13040039 - 8 Jul 2024

Cited by 102 | Viewed by 15632

Abstract

This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production [...] Read more.

This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production. Full article

(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)

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21 pages, 14370 KB

Open AccessEditor’s ChoiceArticle

AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors

by Hovannes Kulhandjian, Jeremiah Barron, Megan Tamiyasu, Mateo Thompson and Michel Kulhandjian

J. Sens. Actuator Netw. 2024, 13(3), 34; https://doi.org/10.3390/jsan13030034 - 14 Jun 2024

Cited by 7 | Viewed by 4549

Abstract

In this paper, we present a pedestrian detection and avoidance scheme utilizing multi-sensor data collection and machine learning for intelligent transportation systems (ITSs). The system integrates a video camera, an infrared (IR) camera, and a micro-Doppler radar for data acquisition and training. A [...] Read more.

In this paper, we present a pedestrian detection and avoidance scheme utilizing multi-sensor data collection and machine learning for intelligent transportation systems (ITSs). The system integrates a video camera, an infrared (IR) camera, and a micro-Doppler radar for data acquisition and training. A deep convolutional neural network (DCNN) is employed to process RGB and IR images. The RGB dataset comprises 1200 images (600 with pedestrians and 600 without), while the IR dataset includes 1000 images (500 with pedestrians and 500 without), 85% of which were captured at night. Two distinct DCNNs were trained using these datasets, achieving a validation accuracy of 99.6% with the RGB camera and 97.3% with the IR camera. The radar sensor determines the pedestrian’s range and direction of travel. Experimental evaluations conducted in a vehicle demonstrated that the multi-sensor detection scheme effectively triggers a warning signal to a vibrating motor on the steering wheel and displays a warning message on the passenger’s touchscreen computer when a pedestrian is detected in potential danger. This system operates efficiently both during the day and at night. Full article

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))

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25 pages, 2165 KB

Open AccessEditor’s ChoiceArticle

A Sensor to Monitor Soil Moisture, Salinity, and Temperature Profiles for Wireless Networks

by Xavier Chavanne and Jean-Pierre Frangi

J. Sens. Actuator Netw. 2024, 13(3), 32; https://doi.org/10.3390/jsan13030032 - 27 May 2024

Cited by 5 | Viewed by 3051

Abstract

This article presents a wireless in situ sensor designed to continuously monitor profiles of parameters in porous media, such as soil moisture, salinity, and temperature. A review of existing in situ soil sensors reveals that it is the only device capable of measuring [...] Read more.

This article presents a wireless in situ sensor designed to continuously monitor profiles of parameters in porous media, such as soil moisture, salinity, and temperature. A review of existing in situ soil sensors reveals that it is the only device capable of measuring the complex permittivity of the medium, allowing for conversions into moisture and salinity that are independent of the instrument. Flow perturbation and invasiveness have also been minimized to maintain good representativeness. Plans include autonomous networks of such sensors, facilitated by the use of the recent radio mode LoRaWAN and cost optimizations for series production. Costs were reduced through electronic simplification and integration, and the use of low-cost modular sensing parts in soil, while still maintaining high measurement quality. A complete set of sensor data recorded during a three-month trial is also presented and interpreted. Full article

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32 pages, 684 KB

Open AccessEditor’s ChoiceReview

A Survey on Life-Cycle-Oriented Certificate Management in Industrial Networking Environments

by Julian Göppert, Andreas Walz and Axel Sikora

J. Sens. Actuator Netw. 2024, 13(2), 26; https://doi.org/10.3390/jsan13020026 - 10 Apr 2024

Cited by 5 | Viewed by 3229

Abstract

Driven by the Industry 4.0 paradigm and the resulting demand for connectivity in industrial networking, there is a convergence of formerly isolated operational technology and information technology networks. This convergence leads to attack surfaces on industrial networks. Therefore, a holistic approach of countermeasures [...] Read more.

Driven by the Industry 4.0 paradigm and the resulting demand for connectivity in industrial networking, there is a convergence of formerly isolated operational technology and information technology networks. This convergence leads to attack surfaces on industrial networks. Therefore, a holistic approach of countermeasures is needed to protect against cyber attacks. One element of these countermeasures is the use of certificate-based authentication for industrial components communicating on the field level. This in turn requires the management of certificates, private keys, and trust anchors in the communication endpoints. The work at hand surveys the topic of certificate management in industrial networking environments throughout their life cycle, from manufacturing until their disposal. To the best of the authors’ knowledge, there is no work yet that surveys the topic of certificate management in industrial networking environments. The work at hand considers contributions from research papers, industrial communication standards, and contributions that originate from the IT domain. In total, 2042 results from IEEE Xplore, Science Direct, Scopus, and Springer Link were taken into account. After applying inclusion and exclusion criteria and title, abstract, and full-text analysis, 20 contributions from research papers were selected. In addition to the presentation of their key contributions, the work at hand provides a synopsis that compares the overarching aspects. This comprises different proposed entity architectures, certificate management functions, involvement of different stakeholders, and consideration of life cycle stages. Finally, research gaps that are to be filled by further work are identified. While the topic of certificate management has already been addressed by the IT domain, its incorporation into industrial communication standards began significantly later and is still the subject of research work. Full article

(This article belongs to the Section Network Security and Privacy)

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38 pages, 4085 KB

Open AccessEditor’s ChoiceReview

Reliability of LoRaWAN Communications in Mining Environments: A Survey on Challenges and Design Requirements

by Sonile K. Musonda, Musa Ndiaye, Hastings M. Libati and Adnan M. Abu-Mahfouz

J. Sens. Actuator Netw. 2024, 13(1), 16; https://doi.org/10.3390/jsan13010016 - 9 Feb 2024

Cited by 14 | Viewed by 6357

Abstract

While a robust and reliable communication network for monitoring the mining environment in a timely manner to take care of people, the planet Earth and profits is key, the mining environment is very challenging in terms of achieving reliable wireless transmission. This survey [...] Read more.

While a robust and reliable communication network for monitoring the mining environment in a timely manner to take care of people, the planet Earth and profits is key, the mining environment is very challenging in terms of achieving reliable wireless transmission. This survey therefore investigates the reliability of LoRaWAN communication in the mining environment, identifying the challenges and design requirements. Bearing in mind that LoRaWAN is an IoT communication technology that has not yet been fully deployed in mining, the survey incorporates an investigation of LoRaWAN and other mining IoT communication technologies to determine their records of reliability, strengths and weaknesses and applications in mining. This aspect of the survey gives insight into the requirements of future mining IoT communication technologies and where LoRaWAN can be deployed in both underground and surface mining. Specific questions that the survey addresses are: (1) What is the record of reliability of LoRaWAN in mining environments? (2) What contributions have been made with regard to LoRa/LoRaWAN communication in general towards improving reliability? (3) What are the challenges and design requirements of LoRaWAN reliability in mining environments? (4) What research opportunities exist for achieving LoRaWAN communication in mining environments? In addition to recommending open research opportunities, the lessons learnt from the survey are also outlined. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data)

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24 pages, 1393 KB

Open AccessEditor’s ChoiceArticle

Network Sliced Distributed Learning-as-a-Service for Internet of Vehicles Applications in 6G Non-Terrestrial Network Scenarios

by David Naseh, Swapnil Sadashiv Shinde and Daniele Tarchi

J. Sens. Actuator Netw. 2024, 13(1), 14; https://doi.org/10.3390/jsan13010014 - 7 Feb 2024

Cited by 12 | Viewed by 3512

Abstract

In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, [...] Read more.

In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T/NTN vehicular scenario. Full article

(This article belongs to the Special Issue Advancing towards 6G Networks)

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24 pages, 518 KB

Open AccessEditor’s ChoiceArticle

On Maximizing the Probability of Achieving Deadlines in Communication Networks

by Benjamin Becker, Christian Oberli, Tobias Meuser and Ralf Steinmetz

J. Sens. Actuator Netw. 2024, 13(1), 9; https://doi.org/10.3390/jsan13010009 - 18 Jan 2024

Cited by 6 | Viewed by 2411

Abstract

We consider the problem of meeting deadline constraints in wireless communication networks. Fulfilling deadlines depends heavily on the routing algorithm used. We study this dependence generically for a broad class of routing algorithms. For analyzing the impact of routing decisions on deadline fulfillment, [...] Read more.

We consider the problem of meeting deadline constraints in wireless communication networks. Fulfilling deadlines depends heavily on the routing algorithm used. We study this dependence generically for a broad class of routing algorithms. For analyzing the impact of routing decisions on deadline fulfillment, we adopt a stochastic model from operations research to capture the source-to-destination delay distribution and the corresponding probability of successfully delivering data before a given deadline. Based on this model, we propose a decentralized algorithm that operates locally at each node and exchanges information solely with direct neighbors in order to determine the probabilities of achieving deadlines. A modified version of the algorithm also improves routing tables iteratively to progressively increase the deadline achievement probabilities. This modified algorithm is shown to deliver routing tables that maximize the deadline achievement probabilities for all nodes in a given network. We tested the approach by simulation and compared it with routing strategies based on established metrics, specifically the average delay, minimum hop count, and expected transmission count. Our evaluations encompass different channel quality and small-scale fading conditions, as well as various traffic load scenarios. Notably, our solution consistently outperforms the other approaches in all tested scenarios. Full article

(This article belongs to the Topic Electronic Communications, IOT and Big Data)

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18 pages, 2901 KB

Open AccessEditor’s ChoiceArticle

Experiences Using Ethereum and Quorum Blockchain Smart Contracts in Dairy Production

by Filisia Melissari, Andreas Papadakis, Dimitris Chatzitheodorou, Duc Tran, Joachim Schouteten, Georgia Athanasiou and Theodore Zahariadis

J. Sens. Actuator Netw. 2024, 13(1), 6; https://doi.org/10.3390/jsan13010006 - 12 Jan 2024

Cited by 11 | Viewed by 4657

Abstract

feta cheese is a Greek protected designation of origin (PDO) product that is produced in three main phases: milk collection, cheese preparation and maturation, and product packaging. Each phase must be aligned with quantitative rules, stemming from the legislation framework and best practices. [...] Read more.

feta cheese is a Greek protected designation of origin (PDO) product that is produced in three main phases: milk collection, cheese preparation and maturation, and product packaging. Each phase must be aligned with quantitative rules, stemming from the legislation framework and best practices. The production complexity, the increased production cost, centralised and monolithic traceability systems, and the lack of a systematic monitoring framework have made dairy products a commodity with increased frequency of food fraud. Given the context of the dairy section in Greece, this study aims to examine (a) whether it is possible to model the end-to-end process of PDO feta cheese considering production rules to develop a trustworthy blockchain-based traceability system (b) how to associate the (‘easy-to-retrieve’, operational) traceability data with the (difficult-to-assess) product characteristics meaningful to the consumer, (c) how to design a technical solution ensuring that information is accessible by the stakeholders and the consumer, while minimising blockchain-related delay, and (d) how to design a graphical user interface and offer tools to consumers so that traceability information is communicated effectively and they can verify it through access to the blockchain. In terms of methods, we analyse and model the process steps, identify measurable, operational parameters and translate the legislative framework into rules. These rules are designed and codified as blockchain smart contracts that ensure the food authenticity and compliance with legislation. The blockchain infrastructure consists of the private Quorum blockchain that is anchored to the public infrastructure of Ethereum. Mechanisms to address scalability in terms of dynamic data volumes, effective data coding, and data verification at the edge as well as relevant limitations are discussed. Consumers are informed about traceability information by using QR codes on food packaging and can verify the data using the blockchain tools and services. Full article

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27 pages, 7028 KB

Open AccessEditor’s ChoiceArticle

Performance Evaluation of LoRa Communications in Harsh Industrial Environments

by L’houssaine Aarif, Mohamed Tabaa and Hanaa Hachimi

J. Sens. Actuator Netw. 2023, 12(6), 80; https://doi.org/10.3390/jsan12060080 - 28 Nov 2023

Cited by 13 | Viewed by 6710

Abstract

LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an industrial environment, necessitating solutions [...] Read more.

LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an industrial environment, necessitating solutions to ensure reliable communication. This paper evaluates and compares LoRa’s performance in terms of packet error rate (PER) with and without forward error correction (FEC) in an industrial environment. The impact of integrating an infinite impulse response (IIR) or finite impulse response (FIR) filter into the LoRa architecture is also evaluated. Simulations are carried out in MATLAB at 868 MHz with a bandwidth of 125 kHz and two spreading factors of 7 and 12. Many-to-one and one-to-many communication modes are considered, as are line of sight (LOS) and non-line of Sight (NLOS) conditions. Simulation results show that, compared to an environment with additive white Gaussian noise (AWGN), LoRa technology suffers a significant degradation of its PER performance in industrial environments. Nevertheless, the use of forward error correction (FEC) contributes positively to offsetting this decline. Depending on the configuration and architecture examined, the gain in signal-to-noise ratio (SNR) using a 4/8 coding ratio ranges from 7 dB to 11 dB. Integrating IIR or FIR filters also boosts performance, with additional SNR gains ranging from 2 dB to 6 dB, depending on the simulation parameters. Full article

(This article belongs to the Section Communications and Networking)

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20 pages, 5774 KB

Open AccessEditor’s ChoiceArticle

A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network

by Giovanni Paragliola, Patrizia Ribino and Zaib Ullah

J. Sens. Actuator Netw. 2023, 12(6), 78; https://doi.org/10.3390/jsan12060078 - 20 Nov 2023

Cited by 3 | Viewed by 3230

Abstract

A result of the pandemic is an urgent need for data collaborations that empower the clinical and scientific communities in responding to rapidly evolving global challenges. The ICU4Covid project joined research institutions, medical centers, and hospitals all around Europe in a telemedicine network [...] Read more.

A result of the pandemic is an urgent need for data collaborations that empower the clinical and scientific communities in responding to rapidly evolving global challenges. The ICU4Covid project joined research institutions, medical centers, and hospitals all around Europe in a telemedicine network for sharing capabilities, knowledge, and expertise distributed within the network. However, healthcare data sharing has ethical, regulatory, and legal complexities that pose several restrictions on their access and use. To mitigate this issue, the ICU4Covid project integrates a federated learning architecture, allowing distributed machine learning within a cross-institutional healthcare system without the data being transported or exposed outside their original location. This paper presents the federated learning approach to support the decision-making process for ICU patients in a European telemedicine network. The proposed approach was applied to the early identification of high-risk hypertensive patients. Experimental results show how the knowledge of every single node is spread within the federation, improving the ability of each node to make an early prediction of high-risk hypertensive patients. Moreover, a performance evaluation shows an accuracy and precision of over 90%, confirming a good performance of the FL approach as a prediction test. The FL approach can significantly support the decision-making process for ICU patients in distributed networks of federated healthcare organizations. Full article

(This article belongs to the Special Issue Federated Learning: Applications and Future Directions)

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27 pages, 2451 KB

Open AccessEditor’s ChoiceArticle

Short-Range Localization via Bluetooth Using Machine Learning Techniques for Industrial Production Monitoring

by Francesco Di Rienzo, Alessandro Madonna, Nicola Carbonaro, Alessandro Tognetti, Antonio Virdis and Carlo Vallati

J. Sens. Actuator Netw. 2023, 12(5), 75; https://doi.org/10.3390/jsan12050075 - 15 Oct 2023

Cited by 1 | Viewed by 2998

Abstract

Indoor short-range localization is crucial in many Industry 4.0 applications. Production monitoring for assembly lines, for instance, requires fine-grained positioning for parts or goods in order to keep track of the production process and the stations traversed by each product. Due to the [...] Read more.

Indoor short-range localization is crucial in many Industry 4.0 applications. Production monitoring for assembly lines, for instance, requires fine-grained positioning for parts or goods in order to keep track of the production process and the stations traversed by each product. Due to the unavailability of the Global Positioning System (GPS) for indoor positioning, a different approach is required. In this paper, we propose a specific design for short-range indoor positioning based on the analysis of the Received Signal Strength Indicator (RSSI) of Bluetooth beacons. To this aim, different machine learning techniques are considered and assessed: regressors, Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). A realistic testbed is created to collect data for the training of the models and to assess the performance of each technique. Our analysis highlights the best models and the most convenient and suitable configuration for indoor localization. Finally, the localization accuracy is calculated in the considered use case, i.e., production monitoring. Our results show that the best performance is obtained using the K-Nearest Neighbors technique, which results in a good performance for general localization and in a high level of accuracy, 99%, for industrial production monitoring. Full article

(This article belongs to the Topic Intelligent Health Monitoring and Assistance Systems and Frameworks)

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29 pages, 6070 KB

Open AccessEditor’s ChoiceArticle

A Multi-Agent Intrusion Detection System Optimized by a Deep Reinforcement Learning Approach with a Dataset Enlarged Using a Generative Model to Reduce the Bias Effect

by Matthieu Mouyart, Guilherme Medeiros Machado and Jae-Yun Jun

J. Sens. Actuator Netw. 2023, 12(5), 68; https://doi.org/10.3390/jsan12050068 - 18 Sep 2023

Cited by 8 | Viewed by 4534

Abstract

Intrusion detection systems can defectively perform when they are adjusted with datasets that are unbalanced in terms of attack data and non-attack data. Most datasets contain more non-attack data than attack data, and this circumstance can introduce biases in intrusion detection systems, making [...] Read more.

Intrusion detection systems can defectively perform when they are adjusted with datasets that are unbalanced in terms of attack data and non-attack data. Most datasets contain more non-attack data than attack data, and this circumstance can introduce biases in intrusion detection systems, making them vulnerable to cyberattacks. As an approach to remedy this issue, we considered the Conditional Tabular Generative Adversarial Network (CTGAN), with its hyperparameters optimized using the tree-structured Parzen estimator (TPE), to balance an insider threat tabular dataset called the CMU-CERT, which is formed by discrete-value and continuous-value columns. We showed through this method that the mean absolute errors between the probability mass functions (PMFs) of the actual data and the PMFs of the data generated using the CTGAN can be relatively small. Then, from the optimized CTGAN, we generated synthetic insider threat data and combined them with the actual ones to balance the original dataset. We used the resulting dataset for an intrusion detection system implemented with the Adversarial Environment Reinforcement Learning (AE-RL) algorithm in a multi-agent framework formed by an attacker and a defender. We showed that the performance of detecting intrusions using the framework of the CTGAN and the AE-RL is significantly improved with respect to the case where the dataset is not balanced, giving an F1-score of 0.7617. Full article

(This article belongs to the Special Issue Machine-Environment Interaction, Volume II)

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16 pages, 5717 KB

Open AccessEditor’s ChoiceArticle

Remote Binaural System (RBS) for Noise Acoustic Monitoring

by Oscar Acosta, Luis Hermida, Marcelo Herrera, Carlos Montenegro, Elvis Gaona, Mateo Bejarano, Kevin Gordillo, Ignacio Pavón and Cesar Asensio

J. Sens. Actuator Netw. 2023, 12(4), 63; https://doi.org/10.3390/jsan12040063 - 14 Aug 2023

Cited by 3 | Viewed by 3777

Abstract

The recent emergence of advanced information technologies such as cloud computing, artificial intelligence, and data science has improved and optimized various processes in acoustics with potential real-world applications. Noise monitoring tasks on large terrains can be captured using an array of sound level [...] Read more.

The recent emergence of advanced information technologies such as cloud computing, artificial intelligence, and data science has improved and optimized various processes in acoustics with potential real-world applications. Noise monitoring tasks on large terrains can be captured using an array of sound level meters. However, current monitoring systems only rely on the knowledge of a singular measured value related to the acoustic energy of the captured signal, leaving aside spatial aspects that complement the perception of noise by the human being. This project presents a system that performs binaural measurements according to subjective human perception. The acoustic characterization in an anechoic chamber is presented, as well as acoustic indicators obtained in the field initially for a short period of time. The main contribution of this work is the construction of a binaural prototype that resembles the human head and which transmits and processes acoustical data on the cloud. The above allows noise level monitoring via binaural hearing rather than a singular capturing device. Likewise, it can be highlighted that the system allows for obtaining spatial acoustic indicators based on the interaural cross-correlation function (IACF), as well as detecting the location of the source on the azimuthal plane. Full article

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23 pages, 3763 KB

Open AccessEditor’s ChoiceArticle

Extraction of Hidden Authentication Factors from Possessive Information

by Nilobon Nanglae, Bello Musa Yakubu and Pattarasinee Bhattarakosol

J. Sens. Actuator Netw. 2023, 12(4), 62; https://doi.org/10.3390/jsan12040062 - 11 Aug 2023

Cited by 2 | Viewed by 2767

Abstract

Smartphones have emerged as a ubiquitous personal gadget that serve as a repository for individuals’ significant personal data. Consequently, both physiological and behavioral traits, which are classified as biometric technologies, are used in authentication systems in order to safeguard data saved on smartphones [...] Read more.

Smartphones have emerged as a ubiquitous personal gadget that serve as a repository for individuals’ significant personal data. Consequently, both physiological and behavioral traits, which are classified as biometric technologies, are used in authentication systems in order to safeguard data saved on smartphones from unauthorized access. Numerous authentication techniques have been developed; however, several authentication variables exhibit instability in the face of external influences or physical impairments. The potential failure of the authentication system might be attributed to several unpredictable circumstances. This research suggests that the use of distinctive and consistent elements over an individual’s lifespan may be employed to develop an authentication classification model. This model would be based on prevalent personal behavioral biometrics and could be readily implemented in security authentication systems. The biological biometrics acquired from an individual’s typing abilities during data entry include their name, surname, email, and phone number. Therefore, it is possible to establish and use a biometrics-based security system that can be sustained and employed during an individual’s lifetime without the explicit dependance on the functionality of the smartphone devices. The experimental findings demonstrate that the use of a mobile touchscreen as the foundation for the proposed verification mechanism has promise as a high-precision authentication solution. Full article

(This article belongs to the Special Issue Advances in Security of Cyber-Physical Systems)

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25 pages, 1365 KB

Open AccessEditor’s ChoiceArticle

Performance Assessment and Mitigation of Timing Covert Channels over the IEEE 802.15.4

by Ricardo Severino, João Rodrigues, João Alves and Luis Lino Ferreira

J. Sens. Actuator Netw. 2023, 12(4), 60; https://doi.org/10.3390/jsan12040060 - 1 Aug 2023

Cited by 6 | Viewed by 2572

Abstract

The fast development and adoption of IoT technologies has been enabling their application into increasingly sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices increases annually, they still present severe [...] Read more.

The fast development and adoption of IoT technologies has been enabling their application into increasingly sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices increases annually, they still present severe cyber-security vulnerabilities, becoming potential targets and entry points for further attacks. As these nodes become compromised, attackers aim to set up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes in a cloaked fashion, and network timing covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim to analyse the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol, particularly upon the DSME behaviour, one of the most promising for large-scale time critical communications. This enables us to better understand the involved risk of such threats and help support the development of active cyber-security mechanisms to mitigate these threats, which, for now, we provide in the form of practical network setup recommendations. Full article

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25 pages, 763 KB

Open AccessEditor’s ChoiceArticle

STARC: Decentralized Coordination Primitive on Low-Power IoT Devices for Autonomous Intersection Management

by Patrick Rathje, Valentin Poirot and Olaf Landsiedel

J. Sens. Actuator Netw. 2023, 12(4), 56; https://doi.org/10.3390/jsan12040056 - 11 Jul 2023

Cited by 1 | Viewed by 2214

Abstract

Wireless communication is an essential element within Intelligent Transportation Systems and motivates new approaches to intersection management, allowing safer and more efficient road usage. With lives at stake, wireless protocols should be readily available and guarantee safe coordination for all involved traffic participants, [...] Read more.

Wireless communication is an essential element within Intelligent Transportation Systems and motivates new approaches to intersection management, allowing safer and more efficient road usage. With lives at stake, wireless protocols should be readily available and guarantee safe coordination for all involved traffic participants, even in the presence of radio failures. This work introduces STARC, a coordination primitive for safe, decentralized resource coordination. Using STARC, traffic participants can safely coordinate at intersections despite unreliable radio environments and without a central entity or infrastructure. Unlike other methods that require costly and energy-consuming platforms, STARC utilizes affordable and efficient Internet of Things devices that connect cars, bicycles, electric scooters, pedestrians, and cyclists. For communication, STARC utilizes low-power IEEE 802.15.4 radios and Synchronous Transmissions for multi-hop communication. In addition, the protocol provides distributed transaction, election, and handover mechanisms for decentralized, thus cost-efficient, deployments. While STARC’s coordination remains resource-agnostic, this work presents and evaluates STARC in a roadside scenario. Our simulations have shown that using STARC at intersections leads to safer and more efficient vehicle coordination. We found that average waiting times can be reduced by up to 50% compared to using a fixed traffic light schedule in situations with fewer than 1000 vehicles per hour. Additionally, we design platooning on top of STARC, improving scalability and outperforming static traffic lights even at traffic loads exceeding 1000 vehicles per hour. Full article

(This article belongs to the Special Issue Recent Advances in Vehicular Networking and Communications)

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26 pages, 1075 KB

Open AccessEditor’s ChoiceReview

Recent Advances in Time-Sensitive Network Configuration Management: A Literature Review

by Boxin Shi, Xiaodong Tu, Bin Wu and Yifei Peng

J. Sens. Actuator Netw. 2023, 12(4), 52; https://doi.org/10.3390/jsan12040052 - 6 Jul 2023

Cited by 16 | Viewed by 6835

Abstract

At present, many network applications are seeking to implement Time-Sensitive Network (TSN) technology, which not only furnishes communication transmission services that are deterministic, low-latency, highly dependable, and have ample bandwidth, but also enables unified configuration management, permitting different network types to function under [...] Read more.

At present, many network applications are seeking to implement Time-Sensitive Network (TSN) technology, which not only furnishes communication transmission services that are deterministic, low-latency, highly dependable, and have ample bandwidth, but also enables unified configuration management, permitting different network types to function under a single management system. These characteristics enable it to be widely used in many fields such as industrial sensor and actuator networks, in-vehicle networks, data center networks, and edge computing. Nonetheless, TSN’s configuration management faces numerous difficulties and challenges related to network deployment, automated operation, and maintenance, as well as real-time and safety assurance, rendering it exceedingly intricate. In recent years, some studies have been conducted on TSN configuration management, encompassing various aspects such as system design, key technologies for configuration management, protocol enhancement, and application development. Nevertheless, there is a dearth of systematic summaries of these studies. Hence, this article aims to provide a comprehensive overview of TSN configuration management. Drawing upon more than 70 relevant publications and the pertinent standards established by the IEEE 802.1 TSN working group, we first introduce the system architecture of TSN configuration management from a macro perspective and then explore specific technical details. Additionally, we demonstrate its application scenarios through practical cases and finally highlight the challenges and future research directions. We aspire to provide a comprehensive reference for peers and new researchers interested in TSN configuration management. Full article

(This article belongs to the Special Issue Protocols, Algorithms and Applications for Time Sensitive Networks)

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57 pages, 5223 KB

Open AccessEditor’s ChoiceReview

DDoS Attack and Detection Methods in Internet-Enabled Networks: Concept, Research Perspectives, and Challenges

by Kazeem B. Adedeji, Adnan M. Abu-Mahfouz and Anish M. Kurien

J. Sens. Actuator Netw. 2023, 12(4), 51; https://doi.org/10.3390/jsan12040051 - 6 Jul 2023

Cited by 72 | Viewed by 30851

Abstract

In recent times, distributed denial of service (DDoS) has been one of the most prevalent security threats in internet-enabled networks, with many internet of things (IoT) devices having been exploited to carry out attacks. Due to their inherent security flaws, the attacks seek [...] Read more.

In recent times, distributed denial of service (DDoS) has been one of the most prevalent security threats in internet-enabled networks, with many internet of things (IoT) devices having been exploited to carry out attacks. Due to their inherent security flaws, the attacks seek to deplete the resources of the target network by flooding it with numerous spoofed requests from a distributed system. Research studies have demonstrated that a DDoS attack has a considerable impact on the target network resources and can result in an extended operational outage if not detected. The detection of DDoS attacks has been approached using a variety of methods. In this paper, a comprehensive survey of the methods used for DDoS attack detection on selected internet-enabled networks is presented. This survey aimed to provide a concise introductory reference for early researchers in the development and application of attack detection methodologies in IoT-based applications. Unlike other studies, a wide variety of methods, ranging from the traditional methods to machine and deep learning methods, were covered. These methods were classified based on their nature of operation, investigated as to their strengths and weaknesses, and then examined via several research studies which made use of each approach. In addition, attack scenarios and detection studies in emerging networks such as the internet of drones, routing protocol based IoT, and named data networking were also covered. Furthermore, technical challenges in each research study were identified. Finally, some remarks for enhancing the research studies were provided, and potential directions for future research were highlighted. Full article

(This article belongs to the Section Communications and Networking)

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35 pages, 785 KB

Open AccessEditor’s ChoiceReview

On Wireless Sensor Network Models: A Cross-Layer Systematic Review

by Fernando Ojeda, Diego Mendez, Arturo Fajardo and Frank Ellinger

J. Sens. Actuator Netw. 2023, 12(4), 50; https://doi.org/10.3390/jsan12040050 - 30 Jun 2023

Cited by 38 | Viewed by 9570

Abstract

Wireless sensor networks (WSNs) have been adopted in many fields of application, such as industrial, civil, smart cities, health, and the surveillance domain, to name a few. Fateway and sensor nodes conform to WSN, and each node integrates processor, communication, sensor, and power [...] Read more.

Wireless sensor networks (WSNs) have been adopted in many fields of application, such as industrial, civil, smart cities, health, and the surveillance domain, to name a few. Fateway and sensor nodes conform to WSN, and each node integrates processor, communication, sensor, and power supply modules, sending and receiving information of a covered area across a propagation medium. Given the increasing complexity of a WSN system, and in an effort to understand, comprehend and analyze an entire WSN, different metrics are used to characterize the performance of the network. To reduce the complexity of the WSN architecture, different approaches and techniques are implemented to capture (model) the properties and behavior of particular aspects of the system. Based on these WSN models, many research works propose solutions to the problem of abstracting and exporting network functionalities and capabilities to the final user. Modeling an entire WSN is a difficult task for researchers since they must consider all of the constraints that affect network metrics, devices and system administration, holistically, and the models developed in different research works are currently focused only on a specific network layer (physical, link, or transport layer), making the estimation of the WSN behavior a very difficult task. In this context, we present a systematic and comprehensive review focused on identifying the existing WSN models, classified into three main areas (node, network, and system-level) and their corresponding challenges. This review summarizes and analyzes the available literature, which allows for the general understanding of WSN modeling in a holistic view, using a proposed taxonomy and consolidating the research trends and open challenges in the area. Full article

(This article belongs to the Topic Wireless Sensor Networks)

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18 pages, 4821 KB

Open AccessEditor’s ChoiceArticle

The Power of Data: How Traffic Demand and Data Analytics Are Driving Network Evolution toward 6G Systems

by Dario Sabella, Davide Micheli and Giovanni Nardini

J. Sens. Actuator Netw. 2023, 12(4), 49; https://doi.org/10.3390/jsan12040049 - 27 Jun 2023

Cited by 10 | Viewed by 6009

Abstract

The evolution of communication systems always follows data traffic evolution and further influences innovations that are unlocking new markets and services. While 5G deployment is still ongoing in various countries, data-driven considerations (extracted from forecasts at the macroscopic level, detailed analysis of live [...] Read more.

The evolution of communication systems always follows data traffic evolution and further influences innovations that are unlocking new markets and services. While 5G deployment is still ongoing in various countries, data-driven considerations (extracted from forecasts at the macroscopic level, detailed analysis of live network traffic patterns, and specific measures from terminals) can conveniently feed insights suitable for many purposes (B2B e.g., operator planning and network management; plus also B2C e.g., smarter applications and AI-aided services) in the view of future 6G systems. Moreover, technology trends from standards and research projects (such as Hexa-X) are moving with industry efforts on this evolution. This paper shows the importance of data-driven insights, by first exploring network evolution across the years from a data point of view, and then by using global traffic forecasts complemented by data traffic extractions from a live 5G operator network (statistical network counters and measures from terminals) to draw some considerations on the possible evolution toward 6G. It finally presents a concrete case study showing how data collected from the live network can be exploited to help the design of AI operations and feed QoS predictions. Full article

(This article belongs to the Special Issue Advancing towards 6G Networks)

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20 pages, 3859 KB

Open AccessEditor’s ChoiceSystematic Review

Testbed Facilities for IoT and Wireless Sensor Networks: A Systematic Review

by Janis Judvaitis, Valters Abolins, Amr Elkenawy, Rihards Balass, Leo Selavo and Kaspars Ozols

J. Sens. Actuator Netw. 2023, 12(3), 48; https://doi.org/10.3390/jsan12030048 - 15 Jun 2023

Cited by 7 | Viewed by 4403

Abstract

As the popularity and complexity of WSN devices and IoT systems are increasing, the testing facilities should keep up. Yet, there is no comprehensive overview of the landscape of the testbed facilities conducted in a systematic manner. In this article, we provide a [...] Read more.

As the popularity and complexity of WSN devices and IoT systems are increasing, the testing facilities should keep up. Yet, there is no comprehensive overview of the landscape of the testbed facilities conducted in a systematic manner. In this article, we provide a systematic review of the availability and usage of testbed facilities published in scientific literature between 2011 and 2021, including 359 articles about testbeds and identifying 32 testbed facilities. The results of the review revealed what testbed facilities are available and identified several challenges and limitations in the use of the testbed facilities, including a lack of supportive materials and limited focus on debugging capabilities. The main contribution of this article is the description of how different metrics impact the uasge of testbed facilities, the review also highlights the importance of continued research and development in this field to ensure that testbed facilities continue to meet the changing needs of the ever-evolving IoT and WSN domains. Full article

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20 pages, 10861 KB

Open AccessEditor’s ChoiceArticle

Machine-Learning-Based Ground-Level Mobile Network Coverage Prediction Using UAV Measurements

by Naser Tarhuni, Ibtihal Al Saadi, Hafiz M. Asif, Mostefa Mesbah, Omer Eldirdiry and Abdulnasir Hossen

J. Sens. Actuator Netw. 2023, 12(3), 44; https://doi.org/10.3390/jsan12030044 - 26 May 2023

Cited by 2 | Viewed by 3617

Abstract

Future mobile network operators and telecommunications authorities aim to provide reliable network coverage. Signal strength, normally assessed using standard drive tests over targeted areas, is an important factor strongly linked to user satisfaction. Drive tests are, however, time-consuming, expensive, and can be dangerous [...] Read more.

Future mobile network operators and telecommunications authorities aim to provide reliable network coverage. Signal strength, normally assessed using standard drive tests over targeted areas, is an important factor strongly linked to user satisfaction. Drive tests are, however, time-consuming, expensive, and can be dangerous in hard-to-reach areas. An alternative safe method involves using drones or unmanned aerial vehicles (UAVs). The objective of this study was to use a drone to measure signal strength at discrete points a few meters above the ground and an artificial neural network (ANN) for processing the measured data and predicting signal strength at ground level. The drone was equipped with low-cost data logging equipment. The ANN was also used to classify specific ground locations in terms of signal coverage into poor, fair, good, and excellent. The data used in training and testing the ANN were collected by a measurement unit attached to a drone in different areas of Sultan Qaboos University campus in Muscat, Oman. A total of 12 locations with different topologies were scanned. The proposed method achieved an accuracy of 97% in predicting the ground level coverage based on measurements taken at higher altitudes. In addition, the performance of the ANN in predicting signal strength at ground level was evaluated using several test scenarios, achieving less than 3% mean square error (MSE). Additionally, data taken at different angles with respect to the vertical were also tested, and the prediction MSE was found to be less than approximately 3% for an angle of 68 degrees. Additionally, outdoor measurements were used to predict indoor coverage with an MSE of less than approximately 6%. Furthermore, in an attempt to find a globally accurate ANN module for the targeted area, all zones’ measurements were cross-tested on ANN modules trained for different zones. It was evaluated that, within the tested scenarios, an MSE of less than approximately 10% can be achieved with an ANN module trained on data from only one zone. Full article

(This article belongs to the Section Big Data, Computing and Artificial Intelligence)

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25 pages, 4161 KB

Open AccessEditor’s ChoiceArticle

Smart Automotive Diagnostic and Performance Analysis Using Blockchain Technology

by Ahmed Mohsen Yassin, Heba Kamal Aslan and Islam Tharwat Abdel Halim

J. Sens. Actuator Netw. 2023, 12(2), 32; https://doi.org/10.3390/jsan12020032 - 7 Apr 2023

Cited by 7 | Viewed by 5162

Abstract

The automotive industry currently is seeking to increase remote connectivity to a vehicle, which creates a high demand to implement a secure way of connecting vehicles, as well as verifying and storing their data in a trusted way. Furthermore, much information must be [...] Read more.

The automotive industry currently is seeking to increase remote connectivity to a vehicle, which creates a high demand to implement a secure way of connecting vehicles, as well as verifying and storing their data in a trusted way. Furthermore, much information must be leaked in order to correctly diagnose the vehicle and determine when or how to remotely update it. In this context, we propose a Blockchain-based, fully automated remote vehicle diagnosis system. The proposed system provides a secure and trusted way of storing and verifying vehicle data and analyzing their performance in different environments. Furthermore, we discuss many aspects of the benefits to different parties, such as the vehicle’s owner and manufacturers. Furthermore, a performance evaluation via simulation was performed on the proposed system using MATLAB Simulink to simulate both the vehicles and Blockchain and give a prototype for the system’s structure. In addition, OMNET++ was used to measure the expected system’s storage and throughput given some fixed parameters, such as sending the periodicity and speed. The simulation results showed that the throughput, end-to-end delay, and power consumption increased as the number of vehicles increased. In general, Original Equipment Manufacturers (OEMs) can implement this system by taking into consideration either increasing the storage to add more vehicles or decreasing the sending frequency to allow more vehicles to join. By and large, the proposed system is fully dynamic, and its configuration can be adjusted to satisfy the OEM’s needs since there are no specific constraints while implementing it. Full article

(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Cyber Security in the Era of IoT and IIoT Applications)

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31 pages, 33015 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of IoT Networking Technologies for Smart Home Automation Applications

by Vasilios A. Orfanos, Stavros D. Kaminaris, Panagiotis Papageorgas, Dimitrios Piromalis and Dionisis Kandris

J. Sens. Actuator Netw. 2023, 12(2), 30; https://doi.org/10.3390/jsan12020030 - 3 Apr 2023

Cited by 63 | Viewed by 15256

Abstract

The expediential increase in Internet communication technologies leads to its expansion to interests beyond computer networks. MEMS (Micro Electro Mechanical Systems) can now be smaller with higher performance, leading to tiny sensors and actuators with enhanced capabilities. WSN (Wireless Sensor Networks) and IoT [...] Read more.

The expediential increase in Internet communication technologies leads to its expansion to interests beyond computer networks. MEMS (Micro Electro Mechanical Systems) can now be smaller with higher performance, leading to tiny sensors and actuators with enhanced capabilities. WSN (Wireless Sensor Networks) and IoT (Internet of Things) have become a way for devices to communicate, share their data, and control them remotely. Machine-to-Machine (M2M) scenarios can be easily implemented as the cost of the components needed in that network is now affordable. Some of these solutions seem to be more affordable but lack important features, while other ones provide them but at a higher cost. Furthermore, there are ones that can cover great distances and surpass the limits of a Smart Home, while others are more specialized for operation in small areas. As there is a variety of choices available, a more consolidated view of their characteristics is needed to figure out the pros and cons of each of these technologies. As there are a great number of technologies examined in this paper, they are presented regarding their connectivity: Wired, Wireless, and Dual mode (Wired and Wireless). Their oddities are examined with metrics based on user interaction, technical characteristics, data integrity, and cost factor. In the last part of this article, a comparison of these technologies is presented as an effort to assist home automation users, administrators, or installers in making the right choice among them. Full article

(This article belongs to the Special Issue Applications of Wireless Sensor Networks: The State of the Art and Future Trends)

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19 pages, 3051 KB

Open AccessEditor’s ChoiceArticle

Intrusion Detection System Using Feature Extraction with Machine Learning Algorithms in IoT

by Dhiaa Musleh, Meera Alotaibi, Fahd Alhaidari, Atta Rahman and Rami M. Mohammad

J. Sens. Actuator Netw. 2023, 12(2), 29; https://doi.org/10.3390/jsan12020029 - 29 Mar 2023

Cited by 129 | Viewed by 10450

Abstract

With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is [...] Read more.

With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is becoming necessary. Machine learning (ML) is one of the promising techniques as a smart IDS in different areas, including IoT. However, the input to ML models should be extracted from the IoT environment by feature extraction models, which play a significant role in the detection rate and accuracy. Therefore, this research aims to introduce a study on ML-based IDS in IoT, considering different feature extraction algorithms with several ML models. This study evaluated several feature extractors, including image filters and transfer learning models, such as VGG-16 and DenseNet. Additionally, several machine learning algorithms, including random forest, K-nearest neighbors, SVM, and different stacked models were assessed considering all the explored feature extraction algorithms. The study presented a detailed evaluation of all combined models using the IEEE Dataport dataset. Results showed that VGG-16 combined with stacking resulted in the highest accuracy of 98.3%. Full article

(This article belongs to the Special Issue Blockchain and Artificial Intelligence for Cyber Security in the Era of IoT and IIoT Applications)

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27 pages, 11909 KB

Open AccessEditor’s ChoiceArticle

A Novel Multi Algorithm Approach to Identify Network Anomalies in the IoT Using Fog Computing and a Model to Distinguish between IoT and Non-IoT Devices

by Rami J. Alzahrani and Ahmed Alzahrani

J. Sens. Actuator Netw. 2023, 12(2), 19; https://doi.org/10.3390/jsan12020019 - 28 Feb 2023

Cited by 27 | Viewed by 4167

Abstract

Botnet attacks, such as DDoS, are one of the most common types of attacks in IoT networks. A botnet is a collection of cooperated computing machines or Internet of Things gadgets that criminal users manage remotely. Several strategies have been developed to reduce [...] Read more.

Botnet attacks, such as DDoS, are one of the most common types of attacks in IoT networks. A botnet is a collection of cooperated computing machines or Internet of Things gadgets that criminal users manage remotely. Several strategies have been developed to reduce anomalies in IoT networks, such as DDoS. To increase the accuracy of the anomaly mitigation system and lower the false positive rate (FPR), some schemes use statistical or machine learning methodologies in the anomaly-based intrusion detection system (IDS) to mitigate an attack. Despite the proposed anomaly mitigation techniques, the mitigation of DDoS attacks in IoT networks remains a concern. Because of the similarity between DDoS and normal network flows, leading to problems such as a high FPR, low accuracy, and a low detection rate, the majority of anomaly mitigation methods fail. Furthermore, the limited resources in IoT devices make it difficult to implement anomaly mitigation techniques. In this paper, an efficient anomaly mitigation system has been developed for the IoT network through the design and implementation of a DDoS attack detection system that uses a statistical method that combines three algorithms: exponentially weighted moving average (EWMA), K-nearest neighbors (KNN), and the cumulative sum algorithm (CUSUM). The integration of fog computing with the Internet of Things has created an effective framework for implementing an anomaly mitigation strategy to address security issues such as botnet threats. The proposed module was evaluated using the Bot-IoT dataset. From the results, we conclude that our model has achieved a high accuracy (99.00%) with a low false positive rate (FPR). We have also achieved good results in distinguishing between IoT and non-IoT devices, which will help networking teams make the distinction as well. Full article

(This article belongs to the Topic Internet of Things: Latest Advances)

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18 pages, 708 KB

Open AccessEditor’s ChoiceArticle

Building Trusted Federated Learning: Key Technologies and Challenges

by Depeng Chen, Xiao Jiang, Hong Zhong and Jie Cui

J. Sens. Actuator Netw. 2023, 12(1), 13; https://doi.org/10.3390/jsan12010013 - 6 Feb 2023

Cited by 15 | Viewed by 6121

Abstract

Federated learning (FL) provides convenience for cross-domain machine learning applications and has been widely studied. However, the original FL is still vulnerable to poisoning and inference attacks, which will hinder the landing application of FL. Therefore, it is essential to design a trustworthy [...] Read more.

Federated learning (FL) provides convenience for cross-domain machine learning applications and has been widely studied. However, the original FL is still vulnerable to poisoning and inference attacks, which will hinder the landing application of FL. Therefore, it is essential to design a trustworthy federation learning (TFL) to eliminate users’ anxiety. In this paper, we aim to provide a well-researched picture of the security and privacy issues in FL that can bridge the gap to TFL. Firstly, we define the desired goals and critical requirements of TFL, observe the FL model from the perspective of the adversaries and extrapolate the roles and capabilities of potential adversaries backward. Subsequently, we summarize the current mainstream attack and defense means and analyze the characteristics of the different methods. Based on a priori knowledge, we propose directions for realizing the future of TFL that deserve attention. Full article

(This article belongs to the Topic Trends and Prospects in Security, Encryption and Encoding)

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18 pages, 661 KB

Open AccessEditor’s ChoiceReview

Practical Challenges of Attack Detection in Microgrids Using Machine Learning

by Daniel T. Ramotsoela, Gerhard P. Hancke and Adnan M. Abu-Mahfouz

J. Sens. Actuator Netw. 2023, 12(1), 7; https://doi.org/10.3390/jsan12010007 - 18 Jan 2023

Cited by 17 | Viewed by 4330

Abstract

The move towards renewable energy and technological advancements in the generation, distribution and transmission of electricity have increased the popularity of microgrids. The popularity of these decentralised applications has coincided with advancements in the field of telecommunications allowing for the efficient implementation of [...] Read more.

The move towards renewable energy and technological advancements in the generation, distribution and transmission of electricity have increased the popularity of microgrids. The popularity of these decentralised applications has coincided with advancements in the field of telecommunications allowing for the efficient implementation of these applications. This convenience has, however, also coincided with an increase in the attack surface of these systems, resulting in an increase in the number of cyber-attacks against them. Preventative network security mechanisms alone are not enough to protect these systems as a critical design feature is system resilience, so intrusion detection and prevention system are required. The practical consideration for the implementation of the proposed schemes in practice is, however, neglected in the literature. This paper attempts to address this by generalising these considerations and using the lessons learned from water distribution systems as a case study. It was found that the considerations are similar irrespective of the application environment even though context-specific information is a requirement for effective deployment. Full article

(This article belongs to the Section Network Services and Applications)

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50 pages, 1152 KB

Open AccessEditor’s ChoiceReview

AI-Based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions

by Reem A. Alzahrani and Malak Aljabri

J. Sens. Actuator Netw. 2023, 12(1), 4; https://doi.org/10.3390/jsan12010004 - 31 Dec 2022

Cited by 29 | Viewed by 20052

Abstract

Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements [...] Read more.

Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements are artificially inflated in click fraud. Typical pay-per-click advertisements charge a fee for each click, assuming that a potential customer was drawn to the ad. Click fraud attackers create the illusion that a significant number of possible customers have clicked on an advertiser’s link by an automated script, a computer program, or a human. Nevertheless, advertisers are unlikely to profit from these clicks. Fraudulent clicks may be involved to boost the revenues of an ad hosting site or to spoil an advertiser’s budget. Several notable attempts to detect and prevent this form of fraud have been undertaken. This study examined all methods developed and published in the previous 10 years that primarily used artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for the detection and prevention of click fraud. Features that served as input to train models for classifying ad clicks as benign or fraudulent, as well as those that were deemed obvious and with critical evidence of click fraud, were identified, and investigated. Corresponding insights and recommendations regarding click fraud detection using AI approaches were provided. Full article

(This article belongs to the Special Issue Feature Papers in Network Security and Privacy)

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29 pages, 584 KB

Open AccessEditor’s ChoiceArticle

A Survey on Integrated Sensing, Communication, and Computing Networks for Smart Oceans

by Minghui Dai, Yang Li, Peichun Li, Yuan Wu, Liping Qian, Bin Lin and Zhou Su

J. Sens. Actuator Netw. 2022, 11(4), 70; https://doi.org/10.3390/jsan11040070 - 26 Oct 2022

Cited by 23 | Viewed by 6816

Abstract

The smart ocean has been regarded as an integrated sensing, communication, and computing ecosystem developed for connecting marine objects in surface and underwater environments. The development of the smart ocean is expected to support a variety of marine applications and services such as [...] Read more.

The smart ocean has been regarded as an integrated sensing, communication, and computing ecosystem developed for connecting marine objects in surface and underwater environments. The development of the smart ocean is expected to support a variety of marine applications and services such as resource exploration, marine disaster rescuing, and environment monitoring. However, the complex and dynamic marine environments and the limited network resources raise new challenges in marine communication and computing, especially for these computing-intensive and delay-sensitive tasks. Recently, the space–air–ground–sea integrated networks have been envisioned as a promising network framework to enhance the communication and computing performance. In this paper, we conduct a comprehensive survey on the integrated sensing, communication, and computing networks (ISCCNs) for smart oceans based on the collaboration of space–air–ground–sea networks from four domains (i.e., space layer, aerial layer, sea surface layer, and underwater layer), and five aspects (i.e., sensing-related, communication-related, computation-related, security-related, and application-related). Specifically, we provide the key technologies for the ISCCNs in smart oceans, and introduce the state-of-the-art marine sensing, communication, and computing paradigms. The emerging challenges with the potential solutions of the ISCCNs for smart oceans are illustrated to enable the intelligent services. Moreover, the new applications for the ISCCNs in smart oceans are discussed, and potential research directions in smart oceans are provided for future works. Full article

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34 pages, 6494 KB

Open AccessEditor’s ChoiceReview

Wireless Body Area Network (WBAN): A Survey on Architecture, Technologies, Energy Consumption, and Security Challenges

by Mohammad Yaghoubi, Khandakar Ahmed and Yuan Miao

J. Sens. Actuator Netw. 2022, 11(4), 67; https://doi.org/10.3390/jsan11040067 - 18 Oct 2022

Cited by 83 | Viewed by 29683

Abstract

Wireless body area networks (WBANs) are a new advance utilized in recent years to increase the quality of human life by monitoring the conditions of patients inside and outside hospitals, the activities of athletes, military applications, and multimedia. WBANs consist of intelligent micro- [...] Read more.

Wireless body area networks (WBANs) are a new advance utilized in recent years to increase the quality of human life by monitoring the conditions of patients inside and outside hospitals, the activities of athletes, military applications, and multimedia. WBANs consist of intelligent micro- or nano-sensors capable of processing and sending information to the base station (BS). Sensors embedded in the bodies of individuals can enable vital information exchange over wireless communication. Network forming of these sensors envisages long-term medical care without restricting patients’ normal daily activities as part of diagnosing or caring for a patient with a chronic illness or monitoring the patient after surgery to manage emergencies. This paper reviews WBAN, its security challenges, body sensor network architecture and functions, and communication technologies. The work reported in this paper investigates a significant security-level challenge existing in WBAN. Lastly, it highlights various mechanisms for increasing security and decreasing energy consumption. Full article

(This article belongs to the Section Actuators, Sensors and Devices)

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30 pages, 1038 KB

Open AccessEditor’s ChoiceArticle

Improving the Performance of Opportunistic Networks in Real-World Applications Using Machine Learning Techniques

by Samaneh Rashidibajgan and Thomas Hupperich

J. Sens. Actuator Netw. 2022, 11(4), 61; https://doi.org/10.3390/jsan11040061 - 26 Sep 2022

Cited by 7 | Viewed by 3882

Abstract

In Opportunistic Networks, portable devices such as smartphones, tablets, and wearables carried by individuals, can communicate and save-carry-forward their messages. The message transmission is often in the short range supported by communication protocols, such as Bluetooth, Bluetooth Low Energy, and Zigbee. These devices [...] Read more.

In Opportunistic Networks, portable devices such as smartphones, tablets, and wearables carried by individuals, can communicate and save-carry-forward their messages. The message transmission is often in the short range supported by communication protocols, such as Bluetooth, Bluetooth Low Energy, and Zigbee. These devices carried by individuals along with a city’s taxis and buses represent network nodes. The mobility, buffer size, message interval, number of nodes, and number of messages copied in such a network influence the network’s performance. Extending these factors can improve the delivery of the messages and, consequently, network performance; however, due to the limited network resources, it increases the cost and appends the network overhead. The network delivers the maximized performance when supported by the optimal factors. In this paper, we measured, predicted, and analyzed the impact of these factors on network performance using the Opportunistic Network Environment simulator and machine learning techniques. We calculated the optimal factors depending on the network features. We have used three datasets, each with features and characteristics reflecting different network structures. We collected the real-time GPS coordinates of 500 taxis in San Francisco, 320 taxis in Rome, and 196 public transportation buses in Münster, Germany, within 48 h. We also compared the network performance without selfish nodes and with

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selfish nodes. We suggested the optimized configuration under real-world conditions when resources are limited. In addition, we compared the performance of Epidemic, Prophet, and PPHB++ routing algorithms fed with the optimized factors. The results show how to consider the best settings for the network according to the needs and how self-sustaining nodes will affect network performance. Full article

(This article belongs to the Topic Wireless Sensor Networks)

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46 pages, 2308 KB

Open AccessEditor’s ChoiceReview

Mobile Edge Computing in Space-Air-Ground Integrated Networks: Architectures, Key Technologies and Challenges

by Yuan Qiu, Jianwei Niu, Xinzhong Zhu, Kuntuo Zhu, Yiming Yao, Beibei Ren and Tao Ren

J. Sens. Actuator Netw. 2022, 11(4), 57; https://doi.org/10.3390/jsan11040057 - 22 Sep 2022

Cited by 35 | Viewed by 10593

Abstract

Space-air-ground integrated networks (SAGIN) provide seamless global coverage and cross-domain interconnection for the ubiquitous users in heterogeneous networks, which greatly promote the rapid development of intelligent mobile devices and applications. However, for mobile devices with limited computation capability and energy budgets, it is [...] Read more.

Space-air-ground integrated networks (SAGIN) provide seamless global coverage and cross-domain interconnection for the ubiquitous users in heterogeneous networks, which greatly promote the rapid development of intelligent mobile devices and applications. However, for mobile devices with limited computation capability and energy budgets, it is still a serious challenge to meet the stringent delay and energy requirements of computation-intensive ubiquitous mobile applications. Therefore, in view of the significant success in ground mobile networks, the introduction of mobile edge computing (MEC) in SAGIN has become a promising technology to solve the challenge. By deploying computing, cache, and communication resources in the edge of mobile networks, SAGIN MEC provides both low latency, high bandwidth, and wide coverage, substantially improving the quality of services for mobile applications. There are still many unprecedented challenges, due to its high dynamic, heterogeneous and complex time-varying topology. Therefore, efficient MEC deployment, resource management, and scheduling optimization in SAGIN are of great significance. However, most existing surveys only focus on either the network architecture and system model, or the analysis of specific technologies of computation offloading, without a complete description of the key MEC technologies for SAGIN. Motivated by this, this paper first presents a SAGIN network system architecture and service framework, followed by the descriptions of its characteristics and advantages. Then, the MEC deployment, network resources, edge intelligence, optimization objectives and key algorithms in SAGIN are discussed in detail. Finally, potential problems and challenges of MEC in SAGIN are discussed for future work. Full article

(This article belongs to the Special Issue Edge Computing for the Internet of Things (IoT))

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31 pages, 29045 KB

Open AccessEditor’s ChoiceArticle

Perception Enhancement and Improving Driving Context Recognition of an Autonomous Vehicle Using UAVs

by Abderraouf Khezaz, Manolo Dulva Hina and Amar Ramdane-Cherif

J. Sens. Actuator Netw. 2022, 11(4), 56; https://doi.org/10.3390/jsan11040056 - 20 Sep 2022

Cited by 4 | Viewed by 3740

Abstract

The safety of various road users and vehicle passengers is very important in our increasingly populated roads and highways. To this end, the correct perception of driving conditions is imperative for a driver to react accordingly to a given driving situation. Various sensors [...] Read more.

The safety of various road users and vehicle passengers is very important in our increasingly populated roads and highways. To this end, the correct perception of driving conditions is imperative for a driver to react accordingly to a given driving situation. Various sensors are currently being used in recognizing driving context. To further enhance such driving environment perception, this paper proposes the use of UAVs (unmanned aerial vehicles, also known as drones). In this work, drones are equipped with sensors (radar, lidar, camera, etc.), capable of detecting obstacles, accidents, and the like. Due to their small size and capability to move places, drones can be used collect perception data and transmit them to the vehicle using a secure method, such as an RF, VLC, or hybrid communication protocol. These data obtained from different sources are then combined and processed using a knowledge base and some set of logical rules. The knowledge base is represented by ontology; it contains various logical rules related to the weather, the appropriateness of sensors with respect to the weather, and the activation mechanism of UAVs containing these sensors. Logical rules about which communication protocols to use also exist. Finally, various driving context cognition rules are provided. The result is a more reliable environment perception for the vehicle. When necessary, users are provided with driving assistance information, leading to safe driving and fewer road accidents. As a proof of concept, various use cases were tested in a driving simulator in the laboratory. Experimental results show that the system is an effective tool in improving driving context recognition and in preventing road accidents. Full article

(This article belongs to the Topic Intelligent Transportation Systems)

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24 pages, 1392 KB

Open AccessEditor’s ChoiceReview

A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization

by Teng Long, Zhangbing Zhou, Gerhard Hancke, Yang Bai and Qi Gao

J. Sens. Actuator Netw. 2022, 11(3), 50; https://doi.org/10.3390/jsan11030050 - 29 Aug 2022

Cited by 31 | Viewed by 12207

Abstract

Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural [...] Read more.

Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification. Full article

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50 pages, 2628 KB

Open AccessEditor’s ChoiceArticle

Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

by Daisy Nkele Molokomme, Adeiza James Onumanyi and Adnan M. Abu-Mahfouz

J. Sens. Actuator Netw. 2022, 11(3), 47; https://doi.org/10.3390/jsan11030047 - 21 Aug 2022

Cited by 44 | Viewed by 9389

Abstract

The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended [...] Read more.

The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs. Full article

(This article belongs to the Section Network Services and Applications)

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36 pages, 612 KB

Open AccessFeature PaperEditor’s ChoiceReview

Blockchain as IoT Economy Enabler: A Review of Architectural Aspects

by Diego Pennino, Maurizio Pizzonia, Andrea Vitaletti and Marco Zecchini

J. Sens. Actuator Netw. 2022, 11(2), 20; https://doi.org/10.3390/jsan11020020 - 29 Mar 2022

Cited by 26 | Viewed by 6151

Abstract

In the IoT-based economy, a large number of subjects (companies, public bodies, or private citizens) are willing to buy data or services offered by subjects that provide, operate, or host IoT devices. To support economic transactions in this setting, and to pave the [...] Read more.

In the IoT-based economy, a large number of subjects (companies, public bodies, or private citizens) are willing to buy data or services offered by subjects that provide, operate, or host IoT devices. To support economic transactions in this setting, and to pave the way for the implementation of decentralized algorithmic governance powered by smart contracts, the adoption of the blockchain has been proposed both in scientific literature and in actual projects. The blockchain technology promises a decentralized payment system independent of (and possibly cheaper than) conventional electronic payment systems. However, there are a number of aspects that need to be considered for an effective IoT–blockchain integration. In this review paper, we start from a number of real IoT projects and applications that (may) take advantage of blockchain technology to support economic transactions. We provide a reasoned review of several architectural choices in light of typical requirements of those applications and discuss their impact on transaction throughput, latency, costs, limits on ecosystem growth, and so on. We also provide a survey of additional financial tools that a blockchain can potentially bring to an IoT ecosystem, with their architectural impact. In the end, we observe that there are very few examples of IoT projects that fully exploit the potential of the blockchain. We conclude with a discussion of open problems and future research directions to make blockchain adoption easier and more effective for supporting an IoT economy. Full article

(This article belongs to the Special Issue Journal of Sensor and Actuator Networks: 10th Year Anniversary)

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31 pages, 544 KB

Open AccessFeature PaperEditor’s ChoiceArticle

A Survey of Outlier Detection Techniques in IoT: Review and Classification

by Mustafa Al Samara, Ismail Bennis, Abdelhafid Abouaissa and Pascal Lorenz

J. Sens. Actuator Netw. 2022, 11(1), 4; https://doi.org/10.3390/jsan11010004 - 4 Jan 2022

Cited by 77 | Viewed by 11255

Abstract

The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station. However, these [...] Read more.

The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station. However, these data are susceptible to different factors that may affect the collected data efficiency or the network functioning, and therefore the desired quality of service (QoS). In this context, one of the main issues requiring more research and adapted solutions is the outlier detection problem. The challenge is to detect outliers and classify them as either errors to be ignored, or important events requiring actions to prevent further service degradation. In this paper, we propose a comprehensive literature review of recent outlier detection techniques used in the IoTs context. First, we provide the fundamentals of outlier detection while discussing the different sources of an outlier, the existing approaches, how we can evaluate an outlier detection technique, and the challenges facing designing such techniques. Second, comparison and discussion of the most recent outlier detection techniques are presented and classified into seven main categories, which are: statistical-based, clustering-based, nearest neighbour-based, classification-based, artificial intelligent-based, spectral decomposition-based, and hybrid-based. For each category, available techniques are discussed, while highlighting the advantages and disadvantages of each of them. The related works for each of them are presented. Finally, a comparative study for these techniques is provided. Full article

(This article belongs to the Special Issue Journal of Sensor and Actuator Networks: 10th Year Anniversary)

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