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17 pages, 3264 KiB

Open AccessArticle

Risk-Based System-Call Sequence Grouping Method for Malware Intrusion Detection

by Tolvinas Vyšniūnas, Dainius Čeponis, Nikolaj Goranin and Antanas Čenys

Electronics 2024, 13(1), 206; https://doi.org/10.3390/electronics13010206 - 2 Jan 2024

Viewed by 1364

Abstract

Malware intrusion is a serious threat to cybersecurity; that is why new and innovative methods are constantly being developed to detect and prevent it. This research focuses on malware intrusion detection through the usage of system calls and machine learning. An effective and [...] Read more.

Malware intrusion is a serious threat to cybersecurity; that is why new and innovative methods are constantly being developed to detect and prevent it. This research focuses on malware intrusion detection through the usage of system calls and machine learning. An effective and clearly described system-call grouping method could increase the various metrics of machine learning methods, thereby improving the malware detection rate in host-based intrusion-detection systems. In this article, a risk-based system-call sequence grouping method is proposed that assigns riskiness values from low to high based on function risk value. The application of the newly proposed grouping method improved classification accuracy by 23.4% and 7.6% with the SVM and DT methods, respectively, compared to previous results obtained on the same methods and data. The results suggest the use of lightweight machine learning methods for malware attack can ensure detection accuracy comparable to deep learning methods. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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24 pages, 9608 KiB

Open AccessArticle

Deploying a Low-Cost Wi-Fi-Based Vehicular Ad Hoc Network in a Shopping Mall Parking Lot: An Empirical Study

by Nurul I. Sarkar, Foysal Ahmed and Sonia Gul

Electronics 2023, 12(22), 4672; https://doi.org/10.3390/electronics12224672 - 16 Nov 2023

Viewed by 1456

Abstract

Vehicular ad hoc networks (VANETs) have the potential to reduce car accidents by facilitating connectivity and warning message exchange between vehicles, both on roads and in parking lots. This research endeavored to accomplish three primary goals: conducting a field measurement in the parking [...] Read more.

Vehicular ad hoc networks (VANETs) have the potential to reduce car accidents by facilitating connectivity and warning message exchange between vehicles, both on roads and in parking lots. This research endeavored to accomplish three primary goals: conducting a field measurement in the parking lot of a large shopping mall in Auckland, developing an OPNET-based simulation model to analyze and validate the system performance, and analyzing the compatibility between five selected radio propagation models (Free-space, Shadowing Path-loss, Egli, Hata, and COST231). These models were selected based on their popularity and relevance to our study. We found that the “Free Space” model outperforms in the scenario in which measurements were conducted from the Level-1 car park to the Roadside. The received signal strengths in the parking lot ranged from −45 dBm to −92 dBm. This research also examines the coverage distance for the successful transmission of warning messages, achieving up to 57 m, 17.5 m, 9.4 m, and 68 m at parking levels 1, 2, 3, and the roadside, respectively. Research findings reveal that a low-cost Wi-Fi-based VANET system can be utilized to prevent car accidents in parking lots. Finally, we provide guidelines for network planners to deploy Wi-Fi-based VANET systems in parking lots. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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15 pages, 3328 KiB

Open AccessArticle

A Novel Approach for Improving the Security of IoT–Medical Data Systems Using an Enhanced Dynamic Bayesian Network

by Mohammed Amin Almaiah, Sandeep Yelisetti, Leena Arya, Nelson Kennedy Babu Christopher, Kumaresan Kaliappan, Pandimurugan Vellaisamy, Fahima Hajjej and Tayseer Alkdour

Electronics 2023, 12(20), 4316; https://doi.org/10.3390/electronics12204316 - 18 Oct 2023

Cited by 8 | Viewed by 1592

Abstract

IoT (Internet of Things) devices are increasingly being used in healthcare to collect and transmit patient data, which can improve patient outcomes and reduce costs. However, this also creates new challenges for data security and privacy. Thus, the major demand for secure and [...] Read more.

IoT (Internet of Things) devices are increasingly being used in healthcare to collect and transmit patient data, which can improve patient outcomes and reduce costs. However, this also creates new challenges for data security and privacy. Thus, the major demand for secure and efficient data-sharing solutions has prompted significant attention due to the increasing volume of shared sensor data. Leveraging a data-fusion-based paradigm within the realm of IoT-protected healthcare systems enabled the collection and analysis of patient data from diverse sources, encompassing medical devices, electronic health records (EHRs), and wearables. This innovative approach holds the potential to yield immediate benefits in terms of enhancing patient care, including more precise diagnoses and treatment plans. It empowers healthcare professionals to devise personalized treatment regimens by amalgamating data from multiple origins. Moreover, it has the capacity to alleviate financial burdens, elevate healthcare outcomes, and augment patient satisfaction. Furthermore, this concept extends to fortifying patient records against unauthorized access and potential misuse. In this study, we propose a novel approach for secure transmission of healthcare data, amalgamating the improved context-aware data-fusion method with an emotional-intelligence-inspired enhanced dynamic Bayesian network (EDBN). The findings indicated that F1 score, accuracy, precision, recall, and ROC-AUC score using DCNN were 89.3%, 87.4%, 91.4%, 92.1%, and 0.56, respectively, which was second-highest to the proposed method. On the other hand, the F1 score, accuracy, precision, recall, and ROC-AUC scores of FRCNN and CNN were low in accuracy at 83.2% and 84.3%, respectively. Our experimental investigation demonstrated superior performance compared with existing methods, as evidenced by various performance metrics, including recall, precision, F measures, and accuracy. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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31 pages, 2699 KiB

Open AccessArticle

Analysis of Consumer IoT Device Vulnerability Quantification Frameworks

by Samira A. Baho and Jemal Abawajy

Electronics 2023, 12(5), 1176; https://doi.org/10.3390/electronics12051176 - 28 Feb 2023

Cited by 14 | Viewed by 9134

Abstract

The increasing deployment of Internet of Things (IoT) devices in mission-critical systems has made them more appealing to attackers. Cyberattacks on IoT devices have the potential to expose sensitive data, disrupt operations, and even endanger lives. As a result, IoT security has recently [...] Read more.

The increasing deployment of Internet of Things (IoT) devices in mission-critical systems has made them more appealing to attackers. Cyberattacks on IoT devices have the potential to expose sensitive data, disrupt operations, and even endanger lives. As a result, IoT security has recently gained traction in both industry and academia. However, no research has examined existing IoT vulnerability assessment frameworks in a systematic and comprehensive manner. To address this gap, this paper systematically reviews and analyses the research challenges and state-of-the-art IoT vulnerability assessment frameworks while taking into account both breadth and depth. The study provides insight into current IoT vulnerability assessment approaches, which is useful for ongoing efforts to characterise cybersecurity risks and manage IoT vulnerabilities. It will be of interest to a spectrum of readers, including those in the IoT research community, researchers in cybersecurity, risk and vulnerability management professionals, and others. By offering the latest perspective on the present IoT vulnerability assessment techniques, this study will raise IoT security awareness and facilitate research into IoT vulnerability assessment methodologies. The knowledge provided by this study will also be beneficial to future academics who are interested in the issues and solutions surrounding IoT security. The report also assists in understanding the research direction in IoT vulnerability assessment approaches, making it beneficial for those looking to create new methods for determining IoT vulnerabilities. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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23 pages, 731 KiB

Open AccessArticle

Machine Learning Techniques for Non-Terrestrial Networks

by Romeo Giuliano and Eros Innocenti

Electronics 2023, 12(3), 652; https://doi.org/10.3390/electronics12030652 - 28 Jan 2023

Cited by 10 | Viewed by 5126

Abstract

Traditionally, non-terrestrial networks (NTNs) are used for a limited set of applications, such as TV broadcasting and communication support during disaster relief. Nevertheless, due to their technological improvements and integration in the 5G 3GPP standards, NTNs have been gaining importance in the last [...] Read more.

Traditionally, non-terrestrial networks (NTNs) are used for a limited set of applications, such as TV broadcasting and communication support during disaster relief. Nevertheless, due to their technological improvements and integration in the 5G 3GPP standards, NTNs have been gaining importance in the last years and will provide further applications and services. 3GPP standardization is integrating low-Earth orbit (LEO) satellites, high-altitude platform stations (HAPSs) and unmanned aerial systems (UASs) as non-terrestrial elements (NTEs) in the NTNs within the terrestrial 5G standard. Considering the NTE characteristics (e.g., traffic congestion, processing capacity, oscillation, altitude, pitch), it is difficult to dynamically set the optimal connection based also on the required service to properly steer the antenna beam or to schedule the UE. To this aim, machine learning (ML) can be helpful. In this paper, we present novel services supported by the NTNs and their architectures for the integration in the terrestrial 5G 3GPP standards. Then, ML techniques are proposed for managing NTN connectivity as well as to improve service performance. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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18 pages, 4250 KiB

Open AccessArticle

QoS Implementation with Triple-Metric-Based Active Queue Management for Military Networks

by Gyudong Park, Byungchun Jeon and Gyu Myoung Lee

Electronics 2023, 12(1), 23; https://doi.org/10.3390/electronics12010023 - 21 Dec 2022

Cited by 3 | Viewed by 1378

Abstract

For supporting Quality of Service (QoS) in a military network, applications of the triple-metric priority of performance, importance, and urgency as well as autonomous and lightweight implementation are required. In a previous study, we analyzed a Korean military network’s QoS implementation in the [...] Read more.

For supporting Quality of Service (QoS) in a military network, applications of the triple-metric priority of performance, importance, and urgency as well as autonomous and lightweight implementation are required. In a previous study, we analyzed a Korean military network’s QoS implementation in the perspective of the triple-metric and presented some improvements in the simplification of the service classes of Differentiated Services (DiffServ). To extend the simplified DiffServ from the previous research, this paper proposes Active Queue Management (AQM) algorithms to process the traffic of each service class differently based on importance and urgency and shows the feasibility through some experiments. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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19 pages, 892 KiB

Open AccessArticle

Performance Analysis of IEEE 802.15.4 Bootstrap Process

by Alberto Gallegos Ramonet and Taku Noguchi

Electronics 2022, 11(24), 4090; https://doi.org/10.3390/electronics11244090 - 8 Dec 2022

Cited by 2 | Viewed by 1953

Abstract

The IEEE 802.15.4 is a popular standard used in wireless sensor networks (WSNs) and the Internet of Things (IoT) applications. In these networks, devices are organized into groups formally known as personal area networks (PAN) which require a bootstrap procedure to [...] Read more.

The IEEE 802.15.4 is a popular standard used in wireless sensor networks (WSNs) and the Internet of Things (IoT) applications. In these networks, devices are organized into groups formally known as personal area networks (PAN) which require a bootstrap procedure to become operational. Bootstrap plays a key role in the initialization and maintenance of these networks. For this reason, this work presents our implementation and performance analysis for the ns-3 network simulator. Specifically, this bootstrap implementation includes the support of three types of scanning mechanisms (energy scan, passive scan, and active scan) and the complete classic association mechanism described by the standard. Both of these mechanisms can be used independently by higher layers protocols to support network initialization, network joining, and maintenance tasks. Performance evaluation is conducted in total network association time and packet overhead terms. Our source code is documented and publicly available in the latest ns-3 official release. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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13 pages, 2690 KiB

Open AccessArticle

E-Ensemble: A Novel Ensemble Classifier for Encrypted Video Identification

by Syed M. A. H. Bukhari, Waleed Afandi, Muhammad U. S. Khan, Tahir Maqsood, Muhammad B. Qureshi, Muhammad A. B. Fayyaz and Raheel Nawaz

Electronics 2022, 11(24), 4076; https://doi.org/10.3390/electronics11244076 - 8 Dec 2022

Cited by 2 | Viewed by 1557

Abstract

In recent years, video identification within encrypted network traffic has gained popularity for many reasons. For example, a government may want to track what content is being watched by its citizens, or businesses may want to block certain content for productivity. Many such [...] Read more.

In recent years, video identification within encrypted network traffic has gained popularity for many reasons. For example, a government may want to track what content is being watched by its citizens, or businesses may want to block certain content for productivity. Many such reasons advocate for the need to track users on the internet. However, with the introduction of the secure socket layer (SSL) and transport layer security (TLS), it has become difficult to analyze traffic. In addition, dynamic adaptive streaming over HTTP (DASH), which creates abnormalities due to the variable-bitrate (VBR) encoding, makes it difficult for researchers to identify videos in internet traffic. The default quality settings in browsers automatically adjust the quality of streaming videos depending on the network load. These auto-quality settings also increase the challenge in video detection. This paper presents a novel ensemble classifier, E-Ensemble, which overcomes the abnormalities in video identification in encrypted network traffic. To achieve this, three different classifiers are combined by using two different combinations of classifiers: the hard-level and soft-level combinations. To verify the performance of the proposed classifier, the classifiers were trained on a video dataset collected over one month and tested on a separate video dataset captured over 20 days at a different date and time. The soft-level combination of classifiers showed more stable results in handling abnormalities in the dataset than those of the hard-level combination. Furthermore, the soft-level classifier combination technique outperformed the hard-level combination with a high accuracy of 81.81%, even in the auto-quality mode. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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18 pages, 7639 KiB

Open AccessFeature PaperArticle

An Enhancement for IEEE 802.11 STA Power Saving and Access Point Memory Management Mechanism

by Vishal Bhargava and Nallanthighal Raghava

Electronics 2022, 11(23), 3914; https://doi.org/10.3390/electronics11233914 - 26 Nov 2022

Cited by 2 | Viewed by 2816

Abstract

Wi-Fi researchers are trying hard to extend battery life by optimizing 802.11 power save. The rising number of Wi-Fi devices and IoT devices and daily demands have reduced Station (STA) device power consumption. Better memory management at the Access Point (AP) side is [...] Read more.

Wi-Fi researchers are trying hard to extend battery life by optimizing 802.11 power save. The rising number of Wi-Fi devices and IoT devices and daily demands have reduced Station (STA) device power consumption. Better memory management at the Access Point (AP) side is also needed, so that AP can store maximum data to deliver sleepy STA devices. There are three main contributions of this study. The first one focuses on a power-saving mechanism scheme with an adaptive change to Listen Interval (LI) based on the battery status of station devices. The second contribution aims to examine better memory management for the AP buffer to store packets that will in the future deliver power-saving STA when awake. The third contribution, under the implementation of the proposed method, includes Wi-Fi corner cases covered as Beacon frames missed via STA, the keep-alive factor, and the upper-layer time taken to care for and ensure the delivery of unicast/multicast/broadcast data. The proposed approach introduced 802.11 protocols to share battery status, a protocol to announce proposed features via AP, and a protocol to change LI at runtime. Simulation results show that the proposed scheme performs better than 802.11 power saving in terms of power usage at the STA and access point memory management. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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26 pages, 4344 KiB

Open AccessArticle

A Low-Cost and Do-It-Yourself Device for Pumping Monitoring in Deep Aquifers

by Carles Aliagas, Agustí Pérez-Foguet, Roc Meseguer, Pere Millán and Carlos Molina

Electronics 2022, 11(22), 3788; https://doi.org/10.3390/electronics11223788 - 18 Nov 2022

Viewed by 2385

Abstract

Water crises due to climate change, high population growth and increasing demands from industry and agriculture claim for increasing efficiency and universalizing water resources management strategies and techniques. Water monitoring helps providing necessary evidences for making sound decisions about managing water resources both [...] Read more.

Water crises due to climate change, high population growth and increasing demands from industry and agriculture claim for increasing efficiency and universalizing water resources management strategies and techniques. Water monitoring helps providing necessary evidences for making sound decisions about managing water resources both now and in the future. In this work, a low cost and “do it yourself” communication device is proposed to record water production and energy consumption of electric pumpings from deep boreholes/wells, and to predict the impact of the ongoing and previous pumpings in the evolution of the water level in the aquifer. The proposal incorporates an edge-computing approach for the simulation of the aquifer response in real-time. Computation of results of interest is performed at the sensor, minimizing communication requirements and ensuring almost immediate results. An approximated solution to physically based modeling of aquifer response is computed thanks to the a priori expression of the water level time evolution in a reduced basis. The accuracy is enough to detect deviations from expected behaviour. The energy consumption of the device is very much reduced with respect to that of a full modelling, which can be computed off-line for calibrating reduced model parameters and perform detailed analyses. The device is tested in a real scenario, in a mountain subbasin of the Ebro river in Spain, obtaining a good trade-off between performance, price, and energy consumption. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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12 pages, 1419 KiB

Open AccessFeature PaperArticle

Blockchain-Empowered AI for 6G-Enabled Internet of Vehicles

by Ferheen Ayaz, Zhengguo Sheng, Daxin Tian, Maziar Nekovee and Nagham Saeed

Electronics 2022, 11(20), 3339; https://doi.org/10.3390/electronics11203339 - 17 Oct 2022

Cited by 9 | Viewed by 2756

Abstract

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and [...] Read more.

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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24 pages, 2813 KiB

Open AccessArticle

ANN and SSO Algorithms for a Newly Developed Flexible Grid Trading Model

by Wei-Chang Yeh, Yu-Hsin Hsieh, Kai-Yi Hsu and Chia-Ling Huang

Electronics 2022, 11(19), 3259; https://doi.org/10.3390/electronics11193259 - 10 Oct 2022

Cited by 3 | Viewed by 3140

Abstract

In the modern era, the trading methods and strategies used in the financial market have gradually changed from traditional on-site trading to electronic remote trading, and even online automatic trading performed by pre-programmed computer programs. This is due to the conduct of trading [...] Read more.

In the modern era, the trading methods and strategies used in the financial market have gradually changed from traditional on-site trading to electronic remote trading, and even online automatic trading performed by pre-programmed computer programs. This is due to the conduct of trading automatically and self-adjustment in financial markets becoming a competitive development trend in the entire financial market, with the continuous development of network and computer computing technology. Quantitative trading aims to automatically form a fixed and quantifiable operational logic from people’s investment decisions and apply it to the financial market, which has attracted the attention of the financial market. The development of self-adjustment programming algorithms for automatically trading in financial markets has transformed to being a top priority for academic research and financial practice. Thus, a new flexible grid trading model incorporating the Simplified Swarm Optimization (SSO) algorithm for optimizing parameters for various market situations as input values and the Fully Connected Neural Network (FNN) and Long Short-Term Memory (LSTM) model for training a quantitative trading model for automatically calculating and adjusting the optimal trading parameters for trading after inputting the existing market situation are developed and studied in this work. The proposed model provides a self-adjust model to reduce investors’ effort in the trading market, obtains outperformed Return of Investment (ROI) and model robustness, and can properly control the balance between risk and return. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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18 pages, 1204 KiB

Open AccessArticle

Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection

by Kayode S. Adewole, Taofeekat T. Salau-Ibrahim, Agbotiname Lucky Imoize, Idowu Dauda Oladipo, Muyideen AbdulRaheem, Joseph Bamidele Awotunde, Abdullateef O. Balogun, Rafiu Mope Isiaka and Taye Oladele Aro

Electronics 2022, 11(19), 3109; https://doi.org/10.3390/electronics11193109 - 28 Sep 2022

Cited by 8 | Viewed by 2581

Abstract

Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research [...] Read more.

Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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16 pages, 3847 KiB

Open AccessArticle

A Voronoi Diagram-Based Grouping Test Localization Scheme in Wireless Sensor Networks

by Guangming Li, Menghui Xu, Gaofei Teng, Wei Yang, Shu-Lun Mak, Chun-Yin Li and Chi-Chung Lee

Electronics 2022, 11(18), 2961; https://doi.org/10.3390/electronics11182961 - 18 Sep 2022

Cited by 1 | Viewed by 1989

Abstract

The wireless sensor network (WSN) provides us with a cost-effective way to remotely monitor a large number of objects, locations, and environmental parameters. Taking advantage of WSNs to applications can add new capabilities to existing products and bring out new services. We propose [...] Read more.

The wireless sensor network (WSN) provides us with a cost-effective way to remotely monitor a large number of objects, locations, and environmental parameters. Taking advantage of WSNs to applications can add new capabilities to existing products and bring out new services. We propose a novel range-free localization scheme, the Voronoi diagram-based grouping test localization (VTL) scheme, to estimate the location efficiently for WSNs. VTL divides the anchor nodes into multiple groups and uses the corresponding closest Voronoi cells to compute the estimated location. Apart from improving the accuracy of location estimation, it also largely simplifies the implementation. Simulation results show that the VTL scheme has better performance compared with other range-free localization schemes. When reaching a certain anchor node density, the VTL scheme will have higher localization accuracy and a larger percentage of localizable nodes. Hence, VTL is likely more appropriate for upcoming WSN scenarios with large ratios of anchor nodes being available. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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14 pages, 973 KiB

Open AccessArticle

Learning Balance Feature for Object Detection

by Zhiqiang Zhang, Xin Qiu and Yongzhou Li

Electronics 2022, 11(17), 2765; https://doi.org/10.3390/electronics11172765 - 2 Sep 2022

Viewed by 1636

Abstract

In the field of studying scale variation, the Feature Pyramid Network (FPN) replaces the image pyramid and has become one of the most popular object detection methods for detecting multi-scale objects. State-of-the-art methods have FPN inserted into a pipeline between the backbone and [...] Read more.

In the field of studying scale variation, the Feature Pyramid Network (FPN) replaces the image pyramid and has become one of the most popular object detection methods for detecting multi-scale objects. State-of-the-art methods have FPN inserted into a pipeline between the backbone and the detection head to enable shallow features with more semantic information. However, FPN is insufficient for object detection on various scales, especially for small-scale object detection. One of the reasons is that the features are extracted at different network depths, which introduces gaps between features. That is, as the network becomes deeper and deeper, the high-level features have more semantics but less content description. This paper proposes a new method that includes a multi-scale receptive fields extraction module, a feature constructor module, and an attention module to improve the detection efficiency of FPN for objects of various scales and to bridge the gap in content description and semantics between different layers. Together, these three modules make the detector capable of selecting the most suitable feature for objects. Especially for the attention module, this paper chooses to use a parallel structure to simultaneously extract channel and spatial attention from the same features. When we use Adopting Adaptive Training Sample Selection (ATSS) and FreeAnchor as the baseline and ResNet50 as the backbone, the experimental results on the MS COCO dataset show that our algorithm can enhance the mean average precision (mAP) by 3.7% and 2.4% compared to FPN, respectively. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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16 pages, 536 KiB

Open AccessArticle

Efficient Detailed Routing for FPGA Back-End Flow Using Reinforcement Learning

by Imran Baig and Umer Farooq

Electronics 2022, 11(14), 2240; https://doi.org/10.3390/electronics11142240 - 18 Jul 2022

Cited by 3 | Viewed by 2741

Abstract

Over the past few years, the computation capability of field-programmable gate arrays (FPGAs) has increased tremendously. This has led to the increase in the complexity of the designs implemented on FPGAs and to the time taken by the FPGA back-end flow. The FPGA [...] Read more.

Over the past few years, the computation capability of field-programmable gate arrays (FPGAs) has increased tremendously. This has led to the increase in the complexity of the designs implemented on FPGAs and to the time taken by the FPGA back-end flow. The FPGA back-end flow comprises of many steps, and routing is one of the most critical steps among them. Routing normally constitutes more than 50% of the total time taken by the back-end flow and an optimization at this step can lead to overall optimization of the back-end flow. In this work, we propose enhancements to the routing step by incorporating a reinforcement learning (RL)-based framework. In the proposed RL-based framework, we use the

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-greedy approach and customized reward functions to speed up the routing step while maintaining similar or better quality of results (QoR) as compared to the conventional negotiation-based congestion-driven routing solution. For experimentation, we use two sets of widely deployed, large heterogeneous benchmarks. Our results show that, for the RL-based framework, the

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-greedy greedy approach combined with a modified reward function gives better results as compared to purely greedy or exploratory approaches. Moreover, the incorporation of the proposed reward function in the RL-based framework and its comparison with a conventional routing algorithm shows that the proposed enhancement requires less routing time while giving similar or better QoR. On average, a speedup of 35% is recorded for the proposed routing enhancement as compared to negotiation-based congestion-driven routing solutions. Finally, the speedup of the routing step leads to an overall reduction in the execution time of the back-end flow of 25%. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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14 pages, 2209 KiB

Open AccessArticle

Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process

by Sofiane Dahmane, Mohamed Bachir Yagoubi, Bouziane Brik, Chaker Abdelaziz Kerrache, Carlos Tavares Calafate and Pascal Lorenz

Electronics 2022, 11(14), 2119; https://doi.org/10.3390/electronics11142119 - 6 Jul 2022

Cited by 10 | Viewed by 2899

Abstract

Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), [...] Read more.

Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs’ privacy. However, UAV devices are usually limited in terms of resources such as battery, memory, and CPU. Some of the UAV devices may not be able to build a local learning models due to their resources capacity. Hence, there is a great need to select the adequate UAVs at each FL round, that are able to build a local DL model based on their resource capacities. In this paper, we design a novel and S-MEC-enabled framework that optimizes the selection of UAV participants at each FL training round, named FedSel. FedSel considers the available UAVs along with their resource capacities, in terms of energy, CPU, and memory, to determine which UAV device is able to participant in the FL process. Thus, we formulate the UAV selection problem as an Integer Linear Program, which considers the aforementioned constraints. We also prove that this problem is NP-hard, and suggest a Tabu Search (TS) metaheuristic-based approach to resolve it. Moreover, FedSel is built on top of blockchain technology, in order to ensure a secure selection of UAV participants, and hence building reliable FL-based models. Simulation results validate the efficiency of our FedSel scheme in balancing computational load among available UAVs and optimizing the UAV selection process. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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22 pages, 2176 KiB

Open AccessArticle

Copa-ICN: Improving Copa as a Congestion Control Algorithm in Information-Centric Networking

by Zhiyuan Wang, Hong Ni and Rui Han

Electronics 2022, 11(11), 1710; https://doi.org/10.3390/electronics11111710 - 27 May 2022

Cited by 3 | Viewed by 2431

Abstract

To fundamentally improve the efficiency of content distribution in the network, information-centric networking (ICN) has received extensive attention. However, the existence of a large number of IP facilities in the current network makes the smooth evolution of the network architecture a realistic requirement. [...] Read more.

To fundamentally improve the efficiency of content distribution in the network, information-centric networking (ICN) has received extensive attention. However, the existence of a large number of IP facilities in the current network makes the smooth evolution of the network architecture a realistic requirement. The ICN architecture that separates the process of name resolution and message routing is widely accepted for its better compatibility with IP networks. In this architecture, the user first obtains the locator of the content replica node from the name resolution system (NRS) and then completes the data transmission through the locator. In data transmission, receiver-driven congestion control algorithms need to be studied. Therefore, we introduce the Copa algorithm into ICN and propose an improved Copa-ICN algorithm. Experiments show that the Copa-ICN algorithm has a high convergence speed and fairness, and when there is a transmission process in the opposite direction, it can still have a high throughput different from the original Copa algorithm. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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27 pages, 3460 KiB

Open AccessFeature PaperArticle

Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms

by Steve Chukwuebuka Arum, David Grace and Paul Daniel Mitchell

Electronics 2022, 11(9), 1508; https://doi.org/10.3390/electronics11091508 - 7 May 2022

Cited by 2 | Viewed by 2282

Abstract

Interest in delivering cellular communication using a high-altitude platform (HAP) is increasing partly due to its wide coverage capability. In this paper, we formulate analytical expressions for estimating the area of a HAP beam footprint, average per-user capacity per cell, average spectral efficiency [...] Read more.

Interest in delivering cellular communication using a high-altitude platform (HAP) is increasing partly due to its wide coverage capability. In this paper, we formulate analytical expressions for estimating the area of a HAP beam footprint, average per-user capacity per cell, average spectral efficiency (SE) and average area spectral efficiency (ASE), which are relevant for radio network planning, especially within the context of HAP extended contiguous cellular coverage and capacity. To understand the practical implications, we propose an enhanced and validated recursive HAP antenna beam-pointing algorithm, which forms HAP cells over an extended service area while considering beam broadening and the degree of overlap between neighbouring beams. The performance of the extended contiguous cellular structure resulting from the algorithm is compared with other alternative schemes using the carrier-to-noise ratio (CNR) and carrier-to-interference-plus-noise ratio (CINR). Results show that there is a steep reduction in average ASE at the edge of coverage. The achievable coverage is limited by the minimum acceptable average ASE at the edge, among other factors. In addition, the results highlight that efficient beam management can be achieved using the enhanced and validated algorithm, which significantly improves user CNR, CINR, and coverage area compared with other benchmark schemes. A simulated annealing comparison verifies that such an algorithm is close to optimal. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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18 pages, 1094 KiB

Open AccessFeature PaperArticle

Modeling Distributed MQTT Systems Using Multicommodity Flow Analysis

by Pietro Manzoni, Vittorio Maniezzo and Marco A. Boschetti

Electronics 2022, 11(9), 1498; https://doi.org/10.3390/electronics11091498 - 7 May 2022

Cited by 2 | Viewed by 1847

Abstract

The development of technologies that exploit the Internet of Things (IoT) paradigm has led to the increasingly widespread use of networks formed by different devices scattered throughout the territory. The Publish/Subscribe paradigm is one of the most used communication paradigms for applications of [...] Read more.

The development of technologies that exploit the Internet of Things (IoT) paradigm has led to the increasingly widespread use of networks formed by different devices scattered throughout the territory. The Publish/Subscribe paradigm is one of the most used communication paradigms for applications of this type. However, adopting these systems due to their centralized structure also leads to the emergence of various problems and limitations. For example, the broker is typically the single point of failure of the system: no communication is possible if the broker is unavailable. Moreover, they may not scale well considering the massive numbers of IoT devices forecasted in the future. Finally, a network architecture with a single central broker is partially at odds with the edge-oriented approach. This work focuses on the development of an adaptive topology control approach, able to find the most efficient network configuration maximizing the number of connections and reduce the waste of resources within it, starting from the definition of the devices and the connections between them present in the system. To reach the goal, we leverage an integer linear programming mathematical formulation, providing the basis to solve and optimize the problem of network configuration in contexts where the resources available to the devices are limited. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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20 pages, 3960 KiB

Open AccessArticle

TCP-LoRaD: A Loss Recovery and Differentiation Algorithm for Improving TCP Performance over MANETs in Noisy Channels

by Nurul I. Sarkar, Ping-Huan Ho, Sonia Gul and Salahuddin Muhammad Salim Zabir

Electronics 2022, 11(9), 1479; https://doi.org/10.3390/electronics11091479 - 5 May 2022

Cited by 5 | Viewed by 2012

Abstract

Mobile Ad hoc Networks (MANETs) are becoming popular technologies because they offer flexibility in setting up anytime and anywhere, and provide communication support on the go. This communication requires the use of Transmission Control Protocol (TCP) which is not originally designed for use [...] Read more.

Mobile Ad hoc Networks (MANETs) are becoming popular technologies because they offer flexibility in setting up anytime and anywhere, and provide communication support on the go. This communication requires the use of Transmission Control Protocol (TCP) which is not originally designed for use in MANET environments; therefore, it raises serious performance issues. To overcome the deficiency of the original TCP, several modifications have been proposed and reported in the networking literature. TCP-WELCOME (Wireless Environment, Link losses, and Congestion packet loss ModEls) is one of the better TCP variants suitable for MANETs. However, it has been found that this protocol has problems with packet losses because of network congestion as it adopts the original congestion control mechanism of TCP New Reno. We also found that TCP-WELCOME does not perform well in noisy channel conditions in wireless environments. In this paper, we propose a novel loss recovery and differentiation algorithm (called TCP-LoRaD) to overcome the above-mentioned TCP problems. We validate the performance of TCP-LoRaD through an extensive simulation setup using Riverbed Modeler (formerly OPNET). Results obtained show that the proposed TCP-LoRaD offers up to 20% higher throughput and about 15% lower end-to-end delays than the TCP-WELCOME in a noisy channel under medium to high traffic loads. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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20 pages, 3700 KiB

Open AccessArticle

Reputation-Based Sharding Consensus Model in Information-Centric Networking

by Jia Shi, Xuewen Zeng and Yang Li

Electronics 2022, 11(5), 830; https://doi.org/10.3390/electronics11050830 - 7 Mar 2022

Cited by 2 | Viewed by 2479

Abstract

The various integration systems of blockchain and information-centric network (ICN) have been applied to provide a trusted and neutral approach to cope with large-scale content distribution in IoT, AR/VR, or 5G/6G scenarios. As a result, the scalability problem of blockchain has been an [...] Read more.

The various integration systems of blockchain and information-centric network (ICN) have been applied to provide a trusted and neutral approach to cope with large-scale content distribution in IoT, AR/VR, or 5G/6G scenarios. As a result, the scalability problem of blockchain has been an increasing concern for researchers. The sharding mechanism is recognized as a promising approach to address this challenge. However, there are still many problems in the existing schemes. Firstly, real-time processing speed trades off security of validation. Secondly, simply randomly assigning nodes to the shards may make nodes located very far from each other, which increases the block propagation time and reduces the efficiency advantage brought by the sharding mechanism. Therefore, we optimize a reputation-based sharding consensus model by multi-dimension trust and leverage the affinity propagation (AP) algorithm for gathering consensus nodes into shards. Given the minimal possibility to be at fault in the security of validation, clients can achieve real-time processing speed with assurance. The evaluation results show that the normalized mean square error (NMSE) between the estimated reputation value and the real reputation value of our reputation scheme is less than 0.02. Meanwhile, compared with the classical sharding scheme Omniledger, TPS performance can achieve 1.4 times promotion in the case of a large-scale blockchain network of 1000 nodes. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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14 pages, 7013 KiB

Open AccessArticle

A 130-to-220-GHz Frequency Quadrupler with 80 dB Dynamic Range for 6G Communication in 0.13-μm SiGe Process

by Tianxiang Wu, Zhiyuan Cao, Zhuofan Xu, Liuyao Dai, Wei Mao, Jin He, Shunli Ma and Hao Yu

Electronics 2022, 11(5), 825; https://doi.org/10.3390/electronics11050825 - 7 Mar 2022

Viewed by 2651

Abstract

This paper presents a broadband frequency quadrupler (FQ) implemented with a standard 130-nm SiGe BiCMOS process. Two broadband push-push frequency doublers (×2) operate at an input frequency of 32.5–55 GHz and 65–110 GHz, respectively. To properly drive the two doublers with enough input [...] Read more.

This paper presents a broadband frequency quadrupler (FQ) implemented with a standard 130-nm SiGe BiCMOS process. Two broadband push-push frequency doublers (×2) operate at an input frequency of 32.5–55 GHz and 65–110 GHz, respectively. To properly drive the two doublers with enough input power and bandwidth, two transformer coupled power amplifiers (PAs) have been adopted. The former power amplifier is based on a neutralized capacitor structure and the latter is based on a transformer topology. A nonlinear device model and a systematic methodology to generate maximum power at second harmonic are proposed. By manipulating the device nonlinearity and optimizing the magnetically and capacitively coupled resonator (MCCR) matching networks, optimum conditions for harmonic power generation are provided. The measurement results show that the proposed quadrupler provides a 90-GHz bandwidth with an 80-dB dynamic range and a high energy efficiency η of 3.7% at 210 GHz. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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18 pages, 367 KiB

Open AccessArticle

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

by Iskanter-Alexandros Chousainov, Ioannis Moscholios, Panagiotis Sarigiannidis and Michael Logothetis

Electronics 2022, 11(5), 773; https://doi.org/10.3390/electronics11050773 - 2 Mar 2022

Viewed by 1521

Abstract

In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units (RUs) and are separated from the remote radio heads (RRHs). The RRHs are grouped into clusters based on their capacity in [...] Read more.

In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units (RUs) and are separated from the remote radio heads (RRHs). The RRHs are grouped into clusters based on their capacity in radio RUs. Each RRH serves different service-classes whose calls have different requirements in terms of radio and computational RUs and follow a compound Poisson process. This means that calls arrive in batches while each batch of calls follows a Poisson process. If the RUs’ requirements of an arriving call are met, then the call is accepted in the serving RRH for an exponentially distributed service time. Otherwise, call blocking occurs. We initially start our analysis with a single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time and call congestion probabilities via a convolution algorithm. Furthermore, the previous model is extended to include the more complex case of many clusters of RRHs. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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Graphical abstract

11 pages, 2861 KiB

Open AccessArticle

Internet News User Analysis Using Deep Learning and Similarity Comparison

by Sunoh Choi

Electronics 2022, 11(4), 569; https://doi.org/10.3390/electronics11040569 - 14 Feb 2022

Cited by 3 | Viewed by 1822

Abstract

Nowadays, many Korean users read news from portal sites like Naver and Daum. Users can comment on news articles on such sites, and some try to influence public opinion through their comments. Therefore, news users need to be analyzed. This study proposes a [...] Read more.

Nowadays, many Korean users read news from portal sites like Naver and Daum. Users can comment on news articles on such sites, and some try to influence public opinion through their comments. Therefore, news users need to be analyzed. This study proposes a deep learning method to classify each user’s political stance. Further, a method is developed to evaluate how many similar comments each user writes, and another method is developed to evaluate the similarity of a user’s comments with other users’ comments. We collect approximately 2.68 million comments from hundreds of thousands of political news articles in April 2017. First, for the top 100 news users, we classify each user’s political stance with 92.3% accuracy by using only 20% of data for deep learning training. Second, an evaluation of how many similar comments each user writes reveals that six users score more than 80 points. Third, an evaluation of the similarity of each user’s comments to other users’ comments reveals that 10 users score more than 80 points. Thus, based on this study, it is possible to detect malicious commenters, thereby enhancing comment systems used in news portal websites. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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Review

Jump to: Research, Other

25 pages, 4178 KiB

Open AccessReview

Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review-Part-I (AI, Big Data, Block Chain, Open-Source Technologies, and Cloud Computing)

by Sridhar Siripurapu, Naresh K. Darimireddy, Abdellah Chehri, B. Sridhar and A. V. Paramkusam

Electronics 2023, 12(3), 750; https://doi.org/10.3390/electronics12030750 - 2 Feb 2023

Cited by 9 | Viewed by 3303

Abstract

In the realm of the emergence and spread of infectious diseases with pandemic potential throughout the history, plenty of pandemics (and epidemics), from the plague to AIDS (1981) and SARS (in 2003) to the bunch of COVID variants, have tormented mankind. Though plenty [...] Read more.

In the realm of the emergence and spread of infectious diseases with pandemic potential throughout the history, plenty of pandemics (and epidemics), from the plague to AIDS (1981) and SARS (in 2003) to the bunch of COVID variants, have tormented mankind. Though plenty of technological innovations are overwhelmingly progressing to curb them—a significant number of such pandemics astounded the world, impacting billions of lives and posing uncovered challenges to healthcare organizations and clinical pathologists globally. In view of addressing these limitations, a critically exhaustive review is performed to signify the prospective role of technological advancements and highlight the implicit problems associated with rendering best quality lifesaving treatments to the patient community. The proposed review work is conducted in two parts. Part 1 is essentially focused upon discussion of advanced technologies akin to artificial intelligence, Big Data, block chain technology, open-source technology, cloud computing, etc. Research works governing applicability of these technologies in solving many uncovered healthcare issues prominently faced by doctors and surgeons in the fields of cardiology, medicine, neurology, orthopaedics, paediatrics, gynaecology, psychiatry, plastic surgery, etc., as well as their role in curtailing the spread of numerous infectious, pathological, neurotic maladies is thrown light off. Boundary conditions and implicitly associated challenges substantiated by remedies coupled with future directions are presented at the end. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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21 pages, 2980 KiB

Open AccessReview

Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review-Part-II (Robotics, Drones, 3D-Printing, Internet of Things, Virtual/Augmented and Mixed Reality)

by Sridhar Siripurapu, Naresh K. Darimireddy, Abdellah Chehri, Sridhar B. and Paramkusam A.V.

Electronics 2023, 12(3), 548; https://doi.org/10.3390/electronics12030548 - 20 Jan 2023

Cited by 11 | Viewed by 5413

Abstract

The substantial applicability of technological advancements to the healthcare sector and its allied segments are on the verge of questioning the abilities of hospitals, medical institutions, doctors and clinical pathologists in delivering world class healthcare facilities to the global patient community. Investigative works [...] Read more.

The substantial applicability of technological advancements to the healthcare sector and its allied segments are on the verge of questioning the abilities of hospitals, medical institutions, doctors and clinical pathologists in delivering world class healthcare facilities to the global patient community. Investigative works pertinent to the role played of technological advancements in the healthcare sector motivated this work to be undertaken. Part-I of the review addressed the applicable role play of advanced technologies such as Artificial intelligence, Big-data, Block chain, Open-Source and Cloud Computing Technologies, etc., to the healthcare sector and its allied segments. The current Part-II manuscript is critically focused upon reviewing the sustainable role of additional disrupting technologies such as Robotics, Drones, 3D-Printing, IoT, Virtual/Augmented/Mixed Reality, etc., to uncover the vast number of implicit problems encountered by the clinical community. Investigations governing the deployment of these technologies in various allied healthcare segments are highlighted in this manuscript. Subsequently, the unspoken challenges and remedial future directions are discussed thereof. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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Other

Jump to: Research, Review

27 pages, 3119 KiB

Open AccessConcept Paper

Outsourcing Authentication, Authorization and Accounting, and Charging and Billing Services to Trusted Third Parties for Future Consumer-Oriented Wireless Communications

by Ivan Ganchev and Máirtín O’Droma

Electronics 2023, 12(3), 558; https://doi.org/10.3390/electronics12030558 - 21 Jan 2023

Cited by 2 | Viewed by 1675

Abstract

In this article, proposals for the realization of an infrastructural re-think on the way authentication, authorization and accounting (AAA) services and charging and billing (C&B) services are supplied within the ubiquitous consumer wireless world (UCWW) are set out. Proposals envisage these services being [...] Read more.

In this article, proposals for the realization of an infrastructural re-think on the way authentication, authorization and accounting (AAA) services and charging and billing (C&B) services are supplied within the ubiquitous consumer wireless world (UCWW) are set out. Proposals envisage these services being owned and organized by trusted third parties (TTPs) and utilizing new globally standardized protocols and infrastructural entity interfaces. Their implementation will affect a successful realization of the UCWW’s consumer-based techno-business infrastructure, complementing or even replacing the present legacy network-centric, subscriber-based one. The approach enables a loose dynamic, or even casual, consumer-type association between consumers (mobile users) and network/teleservice providers, and it opens the door to multifaceted benefits for consumers, for new network/teleservice providers, and for other new UCWW business entities in addition to the 3P-AAA and 3P-C&B service providers at the heart of this article’s proposals. Full article

(This article belongs to the Special Issue Feature Papers in "Networks" Section)

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