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19 pages, 3610 KiB

Open AccessFeature PaperArticle

Overview of Prospects for Service-Aware Radio Access towards 6G Networks

by Zixiao Zhao, Qinghe Du, Dawei Wang, Xiao Tang and Houbing Song

Electronics 2022, 11(8), 1262; https://doi.org/10.3390/electronics11081262 - 16 Apr 2022

Cited by 19 | Viewed by 3870

Abstract

The integration of space–air–ground–sea networking in 6G, which is expected to not only achieve seamless coverage but also offer service-aware access and transmission, has introduced many new challenges for current mobile communications systems. Service awareness requires the 6G network to be aware of [...] Read more.

The integration of space–air–ground–sea networking in 6G, which is expected to not only achieve seamless coverage but also offer service-aware access and transmission, has introduced many new challenges for current mobile communications systems. Service awareness requires the 6G network to be aware of the demands of a diverse range of services as well as the occupation, utilization, and variation of network resources, which will enable the capability of deriving more intelligent and effective solutions for complicated heterogeneous resource configuration. Following this trend, this article investigates potential techniques that may improve service-aware radio access using the heterogeneous 6G network. We start with a discussion on the evolution of cloud-based RAN architectures from 5G to 6G, and then we present an intelligent radio access network (RAN) architecture for the integrated 6G network, which targets balancing the computation loads and fronthaul burden and achieving service-awareness for heterogeneous and distributed requests from users. In order for the service-aware access and transmissions to be equipped for future heterogeneous 6G networks, we analyze the challenges and potential solutions for the heterogeneous resource configuration, including a tightly coupled cross-layer design, resource service-aware sensing and allocation, transmission over multiple radio access technologies (RAT), and user socialization for cloud extension. Finally, we briefly explore some promising and crucial research topics on service-aware radio access for 6G networks. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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19 pages, 710 KiB

Open AccessArticle

Cyber Secure Framework for Smart Agriculture: Robust and Tamper-Resistant Authentication Scheme for IoT Devices

by Saleh Alyahya, Waseem Ullah Khan, Salman Ahmed, Safdar Nawaz Khan Marwat and Shabana Habib

Electronics 2022, 11(6), 963; https://doi.org/10.3390/electronics11060963 - 21 Mar 2022

Cited by 29 | Viewed by 4420

Abstract

Internet of Things (IoT) as refers to a network of devices that have the ability to connect, collect and exchange data with other devices over the Internet. IoT is a revolutionary technology that have tremendous applications in numerous fields of engineering and sciences [...] Read more.

Internet of Things (IoT) as refers to a network of devices that have the ability to connect, collect and exchange data with other devices over the Internet. IoT is a revolutionary technology that have tremendous applications in numerous fields of engineering and sciences such as logistics, healthcare, traffic, oil and gas industries and agriculture. In agriculture field, the farmer still used conventional agriculture methods resulting in low crop and fruit yields. The integration of IoT in conventional agriculture methods has led to significant developments in agriculture field. Different sensors and IoT devices are providing services to automate agriculture precision and to monitor crop conditions. These IoT devices are deployed in agriculture environment to increase yields production by making smart farming decisions and to collect data regarding crops temperature, humidity and irrigation systems. However, the integration of IoT and smart communication technologies in agriculture environment introduces cyber security attacks and vulnerabilities. Such cyber attacks have the capability to adversely affect the countries’ economies that are heavily reliant on agriculture. On the other hand, these IoT devices are resource constrained having limited memory and power capabilities and cannot be secured using conventional cyber security protocols. Therefore, designing robust and efficient secure framework for smart agriculture are required. In this paper, a Cyber Secured Framework for Smart Agriculture (CSFSA) is proposed. The proposed CSFSA presents a robust and tamper resistant authentication scheme for IoT devices using Constrained Application Protocol (CoAP) to ensure the data integrity and authenticity. The proposed CSFSA is demonstrated in Contiki NG simulation tool and greatly reduces packet size, communication overhead and power consumption. The performance of proposed CSFSA is computationally efficient and is resilient against various cyber security attacks i.e., replay attacks, Denial of Service (DoS) attacks, resource exhaustion. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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11 pages, 2330 KiB

Open AccessArticle

Deep Learning-Based Object Detection and Scene Perception under Bad Weather Conditions

by Teena Sharma, Benoit Debaque, Nicolas Duclos, Abdellah Chehri, Bruno Kinder and Paul Fortier

Electronics 2022, 11(4), 563; https://doi.org/10.3390/electronics11040563 - 13 Feb 2022

Cited by 74 | Viewed by 11294

Abstract

Large cities’ expanding populations are causing traffic congestion. The maintenance of the city’s road network necessitates ongoing monitoring, growth, and modernization. An intelligent vehicle detection solution is necessary to address road traffic concerns with the advancement of automatic cars. The identification and tracking [...] Read more.

Large cities’ expanding populations are causing traffic congestion. The maintenance of the city’s road network necessitates ongoing monitoring, growth, and modernization. An intelligent vehicle detection solution is necessary to address road traffic concerns with the advancement of automatic cars. The identification and tracking vehicles on roads and highways are part of intelligent traffic monitoring while driving. In this paper, we have presented how You Only Look Once (YOLO) v5 model may be used to identify cars, traffic lights, and pedestrians in various weather situations, allowing for real-time identification in a typical vehicular environment. In an ordinary or autonomous environment, object detection may be affected by bad weather conditions. Bad weather may make driving dangerous in various ways, whether due to freezing roadways or the illusion of low fog. In this study, we used YOLOv5 model to recognize objects from street-level recordings for rainy and regular weather scenarios on 11 distinct classes of vehicles (car, truck, bike), pedestrians, and traffic signals (red, green, yellow). We utilized freely available Roboflow datasets to train the proposed system. Furthermore, we used real video sequences of road traffic to evaluate the proposed system’s performance. The study results revealed that the suggested approach could recognize cars, trucks, and other roadside items in various circumstances with acceptable results. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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24 pages, 1256 KiB

Open AccessArticle

Synthetic Energy Data Generation Using Time Variant Generative Adversarial Network

by Shashank Asre and Adnan Anwar

Electronics 2022, 11(3), 355; https://doi.org/10.3390/electronics11030355 - 24 Jan 2022

Cited by 19 | Viewed by 4295

Abstract

Energy consumption data is being used for improving the energy efficiency and minimizing the cost. However, obtaining energy consumption data has two major challenges: (i) data collection is very expensive, time-consuming, and (ii) security and privacy concern of the users which can be [...] Read more.

Energy consumption data is being used for improving the energy efficiency and minimizing the cost. However, obtaining energy consumption data has two major challenges: (i) data collection is very expensive, time-consuming, and (ii) security and privacy concern of the users which can be revealed from the actual data. In this research, we have addressed these challenges by using generative adversarial networks for generating energy consumption profile. We have successfully generated synthetic data which is similar to the real energy consumption data. On the basis of the recent research conducted on TimeGAN, we have implemented a framework for synthetic energy consumption data generation that could be useful in research, data analysis and create business solutions. The framework is implemented using the real-world energy dataset, consisting of energy consumption data of the year 2020 for the Australian states of Victoria, New South Wales, South Australia, Queensland and Tasmania. The results of implementation is evaluated using various performance measures and the results are showcased using visualizations along with Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (TSNE) plots. Overall, experimental results show that Synthetic data generated using the proposed implementation possess very similar characteristics to the real dataset with high comparison accuracy. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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16 pages, 3627 KiB

Open AccessArticle

SCNN-Attack: A Side-Channel Attack to Identify YouTube Videos in a VPN and Non-VPN Network Traffic

by Muhammad U. S. Khan, Syed M. A. H. Bukhari, Tahir Maqsood, Muhammad A. B. Fayyaz, Darren Dancey and Raheel Nawaz

Electronics 2022, 11(3), 350; https://doi.org/10.3390/electronics11030350 - 24 Jan 2022

Cited by 11 | Viewed by 4223

Abstract

Encryption Protocols e.g., HTTPS is utilized to secure the traffic between servers and clients for YouTube and other video streaming services, and to further secure the communication, VPNs are used. However, these protocols are not sufficient to hide the identity of the videos [...] Read more.

Encryption Protocols e.g., HTTPS is utilized to secure the traffic between servers and clients for YouTube and other video streaming services, and to further secure the communication, VPNs are used. However, these protocols are not sufficient to hide the identity of the videos from someone who can sniff the network traffic. The present work explores the methodologies and features to identify the videos in a VPN and non-VPN network traffic. To identify such videos, a side-channel attack using a Sequential Convolution Neural Network is proposed. The results demonstrate that a sequence of bytes per second from even one-minute sniffing of network traffic is sufficient to predict the video with high accuracy. The accuracy is increased to 90% accuracy in the non-VPN, 66% accuracy in the VPN, and 77% in the mixed VPN and non-VPN traffic, for models with two-minute sniffing. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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24 pages, 625 KiB

Open AccessArticle

Modeling Bitcoin plus Ethereum as an Open System of Systems of Public Blockchains to Improve Their Resilience against Intentional Risk

by Alberto Partida, Saki Gerassis, Regino Criado, Miguel Romance, Eduardo Giráldez and Javier Taboada

Electronics 2022, 11(2), 241; https://doi.org/10.3390/electronics11020241 - 12 Jan 2022

Cited by 4 | Viewed by 3538

Abstract

In this article, we model the two most market-capitalised public, open and permissionless blockchain implementations, Bitcoin (BTC) and Ethereum (ETH), as a System of Systems (SoS) of public blockchains. We study the concepts of blockchain, BTC, ETH, complex networks, SoS Engineering and intentional [...] Read more.

In this article, we model the two most market-capitalised public, open and permissionless blockchain implementations, Bitcoin (BTC) and Ethereum (ETH), as a System of Systems (SoS) of public blockchains. We study the concepts of blockchain, BTC, ETH, complex networks, SoS Engineering and intentional risk. We analyse BTC and ETH from an open SoS perspective through the main properties that seminal System of Systems Engineering (SoSE) references propose. This article demonstrates that these public blockchain implementations create networks that grow in complexity and connect with each other. We propose a methodology based on a complexity management lever such as SoSE to better understand public blockchains such as BTC and ETH and manage their evolution. Our ultimate objective is to improve the resilience of public blockchains against intentional risk: a key requirement for their mass adoption. We conclude with specific measures, based on this novel systems engineering approach, to effectively improve the resilience against intentional risk of the open SoS of public blockchains, composed of a non-inflationary money system, “sound money”, such as BTC, and of a world financial computer system, “a financial conduit”, such as ETH. The goal of this paper is to formulate a SoS that transfers digital value and aspires to position itself as a distributed alternative to the fiat currency-based financial system. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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22 pages, 701 KiB

Open AccessArticle

A Survey on Data-Driven Learning for Intelligent Network Intrusion Detection Systems

by Ghada Abdelmoumin, Jessica Whitaker, Danda B. Rawat and Abdul Rahman

Electronics 2022, 11(2), 213; https://doi.org/10.3390/electronics11020213 - 11 Jan 2022

Cited by 12 | Viewed by 3459

Abstract

An effective anomaly-based intelligent IDS (AN-Intel-IDS) must detect both known and unknown attacks. Hence, there is a need to train AN-Intel-IDS using dynamically generated, real-time data in an adversarial setting. Unfortunately, the public datasets available to train AN-Intel-IDS are ineluctably static, unrealistic, and [...] Read more.

An effective anomaly-based intelligent IDS (AN-Intel-IDS) must detect both known and unknown attacks. Hence, there is a need to train AN-Intel-IDS using dynamically generated, real-time data in an adversarial setting. Unfortunately, the public datasets available to train AN-Intel-IDS are ineluctably static, unrealistic, and prone to obsolescence. Further, the need to protect private data and conceal sensitive data features has limited data sharing, thus encouraging the use of synthetic data for training predictive and intrusion detection models. However, synthetic data can be unrealistic and potentially bias. On the other hand, real-time data are realistic and current; however, it is inherently imbalanced due to the uneven distribution of anomalous and non-anomalous examples. In general, non-anomalous or normal examples are more frequent than anomalous or attack examples, thus leading to skewed distribution. While imbalanced data are commonly predominant in intrusion detection applications, it can lead to inaccurate predictions and degraded performance. Furthermore, the lack of real-time data produces potentially biased models that are less effective in predicting unknown attacks. Therefore, training AN-Intel-IDS using imbalanced and adversarial learning is instrumental to their efficacy and high performance. This paper investigates imbalanced learning and adversarial learning for training AN-Intel-IDS using a qualitative study. It surveys and synthesizes generative-based data augmentation techniques for addressing the uneven data distribution and generative-based adversarial techniques for generating synthetic yet realistic data in an adversarial setting using rapid review, structured reporting, and subgroup analysis. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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16 pages, 929 KiB

Open AccessArticle

Privacy-Enhanced MQTT Protocol for Massive IoT

by Axelle Hue, Gaurav Sharma and Jean-Michel Dricot

Electronics 2022, 11(1), 70; https://doi.org/10.3390/electronics11010070 - 27 Dec 2021

Cited by 11 | Viewed by 3792

Abstract

The growing expectations for ubiquitous sensing have led to the integration of countless embedded sensors, actuators, and RFIDs in our surroundings. Combined with rapid developments in high-speed wireless networks, these resource-constrained devices are paving the road for the Internet-of-Things paradigm, a computing model [...] Read more.

The growing expectations for ubiquitous sensing have led to the integration of countless embedded sensors, actuators, and RFIDs in our surroundings. Combined with rapid developments in high-speed wireless networks, these resource-constrained devices are paving the road for the Internet-of-Things paradigm, a computing model aiming to bring together millions of heterogeneous and pervasive elements. However, it is commonly accepted that the Privacy consideration remains one of its main challenges, a notion that does not only encompasses malicious individuals but can also be extended to honest-but-curious third-parties. In this paper, we study the design of a privacy-enhanced communication protocol for lightweight IoT devices. Applying the proposed approach to MQTT, a highly popular lightweight publish/subscribe communication protocol prevents no valuable information from being extracted from the messages flowing through the broker. In addition, it also prevents partners re-identification. Starting from a privacy-ideal, but unpractical, exact transposition of the Oblivious Transfer (OT) technology to MQTT, this paper follows an iterative process where each previous model’s drawbacks are appropriately mitigated all the while trying to preserve acceptable privacy levels. Our work provides resistance to statistical analysis attacks and dynamically supports new client participation. Additionally the whole proposal is based on the existence of a non-communicating 3rd party during pre-development. This particular contribution reaches a proof-of-concept stage through implementation, and achieves its goals thanks to OT’s indistinguishability property as well as hash-based topic obfuscations. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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16 pages, 2033 KiB

Open AccessArticle

A Privacy Preserved, Trust Relationship (PTR) Model for Internet of Vehicles

by Haleem Farman, Abizar Khalil, Naveed Ahmad, Waleed Albattah, Muazzam A. Khan and Muhammad Islam

Electronics 2021, 10(24), 3105; https://doi.org/10.3390/electronics10243105 - 14 Dec 2021

Cited by 7 | Viewed by 4316

Abstract

The Internet of vehicles (IoV) depicts a reality where ordinary things are connected to vehicular ad-hoc networks (VANETs), allowing them to transmit and collaborate. By placing these regular objects in VANETs and making them available at any time, this network and data sharing [...] Read more.

The Internet of vehicles (IoV) depicts a reality where ordinary things are connected to vehicular ad-hoc networks (VANETs), allowing them to transmit and collaborate. By placing these regular objects in VANETs and making them available at any time, this network and data sharing may raise real privacy and security issues. Thus, group-based communication is mostly preferred in the literature. However, in heavy network scenarios, cluster-based communication mostly leads to additional overload in the form of the group leader that causes delay and disrupts the performance of a network. Due to the interaction of VANETs with applications that are not stable for life, privacy and security mechanism for detecting many malicious nodes is in great demand. Therefore, a multi-phantom node selection has been proposed in this paper to select trustworthy, normal, and malicious nodes. The multi-phantom node scheme is proposed to reduce the phantom node load, where the multi-lateral nodes in a cluster act as a phantom node to share the load. A multi criteria decision-making (MCDM) methodology (analytic network process) is used to optimize the phantom node to pre-serve privacy using the privacy preserved trust relationship (PTR) model. The results show checking the stability of parameters and using sensitivity analysis by considering different scenarios for the most optimal phantom node to preserve vehicle location privacy. The impact of the proposed model will be more clearly visible in its real-time implementation in urban areas vehicle networks. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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18 pages, 907 KiB

Open AccessArticle

An Adaptive Routing Framework for Efficient Power Consumption in Software-Defined Datacenter Networks

by Mohammed Nsaif, Gergely Kovásznai, Anett Rácz, Ali Malik and Ruairí de Fréin

Electronics 2021, 10(23), 3027; https://doi.org/10.3390/electronics10233027 - 4 Dec 2021

Cited by 9 | Viewed by 2852

Abstract

Data Center Networks (DCNs) form the backbone of many Internet applications and services that have become necessary in daily life. Energy consumption causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network [...] Read more.

Data Center Networks (DCNs) form the backbone of many Internet applications and services that have become necessary in daily life. Energy consumption causes both economic and environmental issues. It is reported that 10% of global energy consumption is due to ICT and network usage. Computer networking equipment is designed to accommodate network traffic; however, the level of use of the equipment is not necessarily proportional to the power consumed by it. For example, DCNs do not always run at full capacity yet the fact that they are supporting a lighter load is not mirrored by a reduction in energy consumption. DCNs have been shown to unnecessarily over-consume energy when they are not fully loaded. In this paper, we propose a new framework that reduces power consumption in software-defined DCNs. The proposed approach is composed of a new Integer Programming model and a heuristic link utility-based algorithm that strikes a balance between energy consumption and performance. We evaluate the proposed framework using an experimental platform, which consists of an optimization tool called LinGo for solving convex and non-convex optimization problems, the POX controller and the Mininet network emulator. Compared with the state-of-the-art approach, the equal cost multi-path algorithm, the results show that the proposed method reduces the power consumption by up to 10% when the network is experiencing a high traffic load and 63.3% when the traffic load is low. Based on these results, we outline how machine learning approaches could be used to further improve our approach in future work. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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23 pages, 6967 KiB

Open AccessArticle

A New Modulated Finite Control Set-Model Predictive Control of Quasi-Z-Source Inverter for PMSM Drives

by Abdelsalam A. Ahmed, Abualkasim Bakeer, Hassan Haes Alhelou, Pierluigi Siano and Mahmoud A. Mossa

Electronics 2021, 10(22), 2814; https://doi.org/10.3390/electronics10222814 - 16 Nov 2021

Cited by 11 | Viewed by 2637

Abstract

In this paper, a new modulated finite control set-model predictive control (FCS-MPC) methodology is proposed for a quasi-Z-source inverter (qZSI). The application of the qZSI in this paper is to drive the permanent magnet synchronous machine (PMSM). The proposed methodology calculates the optimal [...] Read more.

In this paper, a new modulated finite control set-model predictive control (FCS-MPC) methodology is proposed for a quasi-Z-source inverter (qZSI). The application of the qZSI in this paper is to drive the permanent magnet synchronous machine (PMSM). The proposed methodology calculates the optimal duration time (ODT) for the candidate vector from the switching patterns of the inverter after it is selected from the FCS-MPC algorithm. The control objective of the FCS-MPC are the three-phase currents of PMSM, when the motor speed is below or equal to the base speed. While at a speed beyond the based speed, the inductor current and capacitor voltage of the qZS network are added as control objectives. For each candidate optimal vector, the optimal time, which is a part of the sampling interval, is determined based on minimizing the ripples of the control objectives using a quadratic cost function. Then, the optimal vector is applied only to the inverter switches during the calculated ODT at the start of the sampling interval, while the zero vector is applied during the remaining part of the sampling interval. To reduce the calculation burden, the zero-state is excluded from the possible states of the inverter, and the sub-cost function definition is used for the inductor current regulation. The proposed modulated FCS-MPC is compared with the unmodulated FCS-MPC at the same parameters to handle a fair comparison. The simulation results based on the MATLAB/Simulink© software shows the superiority of the proposed algorithm compared to the unmodulated FCS-MPC in terms of a lower ripple in the inductor current and capacitor voltage, and a lower THD for the PMSM currents. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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15 pages, 3417 KiB

Open AccessEditor’s ChoiceArticle

Mitigating Broadcasting Storm Using Multihead Nomination Clustering in Vehicular Content Centric Networks

by Ayesha Siddiqa, Muhammad Diyan, Muhammad Toaha Raza Khan, Malik Muhammad Saad and Dongkyun Kim

Electronics 2021, 10(18), 2270; https://doi.org/10.3390/electronics10182270 - 15 Sep 2021

Cited by 5 | Viewed by 2448

Abstract

Vehicles are highly mobile nodes; therefore, they frequently change their topology. To maintain a stable connection with the server in high-speed vehicular networks, the handover process is restarted again to satisfy the content requests. To satisfy the requested content, a vehicular-content-centric network (VCCN) [...] Read more.

Vehicles are highly mobile nodes; therefore, they frequently change their topology. To maintain a stable connection with the server in high-speed vehicular networks, the handover process is restarted again to satisfy the content requests. To satisfy the requested content, a vehicular-content-centric network (VCCN) is proposed. The proposed scheme adopts in-network caching instead of destination-based routing to satisfy the requests. In this regard, various routing protocols have been proposed to increase the communication efficiency of VCCN. Despite disruptive communication links due to head vehicle mobility, the vehicles create a broadcasting storm that increases communication delay and packet drop fraction. To address the issues mentioned above in the VCCN, we proposed a multihead nomination clustering scheme. It extends the hello packet header to get the vehicle information from the cluster vehicles. The novel cluster information table (CIT) has been proposed to maintain several nominated head vehicles of a cluster on roadside units (RSUs). In disruptive communication links due to the head vehicle’s mobility, the RSU nominates the new head vehicle using CIT entries, resulting in the elimination of the broadcasting storm effect on disruptive communication links. Finally, the proposed scheme increases the successful communication rate, decreases the communication delay, and ensures a high cache success ratio on an increasing number of vehicles. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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21 pages, 660 KiB

Open AccessFeature PaperArticle

On the Detection of Low-Rate Denial of Service Attacks at Transport and Application Layers

by Vasudha Vedula, Palden Lama, Rajendra V. Boppana and Luis A. Trejo

Electronics 2021, 10(17), 2105; https://doi.org/10.3390/electronics10172105 - 30 Aug 2021

Cited by 16 | Viewed by 3998

Abstract

Distributed denial of service (DDoS) attacks aim to deplete the network bandwidth and computing resources of targeted victims. Low-rate DDoS attacks exploit protocol features such as the transmission control protocol (TCP) three-way handshake mechanism for connection establishment and the TCP congestion-control induced backoffs [...] Read more.

Distributed denial of service (DDoS) attacks aim to deplete the network bandwidth and computing resources of targeted victims. Low-rate DDoS attacks exploit protocol features such as the transmission control protocol (TCP) three-way handshake mechanism for connection establishment and the TCP congestion-control induced backoffs to attack at a much lower rate and still effectively bring down the targeted network and computer systems. Most of the statistical and machine/deep learning-based detection methods proposed in the literature require keeping track of packets by flows and have high processing overheads for feature extraction. This paper presents a novel two-stage model that uses Long Short-Term Memory (LSTM) and Random Forest (RF) to detect the presence of attack flows in a group of flows. This model has a very low data processing overhead; it uses only two features and does not require keeping track of packets by flows, making it suitable for continuous monitoring of network traffic and on-the-fly detection. The paper also presents an LSTM Autoencoder to detect individual attack flows with high detection accuracy using only two features. Additionally, the paper presents an analysis of a support vector machine (SVM) model that detects attack flows in slices of network traffic collected for short durations. The low-rate attack dataset used in this study is made available to the research community through GitHub. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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18 pages, 1222 KiB

Open AccessEditor’s ChoiceArticle

Determination of Traffic Characteristics of Elastic Optical Networks Nodes with Reservation Mechanisms

by Maciej Sobieraj, Piotr Zwierzykowski and Erich Leitgeb

Electronics 2021, 10(15), 1853; https://doi.org/10.3390/electronics10151853 - 1 Aug 2021

Cited by 9 | Viewed by 2563

Abstract

With the ever-increasing demand for bandwidth, appropriate mechanisms that would provide reliable and optimum service level to designated or specified traffic classes during heavy traffic loads in networks are becoming particularly sought after. One of these mechanisms is the resource reservation mechanism, in [...] Read more.

With the ever-increasing demand for bandwidth, appropriate mechanisms that would provide reliable and optimum service level to designated or specified traffic classes during heavy traffic loads in networks are becoming particularly sought after. One of these mechanisms is the resource reservation mechanism, in which parts of the resources are available only to selected (pre-defined) services. While considering modern elastic optical networks (EONs) where advanced data transmission techniques are used, an attempt was made to develop a simulation program that would make it possible to determine the traffic characteristics of the nodes in EONs. This article discusses a simulation program that has the advantage of providing the possibility to determine the loss probability for individual service classes in the nodes of an EON where the resource reservation mechanism has been introduced. The initial assumption in the article is that a Clos optical switching network is used to construct the EON nodes. The results obtained with the simulator developed by the authors will allow the influence of the introduced reservation mechanism on the loss probability of calls of individual traffic classes that are offered to the system under consideration to be determined. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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25 pages, 1063 KiB

Open AccessArticle

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

by Abdullah Lakhan, Mazhar Ali Dootio, Tor Morten Groenli, Ali Hassan Sodhro and Muhammad Saddam Khokhar

Electronics 2021, 10(14), 1719; https://doi.org/10.3390/electronics10141719 - 17 Jul 2021

Cited by 31 | Viewed by 5292

Abstract

These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive [...] Read more.

These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, E-Train and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, round-trip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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26 pages, 1272 KiB

Open AccessArticle

Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0

by Mohamed Amine Ferrag, Lei Shu, Hamouda Djallel and Kim-Kwang Raymond Choo

Electronics 2021, 10(11), 1257; https://doi.org/10.3390/electronics10111257 - 25 May 2021

Cited by 152 | Viewed by 10145

Abstract

Smart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intelligent Agriculture [...] Read more.

Smart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intelligent Agriculture provides new opportunities for migration from factory agriculture to the future generation, known as Agriculture 4.0. However, since the deployment of thousands of IoT based devices is in an open field, there are many new threats in Agriculture 4.0. Security researchers are involved in this topic to ensure the safety of the system since an adversary can initiate many cyber attacks, such as DDoS attacks to making a service unavailable and then injecting false data to tell us that the agricultural equipment is safe but in reality, it has been theft. In this paper, we propose a deep learning-based intrusion detection system for DDoS attacks based on three models, namely, convolutional neural networks, deep neural networks, and recurrent neural networks. Each model’s performance is studied within two classification types (binary and multiclass) using two new real traffic datasets, namely, CIC-DDoS2019 dataset and TON_IoT dataset, which contain different types of DDoS attacks. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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26 pages, 828 KiB

Open AccessArticle

Mitigation of the Effects of Network Outage on Video QoE Using a Sender Buffer

by Tahir Nawaz Minhas and Markus Fiedler

Electronics 2021, 10(10), 1209; https://doi.org/10.3390/electronics10101209 - 19 May 2021

Cited by 2 | Viewed by 2903

Abstract

With the growth of multimedia applications and the mobile Internet, quality sense and quality expectation of the end-user are rising rapidly. A small notable distortion in the multimedia applications may degrade the degree of delight of the user, who is very considerate of [...] Read more.

With the growth of multimedia applications and the mobile Internet, quality sense and quality expectation of the end-user are rising rapidly. A small notable distortion in the multimedia applications may degrade the degree of delight of the user, who is very considerate of the video Quality of Experience (QoE). During live streaming, a network outage may result in video freezes and video jumps. To dampen the impact of a network outage on the video QoE, we propose the use of a well-sized sender buffer. We present the concept, derive key analytical relations, and perform a set of subjective tests. Based on those, we report a significant enhancement of user ratings due to the proposed sender buffer in the presence of network outages. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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26 pages, 7117 KiB

Open AccessFeature PaperArticle

Impact of People’s Movement on Wi-Fi Link Throughput in Indoor Propagation Environments: An Empirical Study

by Nurul I Sarkar, Osman Mussa and Sonia Gul

Electronics 2021, 10(7), 856; https://doi.org/10.3390/electronics10070856 - 3 Apr 2021

Cited by 3 | Viewed by 2903

Abstract

There has been tremendous growth in the deployment of Wi-Fi 802.11-based networks in recent years. Many researchers have been investigating the performance of the Wi-Fi 802.11-based networks by exploring factors such as signal interference, radio propagation environments, and wireless protocols. However, exploring the [...] Read more.

There has been tremendous growth in the deployment of Wi-Fi 802.11-based networks in recent years. Many researchers have been investigating the performance of the Wi-Fi 802.11-based networks by exploring factors such as signal interference, radio propagation environments, and wireless protocols. However, exploring the effect of people’s movement on the Wi-Fi link throughout the performance is still a potential area yet to be explored. This paper investigates the impact of people’s movement on Wi-Fi link throughput. This is achieved by setting up experimental scenarios by using a pair of wireless laptops to file share where there is human movement between the two nodes. Wi-Fi link throughput is measured in an obstructed office block, laboratory, library, and suburban residential home environments. The collected data from the experimental study show that the performance difference between fixed and random human movement had an overall average of 2.21 ± 0.07 Mbps. Empirical results show that the impact of people’s movement (fixed and random people movements) on Wi-Fi link throughput is insignificant. The findings reported in this paper provide some insights into the effect of human movement on Wi-Fi throughputs that can help network planners for the deployment of next generation Wi-Fi systems. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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24 pages, 6767 KiB

Open AccessFeature PaperArticle

A Framework for Component Selection Considering Dark Sides of Artificial Intelligence: A Case Study on Autonomous Vehicle

by Mohammad Reza Jabbarpour, Ali Mohammad Saghiri and Mehdi Sookhak

Electronics 2021, 10(4), 384; https://doi.org/10.3390/electronics10040384 - 4 Feb 2021

Cited by 8 | Viewed by 3385

Abstract

Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent [...] Read more.

Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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Review

Jump to: Research

20 pages, 10831 KiB

Open AccessReview

A Survey on Energy Harvesting Wireless Networks: Channel Capacity, Scheduling, and Transmission Power Optimization

by Nurul I. Sarkar, Dev Pal Singh and Monjur Ahmed

Electronics 2021, 10(19), 2342; https://doi.org/10.3390/electronics10192342 - 24 Sep 2021

Cited by 4 | Viewed by 2790

Abstract

This paper presents a survey on energy harvesting (EH) wireless communication networks focusing on channel capacity, transmission schemes, and power optimization. While many network researchers focus on energy management policies addressing the intermittency and randomness of the EH processes, but the channel capacity, [...] Read more.

This paper presents a survey on energy harvesting (EH) wireless communication networks focusing on channel capacity, transmission schemes, and power optimization. While many network researchers focus on energy management policies addressing the intermittency and randomness of the EH processes, but the channel capacity, and transmission power optimization have not been fully explored yet. In this paper, we provide a review and analysis of channel capacity, offline and online transmission schemes, and power optimization from an information theory perspective. By reviewing and analyzing wireless networking literature, we found that EH is a technologically feasible and economically viable paradigm for cost-effectiveness in the design and deployment of next-generation wireless networks. Finally, we identify open research problems and future research directions in the emerging field of EH wireless networks. We expect this study to stimulate more research endeavors to build energy-efficient scalable next-generation wireless network systems. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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22 pages, 4875 KiB

Open AccessReview

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

by Himanshu Sharma, Ahteshamul Haque and Frede Blaabjerg

Electronics 2021, 10(9), 1012; https://doi.org/10.3390/electronics10091012 - 23 Apr 2021

Cited by 118 | Viewed by 15979

Abstract

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart [...] Read more.

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications. Full article

(This article belongs to the Special Issue 10th Anniversary of Electronics: Advances in Networks)

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