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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 37049

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Special Issue Editor

Dr. Daehan Kwak

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Guest Editor

School of Computer Science, Kean University, Union, NJ 07083, USA
Interests: systems & networking; iot-enabled cyber physical systems; wireless & sensor systems; ubiquitous computing

Special Issue Information

The emerging trends and new generations of cyber-physical systems (CPS) are changing the paradigm of existing systems, offering a vast amount of potential killer applications. Advances in communication, sensing and data analytics, exploitation of big data, data fusion techniques, drones, robotics, new human–computer interaction, and new solutions that provide artificial intelligence and machine learning are rapidly proliferating innovations to a wide range of CPS applications.

Modern CPS applications can be found anywhere in various domains ranging from smart homes and buildings, smart cities, energy management, smart grid systems, environmental sensing, smart health, process control, smart manufacturing and logistics (Industry 4.0) to intelligent transportation systems, electric and autonomous cars, security and privacy, military systems, smart learning, human-in-the-loop systems, etc. CPS needs to achieve scalability, adaptability, efficiency, autonomy, usability, reliability, resiliency, safety, and security; thus, CPS applications must be smart. Challenges still remain in the numerous domains and trends collectively or separately to achieve ground-breaking innovations.

This Special Issue aims to cover the recent emerging trends and applications for smart cyber-physical systems. We invite articles on the innovative research challenges of applications for smart CPS. Topics of interest include but are not limited to:

Machine learning/Artificial Intelligence approaches for CPS applications;
Ontology-based models for CPS applications;
Data mining and analytics for CPS applications;
Data fusion for CPS applications;
Privacy and security for CPS applications;
CPS applications for smart transportation;
CPS applications for smart health;
CPS applications for smart factory;
CPS applications for smart grid;
User studies for CPS applications;
Smart sensing for CPS applications;
Mobile and cloud for CPS applications;
Networking for CPS applications;
Education for CPS applications;
Human-in-the-loop in CPS applications.

Dr. Daehan Kwak
Guest Editor

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Published Papers (9 papers)

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Research

18 pages, 1540 KiB

Open AccessArticle

Wearable IoTs and Geo-Fencing Based Framework for COVID-19 Remote Patient Health Monitoring and Quarantine Management to Control the Pandemic

by Farman Ullah, Hassan Ul Haq, Jebran Khan, Arslan Ali Safeer, Usman Asif and Sungchang Lee

Electronics 2021, 10(16), 2035; https://doi.org/10.3390/electronics10162035 - 23 Aug 2021

Cited by 26 | Viewed by 5081

Abstract

The epidemic disease of Severe Acute Respiratory Syndrome (SARS) called COVID-19 has become a more frequently active disease. Managing and monitoring COVID-19 patients is still a challenging issue for advanced technologies. The first and foremost critical issue in COVID-19 is to diagnose it timely and cut off the chain of transmission by isolating the susceptible and patients. COVID-19 spreads through close interaction and contact with an infected person. It has affected the entire world, and every country is facing the challenges of having adequate medical facilities along with the availability of medical staff in rural and urban areas that have a high number of patients due to the pandemic. Due to the invasive method of treatment, SARS-COVID is spreading swiftly. In this paper, we propose an intelligent health monitoring framework using wearable Internet of Things (IoT) and Geo-fencing for COVID-19 susceptible and patient monitoring, and isolation and quarantine management to control the pandemic. The proposed system consists of four layers, and each layer has different functionality: a wearable sensors layer, IoT gateway layer, cloud server layer, and client application layer for visualization and analysis. The wearable sensors layer consists of wearable biomedical and GPS sensors for physiological parameters, and GPS and Wi-Fi Received Signal Strength Indicator acquisition for health monitoring and user Geo-fencing. The IoT gateway layer provides a Bluetooth and Wi-Fi based wireless body area network and IoT environment for data transmission anytime and anywhere. Cloud servers use Raspberry Pi and ThingSpeak cloud for data analysis and web-based application layers for remote monitoring based on user consent. The susceptible and patient conditions, real-time sensor’s data, and Geo-fencing enables minimizing the spread through close interaction. The results show the effectiveness of the proposed framework. Full article

(This article belongs to the Special Issue Applications for Smart Cyber Physical Systems)

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18 pages, 2086 KiB

Open AccessArticle

A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server’s CPU Usage Prediction

by Qazi Zia Ullah, Gul Muhammad Khan, Shahzad Hassan, Asif Iqbal, Farman Ullah and Kyung Sup Kwak

Electronics 2021, 10(1), 67; https://doi.org/10.3390/electronics10010067 - 1 Jan 2021

Cited by 6 | Viewed by 3198

Abstract

Cloud computing use is exponentially increasing with the advent of industrial revolution 4.0 technologies such as the Internet of Things, artificial intelligence, and digital transformations. These technologies require cloud data centers to process massive volumes of workloads. As a result, the data centers consume gigantic amounts of electrical energy, and a large portion of data center electrical energy comes from fossil fuels. It causes greenhouse gas emissions and thus ensuing in global warming. An adaptive resource utilization mechanism of cloud data center resources is vital to get by with this huge problem. The adaptive system will estimate the resource utilization and then adjust the resources accordingly. Cloud resource utilization estimation is a two-fold challenging task. First, the cloud workloads are sundry, and second, clients’ requests are uneven. In the literature, several machine learning models have estimated cloud resources, of which artificial neural networks (ANNs) have shown better performance. Conventional ANNs have a fixed topology and allow only to train their weights either by back-propagation or neuroevolution such as a genetic algorithm. In this paper, we propose Cartesian genetic programming (CGP) neural network (CGPNN). The CGPNN enhances the performance of conventional ANN by allowing training of both its parameters and topology, and it uses a built-in sliding window. We have trained CGPNN with parallel neuroevolution that searches for global optimum through numerous directions. The resource utilization traces of the Bitbrains data center is used for validation of the proposed CGPNN and compared results with machine learning models from the literature on the same data set. The proposed method has outstripped the machine learning models from the literature and resulted in 97% prediction accuracy. Full article

(This article belongs to the Special Issue Applications for Smart Cyber Physical Systems)

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19 pages, 3529 KiB

Open AccessArticle

A Machine Learning Approach for 5G SINR Prediction

by Ruzat Ullah, Safdar Nawaz Khan Marwat, Arbab Masood Ahmad, Salman Ahmed, Abdul Hafeez, Tariq Kamal and Muhammad Tufail

Electronics 2020, 9(10), 1660; https://doi.org/10.3390/electronics9101660 - 12 Oct 2020

Cited by 18 | Viewed by 5599

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are envisaged to play key roles in 5G networks. Efficient radio resource management is of paramount importance for network operators. With the advent of newer technologies, infrastructure, and plans, spending significant radio resources on estimating channel conditions in mobile networks poses a challenge. Automating the process of predicting channel conditions can efficiently utilize resources. To this point, we propose an ML-based technique, i.e., an Artificial Neural Network (ANN) for predicting SINR (Signal-to-Interference-and-Noise-Ratio) in order to mitigate the radio resource usage in mobile networks. Radio resource scheduling is generally achieved on the basis of estimated channel conditions, i.e., SINR with the help of Sounding Reference Signals (SRS). The proposed Non-Linear Auto Regressive External/Exogenous (NARX)-based ANN aims to minimize the rate of sending SRS and achieves an accuracy of R = 0.87. This can lead to vacating up to 4% of the spectrum, improving bandwidth efficiency and decreasing uplink power consumption. Full article

(This article belongs to the Special Issue Applications for Smart Cyber Physical Systems)

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14 pages, 2248 KiB

Open AccessArticle

A Lightweight Authentication Scheme for V2G Communications: A PUF-Based Approach Ensuring Cyber/Physical Security and Identity/Location Privacy

by Masoud Kaveh, Diego Martín and Mohammad Reza Mosavi

Electronics 2020, 9(9), 1479; https://doi.org/10.3390/electronics9091479 - 9 Sep 2020

Cited by 44 | Viewed by 3788

Abstract

Vehicle-to-grid (V2G) technology has become a promising concept for the near future smart grid eco-system. V2G improves smart grid resiliency by enabling two-way communication and electricity flows while reducing the greenhouse gases emission. V2G practicality and stability is strongly based on the exchanged data between electrical vehicles (EVs) and the grid server (GS). However, using communication protocols to exchange vital information leads grid to being vulnerable against various types of attack. To prevent the well-known attacks in V2G network, this paper proposes a privacy-aware authentication scheme that ensures data integrity, confidentiality, users’ identity and location privacy, mutual authentication, and physical security based on physical unclonable function (PUF). Furthermore, the performance analysis shows that the proposed scheme outperforms the state-of-the-art, since EVs only use lightweight cryptographic primitives for every protocol execution. Full article

(This article belongs to the Special Issue Applications for Smart Cyber Physical Systems)

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22 pages, 5135 KiB

Open AccessArticle

Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

by Mai Ramadan Ibraheem, Shaker El-Sappagh, Tamer Abuhmed and Mohammed Elmogy

Electronics 2020, 9(9), 1443; https://doi.org/10.3390/electronics9091443 - 4 Sep 2020

Cited by 3 | Viewed by 3569

Abstract

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques. Full article

(This article belongs to the Special Issue Applications for Smart Cyber Physical Systems)

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18 pages, 3249 KiB

Open AccessArticle

Medium Access-Based Scheduling Scheme for Cyber Physical Systems in 5G Networks

by Safdar Nawaz Khan Marwat, Muhammad Shuaib, Salman Ahmed, Abdul Hafeez and Muhammad Tufail

Electronics 2020, 9(4), 639; https://doi.org/10.3390/electronics9040639 - 13 Apr 2020

Cited by 3 | Viewed by 2515

Abstract

The development of the 5G mobile communication standard attempts to meet the future needs of data users. The impact of Cyber Physical Systems (CPS) is crucial in Internet of Things (IoT) and other emerging technologies. The design of medium access mechanisms for CPS such as radio resource scheduling schemes has a significant effect on network performance. Recent literature shows that limited work is available on uplink scheduling schemes, particularly in the 5G domain. Planning a network that can address the modern needs of users entails efficient CPS scheduling mechanisms such that resources are amicably distributed between users of contrasting priorities. The prime focus of this work is to design and develop an uplink radio resource scheduling framework for CPS-based future networks such as 5G. In the designed framework, scarce radio resources are sought to be distributed efficiently according to the service-based needs of users. The proposed scheduling scheme is a service aware (SA) scheduler designed for CPS in accordance with the 5G network peculiarities, intended to achieve higher throughput and reduced latency. The proposed SA scheduler supports multi-bearer traffic and is capable of providing resources in adverse channel conditions in an efficient manner. The SA scheduling mechanism’s performance is evaluated and compared with renowned scheduling algorithms such as blind equal throughput (BET), maximum throughput (MT), and proportional fair (PF) scheduling schemes. The simulation results obtained in a cellular environment demonstrate that the SA scheduler achieves acceptable cell throughput and end-to-end delay results in all scenarios and out-performs other contemporary scheduling schemes. Full article

(This article belongs to the Special Issue Applications for Smart Cyber Physical Systems)

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21 pages, 5969 KiB

Open AccessArticle

Applications of Extreme Gradient Boosting for Intelligent Handovers from 4G To 5G (mm Waves) Technology with Partial Radio Contact

by Saad Ijaz Majid, Syed Waqar Shah and Safdar Nawaz Khan Marwat

Electronics 2020, 9(4), 545; https://doi.org/10.3390/electronics9040545 - 25 Mar 2020

Cited by 6 | Viewed by 3183

Abstract

In a network topology, where 5G (mm Waves) have better coverage footprint compared to 4G (LTE or LTE-A) technology, mobile devices would generally be handed over from 4G to 5G. In this work, a supervised intelligent prediction technique for improved handover success rate (HSR) from 4G to 5G technology is proposed. The technique is applicable for base stations enabled with sub-6-GHz and mm-wave bands. This technique is novel since it can predict HSR even before switching to 5G radio circuitry or initiating its measurement gap for acquisition of mm-wave reference signal received power (RSRP) unlike conventional algorithms. Thus, preempting all handovers which are likely to fail will provide improvements in latency, delay, and handover success rate, as well as decrease call drops. Therefore, this research work answers previous research shortcomings and can unleash applications of supervised intelligent algorithms for predicting the HSR from 4G to 5G. The proposed algorithm is validated by showing improvements obtained through simulation results performed using Python-based framework. The proposed algorithm is tested for reliability with increasing parameters such as the intensity number of UEs and simulation time. Improvements in standard handover algorithm are also proposed. Full article

(This article belongs to the Special Issue Applications for Smart Cyber Physical Systems)

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20 pages, 3788 KiB

Open AccessArticle

Mobile Wi-Fi Based Scheduling of Cyber-Physical Systems in Healthcare

by Safdar Nawaz Khan Marwat, Yasir Mehmood, Farman Ullah, Ahmad Khan, Shahid Khan, Salman Ahmed, Daehan Kwak and Afia Nazir

Electronics 2020, 9(2), 247; https://doi.org/10.3390/electronics9020247 - 2 Feb 2020

Cited by 1 | Viewed by 3747

Abstract

Wireless Body Area Networks (WBANs) and Wireless Local Area Networks (WLANs) have been widely regarded as solution providers for future Cyber-Physical Systems (CPS)-based ehealthcare amenities. The IEEE 802.11 standard specifies media access protocols in wireless networks, along with channel access methods. WBANs are expected to improve the existing healthcare services significantly, but several research challenges also have to be tackled for apt utilization of the technology. Guarantee of Quality-of-Service (QoS) differentiation between various health parameters, such as temperature and blood pressure, during mobility is a major challenge for the provision of ehealthcare services. The scheme proposed in this paper for the Mobile Wi-Fi based connectivity of WBANs is designed to provide QoS-based priorities for ehealthcare subscribers by altering the Contention Window (CW) for different applications of patient health monitoring. The relationship between CW and QoS is utilized to achieve efficient resource assignment. Three different health parameters, i.e., ECG (Electrocardiogram), BP (blood pressure) and temperature. are monitored using medical CPS in this work. The performance evaluation results, such as end-to-end packet delay and throughput for various data traffic classes reveal that the proposed scheme improves QoS provision. Full article

(This article belongs to the Special Issue Applications for Smart Cyber Physical Systems)

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17 pages, 7906 KiB

Open AccessFeature PaperArticle

A Framework for Analyzing and Testing Cyber–Physical Interactions for Smart Grid Applications

by Mohamad El Hariri, Tarek Youssef, Mahmoud Saleh, Samy Faddel, Hany Habib and Osama A. Mohammed

Electronics 2019, 8(12), 1455; https://doi.org/10.3390/electronics8121455 - 1 Dec 2019

Cited by 7 | Viewed by 3542

Abstract

The reliable performance of the smart grid is a function of the configuration and cyber–physical nature of its constituting sub-systems. Therefore, the ability to capture the interactions between its cyber and physical domains is necessary to understand the effect that each one has on the other. As such, the work in this paper presents a co-simulation platform that formalizes the understanding of cyber information flow and the dynamic behavior of physical systems, and captures the interactions between them in smart grid applications. Power system simulation software packages, embedded microcontrollers, and a real communication infrastructure are combined together to provide a cohesive smart grid cyber–physical platform. A data-centric communication scheme, with automatic network discovery, was selected to provide an interoperability layer between multi-vendor devices and software packages, and to bridge different protocols. The effectiveness of the proposed framework was verified in three case studies: (1) hierarchical control of electric vehicles charging in microgrids, (2) International Electrotechnical Committee (IEC) 61850 protocol emulation for protection of active distribution networks, and (3) resiliency enhancement against fake data injection attacks. The results showed that the co-simulation platform provided a high-fidelity design, analysis, and testing environment for cyber information flow and their effect on the physical operation of the smart grid, as they were experimentally verified, down to the packet, over a real communication network. Full article

(This article belongs to the Special Issue Applications for Smart Cyber Physical Systems)

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