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The integration of Software-Defined Networking (SDN) technology in Smart Grids has emerged as a transformative approach to modernizing energy infrastructures and enhancing operational efficiency. This comprehensive review paper explores the advancements, challenges, and future perspectives of SDN implementation in Smart Grid environments. It delves into the applications of SDN in areas such as real-time monitoring, energy distribution optimization, grid resilience, integration of renewable energy sources, and demand response management. Additionally, the paper addresses key challenges including security concerns, interoperability issues, scalability constraints, and regulatory compliance requirements that accompany the adoption of SDN in Smart Grids. Looking ahead, the paper discusses future perspectives such as leveraging artificial intelligence, edge computing, and blockchain technology to further enhance the capabilities of SDN in Smart Grids. Through an in-depth analysis of current developments and future trends, this review provides valuable insights for researchers, practitioners, and policymakers seeking to harness the full potential of SDN for advancing Smart Grid infrastructures.

Metaverse is a virtual world platform that enables users to create and explore 3D environments. Recently, this technology has gained traction due to its potential to revolutionize the way people interact and share information. This paper provides an overview of the current state of Metaverse technologies. It begins by introducing their core components and surveys recent research and development activities in the area. Next, the paper examines the potential applications of Metaverse technology in different domains, such as gaming, social networking, training, education, healthcare, and marketing. It then outlines the challenges and opportunities associated with the development and deployment of Metaverse technologies, including scalability, privacy, and security issues. Finally, the paper concludes with a discussion of the implications of Metaverse technologies for the future of digital communication and information sharing.

The purpose of this paper is to fill a gap in the existing literature about the general analysis of airport activities and detailed specifications for 5G implementation. The gap spans from overgeneralized studies to highly specific ones as will be mentioned in the paper. This research aims to bridge this gap by identifying the requirements of 5G implementation in various airport use cases, proposing detailed 5G solutions for these cases. As is well known, 5G is the Fifth Generation of cellular communications, aiming to provide high-throughput high-reliability connectivity to greatly diversified scenarios. With this objective, it shall act as a viable solution for environments as complex as an airport terminal, whose dynamic work cycle includes a wide range of diversified activities. As such, 5G capacities are assessed, identifying those airport processes that can benefit from its application as well as the general requirements of the network deployed for such scenarios. From the identified use cases, the streaming of high quality video for aircraft visual inspection as well as the monitoring of luggage trolleys via an IoT system are developed and evaluated, showing the capabilities of the approach.

ChatGPT, a groundbreaking natural language processing technology released a few months ago, has attracted significant attention due to its remarkable capabilities. This AI milestone has urged researchers, industry, decision-makers, and governments to examine this technology, including its implications, threats, and benefits. Despite the short period since its release, several researchers have examined ChatGPT from different perspectives. This paper presents a comprehensive review of ChatGPT, highlighting its technical novelties compared to previous models and analyzing existing research from various perspectives. We followed a rigorous methodology to conduct a critical review of existing research on ChatGPT and developed a taxonomy for the different areas of study. Additionally, we identify future challenges and research trends associated with ChatGPT. Our paper is the first critical review of ChatGPT literature, providing valuable insights for practitioners and policymakers. This paper is a reference for researchers seeking to advance research on ChatGPT, including its applications and development.

Second-order cone programming problems are a tractable subclass of convex optimization problems that can be solved using polynomial algorithms. In the last decade, stochastic second-order cone programming problems have been studied, and efficient algorithms for solving them have been developed. The mixed-integer version of these problems is a new class of interest to the optimization community and practitioners, in which certain variables are required to be integers. In this paper, we describe five applications that lead to stochastic mixed-integer second-order cone programming problems. Additionally, we present solution algorithms for solving stochastic mixed-integer second-order cone programming using cuts and relaxations by combining existing algorithms for stochastic second-order cone programming with extensions of mixed-integer second-order cone programming. The applications, which are the focus of this paper, include facility location, portfolio optimization, uncapacitated inventory, battery swapping stations, and berth allocation planning. Considering the fact that mixed-integer programs are usually known to be NP-hard, bringing applications to the surface can detect tractable special cases and inspire for further algorithmic improvements in the future.

In recent years, enterprise service bus (ESB) has become a favorable adoption as a technology category in the IT industry as it provides secure and guaranteed delivery of services. The elasticity of Enterprise Service Bus (ESB) enables numerous applications to exchange information makes it a significant middleware layer responsible for transferring information in a Service-Oriented Architecture (SOA). ESB is presently the utmost promising tactic for the integration of business applications in distributed and diverse environments. It also offers essential infrastructure support for transforming messages or data, intelligent routing, and protocol transformation. The idea of ESBs emerged from the requirements to move out from traditional integration patterns, that becomes difficult to manage with the passage of time. Our study aim is to understand and provide ongoing research topics, challenges and future directions concerning ESB applications. A systematic mapping study (SMS) is therefore implemented to categorize the selected papers into the following classification: contribution type, ESB applications, research type and their approaches. We have extracted a total of twenty-two papers for this systematic study and they are classified according to defined criteria. The findings of this SMS are discussed and researchers were provided with suggestions on possible directions for future research.

Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.

The Internet of Vehicles (IoV) is a convergence of the mobile Internet and the Internet of Things (IoT), where vehicles function as smart moving intelligent nodes or objects within the sensing network. This paper gives two contributions to the state-of-the-art for IoV technology research. First, we present a comprehensive review of the current and emerging IoV paradigms and communication models with an emphasis on deployment in smart cities. Currently, surveys from many authors have focused concentration on the IoV as only serving applications for intelligent transportation like driver safety, traffic efficiency, and infotainment. This paper presents a more inclusive review of the IoV for also serving the needs of smart cities for large-scale data sensing, collection, information processing, and storage. The second component of the paper presents a new universal architecture for the IoV which can be used for different communication models in smart cities to address the above challenges. It consists of seven layers: vehicle identification layer, object layer, inter-intra devices layer, communication layer, servers and cloud services layer, big data and multimedia computation layer, and application layer. The final part of this paper discusses various challenges and gives some experimental results and insights for future research direction such as the effects of a large and growing number of vehicles and the packet delivery success rate in the dynamic network structure in a smart city scenario.

Feature selection has been an important research area in data mining, which chooses a subset of relevant features for use in the model building. This paper aims to provide an overview of feature selection methods for big data mining. First, it discusses the current challenges and difficulties faced when mining valuable information from big data. A comprehensive review of existing feature selection methods in big data is then presented. Herein, we approach the review from two aspects: methods specific to a particular kind of big data with certain characteristics and applications of methods in classification analysis, which are significantly different to the existing review work. This paper also highlights the current issues of feature selection in big data and suggests the future research directions.

The development of IoT technologies and the massive admiration and acceptance of social media tools and applications, new doors of opportunity have been opened for using data analytics in gaining meaningful insights from unstructured information. The application of opinion mining and sentiment analysis (OMSA) in the era of big data have been used a useful way in categorizing the opinion into different sentiment and in general evaluating the mood of the public. Moreover, different techniques of OMSA have been developed over the years in different data sets and applied to various experimental settings. In this regard, this paper presents a comprehensive systematic literature review, aims to discuss both technical aspect of OMSA (techniques and types) and non-technical aspect in the form of application areas are discussed. Furthermore, this paper also highlighted both technical aspects of OMSA in the form of challenges in the development of its technique and non-technical challenges mainly based on its application. These challenges are presented as a future direction for research.

In this paper, the integration of Internet of Things (IoT) regarding recent technology is discussed in a healthcare perspective. Various healthcare devices have emerged in recent years, allowing individuals to diagnose and manage their health conditions easily at home. However, the diagnostic utilization of IoT healthcare services is not yet fully integrated with the Internet. For this reason, people still have to go, for example, through a hands-on sampling process to self-analyze their urine, which is the same for inspectors in hospital labs. Therefore, with the exception of people suffering from certain diseases (e.g., diabetes), there are only a few who check their own health status due to the inconvenience. In this paper, sensor-based urine and feces separating toilet proposes a categorizing method for the user's toilet, which applies various scenarios and analyses of the urine components using long-range wireless communication technology to transmit data from the smart toilet to the user.

This paper presents a system based on ontologies for the definition of alarms in sensor systems. Although we consider that the ontology and the system are interesting themselves, we have a special impact on their experience with the software tools used and the technology related to the Semantic Web. This approach is intended to illustrate the process for those readers not familiar with this field and who can consider the use of ontologies for their next developments. An ontology has been designed in the OWL language, complemented with Semantic Web Rule Language rules and search elements in the Semantic Query-enhanced Web Rule Language. The set has offered satisfactory results in simulation. Other interesting contributions of this paper are: a survey of the available literature in the field of the use of Semantic Web technologies and ontologies for the detection of events from data obtained from sensors and a study on the tools and vocabularies to be used to create a system that interfaces with ontologies and an educational method-reporting our experience-to help university students understand this topic.

Since the early 1990s, a large number of chaos-based communication systems have been proposed exploiting the properties of chaotic waveforms. The motivation lies in the significant advantages provided by this class of non-linear signals. For this aim, many communication schemes and applications have been specially designed for chaos-based communication systems where energy, data rate, and synchronization awareness are considered in most designs. Recently, the major focus, however, has been given to the non-coherent chaos-based systems to benefit from the advantages of chaotic signals and non-coherent detection and to avoid the use of chaotic synchronization, which suffers from weak performance in the presence of additive noise. This paper presents a comprehensive survey of the entire wireless radio frequency chaos-based communication systems. First, it outlines the challenges of chaos implementations and synchronization methods, followed by comprehensive literature review and analysis of chaos-based coherent techniques and their applications. In the second part of the survey, we offer a taxonomy of the current literature by focusing on non-coherent detection methods. For each modulation class, this paper categorizes different transmission techniques by elaborating on its modulation, receiver type, data rate, complexity, energy efficiency, multiple access scheme, and performance. In addition, this survey reports on the analysis of tradeoff between different chaos-based communication systems. Finally, several concluding remarks are discussed.

The study of signals, processes, and systems has motivated the development of different representations that can be used to analyze and understand them. Classical ways of studying the behavior of signals are the time domain and frequency domain representations. For the analysis of non-stationary signals, time-frequency representations have become an essential tool to understand how the frequency content of signals changes with time. A common time-frequency technique employed in the literature is the wavelet transform. Nevertheless, selecting an adequate mother wavelet to perform the wavelet transform has become challenging due to the diverse available wavelet families. This paper reviews the applications and uses of a particular class of wavelet basis known as the Generalized Morse Wavelets. This class of wavelet family provides a systematic framework to choose and generate a wavelet for general-purpose use. This study reviews the application of Generalized Morse Wavelets in biomedical engineering, dynamical systems analysis, electrical engineering, geophysics, and communication systems. Moreover, the parameters of the Generalized Morse Wavelets used in each study are presented. The results of this study reveal that Generalized Morse Wavelets have proven helpful in studying signals, systems, and processes in areas ranging from biomedical engineering to geophysics. Nonetheless, the parameters of the Generalized Morse Wavelets are yet to be chosen through a rigorous methodology and argumentation. Therefore, there is an opportunity to generate methods for selecting the parameters of the Generalized Morse Wavelets based on the characteristics of the signals, systems, or processes under research.

The Internet of Things (IoT) makes smart objects the ultimate building blocks in the development of cyber-physical smart pervasive frameworks. The IoT has a variety of application domains, including health care. The IoT revolution is redesigning modern health care with promising technological, economic, and social prospects. This paper surveys advances in IoT-based health care technologies and reviews the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. In addition, this paper analyzes distinct IoT security and privacy features, including security requirements, threat models, and attack taxonomies from the health care perspective. Further, this paper proposes an intelligent collaborative security model to minimize security risk; discusses how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context; addresses various IoT and eHealth policies and regulations across the world to determine how they can facilitate economies and societies in terms of sustainable development; and provides some avenues for future research on IoT-based health care based on a set of open issues and challenges.

The cross-industry concept of Digital Twin promises numerous benefits in areas such as product customization and predictive maintenance, but many companies often struggle to determine a starting point. Digital Twin use cases are abundant, but efforts and stakeholder benefits are difficult to estimate when developing and implementing Digital Twin applications. This paper proposes a management approach to Digital Twin use case prioritization suitable for planning Digital Twin applications at an early phase of development. Considering stakeholder satisfaction, infrastructure scalability, and effort for implementation and maintenance, we present a methodology to determine the most impactful Digital Twin use cases requiring low effort and high scalability. Tools and related methods from the fields of software development, innovation, process engineering, and product development are described, and the methodology is discussed with regard to these and other research works. An example from mechatronic product development at Siemens Healthineers Innovation Think Tank validates the approach.

Drones, or unmanned aerial vehicles (UAVs), are among the most beneficial and emerging technologies, with a wide range of applications that can support the sustainability concerns of smart cities and ultimately improve citizens’ quality of life. The goals of this systematic review were to explore the involvement of surveillance drones in smart cities in terms of application status, application areas, proposed models, and characteristics of drones. We conducted this systematic review based on the preferred reporting items for systematic reviews and meta-analyzes (PRISMA) guidelines. We systematically searched the Web of Science and Scopus for journal articles and conference papers written in English and published up to August 2021. Of the 323 records identified, 43 met the inclusion criteria. Findings showed that surveillance drones were used in seven distinct research fields (transportation, environment, infrastructure, object or people detection, disaster management, data collection, and other applications). Air pollution and traffic monitoring were the dominant application areas. The majority of reviewed models were based on the application of rotary-wing single-drones with the camera as the aerial sensor. Reviewed models showed that the adoption of a single or multiple UAVs, either as a stand-alone technology or integrated with other technologies (e.g., internet of things, wireless sensor networks, convolutional neural networks, artificial intelligence, machine learning, computer vision, cloud computing, web applications), can offer efficient and sustainable solutions compared to conventional surveillance methods. This review can benefit academic researchers and practitioners.

For this study, the researchers conducted a systematic literature review to answer complex questions about the field of blockchain technology. We used an unbiased systematic review process to find works on blockchain-based applications and developed a Python code that searched various online databases. This paper provides an overview of the characteristics, mode of operation, and applications of blockchains in various domains such as transportation, commerce and industry, privacy and security, the financial sector, government, education, healthcare, and the Internet of Things (IoT). The aim was to identify the key research themes addressed in existing articles within each application domain and suggest future research directions for these domains. We analyzed a set of 750 articles published between 2015 and 2021 that dealt with blockchain applications. We found that financial management and security issues have been the main research focus since 2015. However, the use of blockchain in education has become a central research theme in 2021. Healthcare, IoT, and government applications have also grown in popularity. We furthermore analyzed some of the implementations of privacy mechanisms, as well as the challenges and future directions that need to be addressed for effective blockchain deployment. This study contributes to existing research by providing a comprehensive overview of blockchain application themes and their emerging areas for stakeholders in diverse sectors.

Along the last decades, Robotics research has taken a major turn from laboratories to factories and ordinary real-world environments. Consequently, new issues to be overcome have arisen, specially when autonomous, dexterous robots are in place. In this paper, we present this evolution in the case of robot vision for manipulation through several robot developments, by analysing their challenges and proposed solutions. This overview highlights the need of using different techniques depending on the task at hand and the scenario to work in.

The connectivity of heterogeneous components is the key factor behind the notion of the Internet of Things (IoT). Typically, IoT applications involve several communication protocols that are developed based on heterogeneous data models. This has complicated the connectivity within IoT applications. It has also caused significant interoperability issues. Therefore, in this paper we propose a novel connectivity layer which we refer to as the Distributed Data Interoperability Layer (DDIL). DDIL aims at addressing the connectivity issues that arise due to the heterogeneity of data models. In the approach, we construct DDIL into different software components. We then describe these components as a set of configurable features to allow DDIL to be tailored based on the requirements of each application. DDIL has the capabilities to address both syntactic and semantic interoperability. The feature-oriented design of DDIL provides required flexibility which is a key concern in several IoT applications. Additionally, DDIL supports backward compatibility. It also allows utilizing preexisting technologies which supports rapid development of applications. We implemented the approach in a simulated smart grid environment. The results prove that DDIL has the capabilities to support the connectivity of different applications even if they are developed based on different protocols and heterogeneous data models.

The digital contact tracing applications are one of the many initiatives to fight the COVID-19 virus. Some of these Apps use the Exposure Notification (EN) system available on Google and Apple’s operating systems. However, EN-based contact tracing Apps depend on the availability of Bluetooth interfaces to exchange proximity identifiers, which, if compromised, directly impact their effectiveness. This paper discloses and details the Advertising Overflow attack, a novel internal Denial of Service (DoS) attack targeting the EN system on Android devices. The attack is performed by a malicious App that occupies all the Bluetooth advertising slots in an Android device, effectively blocking any advertising attempt of EN or other Apps. The impact of the disclosed attack and other previously disclosed DoS-based attacks, namely Battery Exhaustion and Storage Drain, were tested using two target smartphones and other six smartphones as attackers. The results show that the Battery Exhaustion attack imposes a battery discharge rate 1.95 times higher than in the normal operation scenario. Regarding the Storage Drain, the storage usage increased more than 30 times when compared to the normal operation scenario results. The results of the novel attack reveal that a malicious App can prevent any other App to place their Bluetooth advertisements, for any chosen time period, thus canceling the operation of the EN system and compromising the efficiency of any COVID contact tracing App using this system.

Multifarious flexure-based displacement amplifiers have been proposed and studied in the past decades, showing importance in many industrial fields, such as bioengineering, optical instruments, and semiconductor technology. Displacement amplifiers provide precise motion and large stroke through a simple and low-cost way compared with other piezoelectric actuators. Those merits have opened a door for new and advanced micro devices with unprecedented performance. This paper aims to proffer a comprehensive review on the design, modeling, characteristics, and applications of flexure-based displacement amplifiers, following by pointing out the inherent drawbacks in this research area such as amplification ratio limit, parasitic motion, low lateral stiffness, low natural frequency, and discussing existing solutions and some potential research directions in those topics. Finally, a summary is concluded and the future development perspectives of the displacement amplifiers are discussed. This review contributes to giving a comprehensive understanding of the displacement amplifier, which provides guidance on designing new displacement amplifiers for improving their mechanical output performance. It is also expected to be instrumental for related researchers to understand displacement amplifiers, and to successfully select and design for specific applications.

The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneously, dramatic improvements in machine learning and deep neural network architectures have enabled unprecedented analytical capabilities, which we see in increasingly common applications and production technologies, such as self-driving vehicles and intelligent mobile applications. Predictably, these technologies have seen rapid adoption, which has left many implementations vulnerable to threats unforeseen or undefended against. Moreover, such technologies can be used by malicious actors, and the potential for cyber threats, attacks, intrusions, and obfuscation that are only just being considered, applied, and countered. In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT. In detail, we consider the numerous benefits (good use) that machine learning has brought, both in general, and specifically for security and CPS/IoT, such as the improvement of intrusion detection mechanisms and decision accuracy in CPS/IoT. More pressing, we consider the vulnerabilities of machine learning (bad use) from the perspectives of security and CPS/IoT, including the ways in which machine learning systems can be compromised, misled, and subverted at all stages of the machine learning life-cycle (data collection, pre-processing, training, validation, implementation, etc.). Finally, the most concerning, a growing trend has been the utilization of machine learning in the execution of cyberattacks and intrusions (ugly use). Thus, we consider existing mechanisms with the potential to improve target acquisition and existing threat patterns, as well as those that can enable novel attacks yet to be seen.

Due to the centralized control, network-wide monitoring and flow-level scheduling of software-defined-networking (SDN), it can be utilized to achieve quality of service (QoS) for cloud applications and services, such as voice over IP, video conference, and online games. However, most existing approaches stay at the QoS framework design and test level, while few works focus on studying the basic QoS techniques supported by SDN. In this paper, we enable SDN with QoS guaranteed abilities, which could provide end-to-end QoS routing for each cloud user service. First of all, we implement an application identification technique on SDN controller to determine required QoS levels for each application type. Then, we implement a queue scheduling technique on SDN switch. It queues the application flows into different queues and schedules the flows out of the queues with different priorities. At last, we evaluate the effectiveness of the proposed SDN-based QoS technique through both theoretical and experimental analysis. Theoretical analysis shows that our methods can provide differentiated services for the application flows mapped to different QoS levels. Experiment results show that when the output interface has sufficiently available bandwidth, the delay can be reduced by 28% on average. In addition, for the application flow with the highest priority, our methods can reduce 99.99% delay and increase 90.17% throughput on average when the output interface utilization approaches to the maximum bandwidth limitation.