With the explosion of ChatGPT, the development of artificial intelligence technology ushered in another explosion. Similarly, with the rapid development of extended reality technology and Internet of Things technology, Metaverse will also usher in greater breakthroughs. However, in the development of the extended reality metaverse under edge computing, many security issues will arise. This paper focuses on data security, considering that data will be transmitted and processed between multiple devices and nodes instead of being concentrated in the cloud, which may bring To solve data security issues, relying on the integrated architecture of Metaverse educational applications based on edge computing (MEC), it provides an identity verification and access with higher security and scalability, better performance, and service requirements that meet the current environment Control mechanism solutions, while analyzing other problems that will arise during the development of the extended reality metaverse. Aiming at the security problem on the edge side, a signature authentication scheme is designed based on Elliptic Curve Cryptography (ECC) integrated blockchain encryption technology and the effectiveness of the method is proved. In order to promote the extended reality metaverse under edge computing, it provides a mirror for the application in the field of education under the condition of ensuring data security.
WiFi is a ubiquitous protocol, but exhibits flaws that become particularly critical for teams of robots in human environments. We demonstrate that our Time-Triggered WiFi (TTWiFi) protocol allows us to utilise the benefits of the hardware available in mobile robotic systems while ensuring resilience and bounded error detection in the time domain as required by teams of robots to make reliable real-time decisions. Our experiments demonstrate that TTWiFi performs equally well in static and mobile scenarios in retaining its resilience to interference.
Over the last few years, generation of Internet data by the users has drastically increased. As this data is accessed, processed, shared, and delivered through the data centers (DC), therefore bandwidth requirement for DC is increasing. Although, wired technologies are being used, but at the cost of high maintenance, limited flexibility, and high energy consumption. Wireless Terahertz (THz) links can help to overcome these challenges of wired technologies and can also provide high data rates upto Terabits-per-second (Tbps) with low latency due to very high bandwidth availability. However, THz band itself has some challenges including high spreading, molecular, and material penetration losses. In the same way, the challenges for a DC includes its unique environmental and traffic characteristics which are in form of blockages, and different packet sizes (small and long flows) and traffic patterns. With these factors communication in DC can become challenging, so to ensure reliable and efficient communication it is necessary to understand the effect of these factors in the existing THz Medium Access Control (MAC) protocols. In this paper, we compare the existing THz MAC protocols, considering the unique environment and characteristics of DC like blockages and different packet sizes which will provide the basis to design and develop new THz MAC protocols. These protocols are implemented and evaluated on NS-3, and results show that the performance decreases when the unique requirements of THz band and DC environment are considered.
In an era of rapidly evolving mobile computing, integrating satellite technologies with the Internet of Things (IoT) creates new communication and data management horizons. Our research focuses on the emerging challenge of efficiently managing heavy computing tasks in satellite-based mist computing environments. These tasks, crucial in fields ranging from satellite communication optimization to blockchain-based IoT processes, demand significant computational resources and timely execution. Addressing these challenges, we propose a novel orchestration algorithm, K-Closest Load-balanced Selection (KLS), explicitly designed for satellite-based mist computing. This innovative approach prioritizes the selection of mist satellites based on proximity and load balance, optimizing task deployment and performance. Our experimentation involved varying the percentages of mist layer devices and implementing a round-robin principle for equitable task distribution. The results showed promising outcomes in terms of energy consumption, end-to-end delay, and network usage times, highlighting the algorithm's effectiveness in specific scenarios. However, it also highlighted areas for future improvements, such as CPU utilization and bandwidth consumption, indicating the need for further refinement. Our findings contribute significant insights into optimizing task orchestration in satellite-based mist computing environments, paving the way for more efficient, reliable, and sustainable satellite communication systems.
The purpose of this research article is to present an online intelligent pilot medical system designed to support the existing Greek pre-hospital medical care system. The proposed system effectively dispatches the available ambulances when an incident occurs and provides high quality medical services to the patients as well as transportation to the appropriate hospital. The evaluation of the proposed system, benchmarked via the paired t-test statistical tests and using the TIBCO business studio, shows a significant performance improvement on both the overall time to respond and the associated costs.
Developing an electronic voting system that would replace the old, traditional electing procedures has been a concern of many researchers for years. Blockchain technology could provide some guarantees for voting platforms, such as transparency, immutability, and confidentiality. In most research works, secure and reliable electronic voting systems are required to address known security, anonymity, and fraud issues. This paper presents a secure decentralized electronic voting system, named the EtherVote, which is based on the Ethereum Blockchain network focusing on eligible citizens' identification. The EtherVote is a serverless e-voting model, thus improving security, privacy, and election costs. An effective method for voter registration and identification to enhance security is proposed. Among the main properties the EtherVote holds are storing encrypted votes, efficiency in handling elections with numerous participants, and simplicity. The system is tested and evaluated, vulnerabilities and possible attacks are exposed, and a discussion examines opportunities for enhancing the proposed e-voting system.
This study aims to deeply explore the impact of mobile edge computing based on extended reality technology on the multi-person linkage of the Zhijiang Peace Culture Memorial Hall under the immersive experience. In the part of research background and significance, it discusses the rise of the application of XR technology in the field of culture and education, emphasizing the importance of education on the history of revolution and its value in the social sense. Then, it expounds the core topic of this research aimed at integrating XR technology with education on the history of revolution, and presenting visitors with more realistic and immersive historical scenes by creating an immersive experience of multi-person linkage. The use of these technologies enables visitors to transcend time and space barriers, directly participate in historical events, and deeply appreciate the intrinsic value of red culture. In order to achieve multi-person linkage, this study adopts mobile edge computing technology to ensure that multiple visitors can realize real-time interaction in the virtual scene and jointly build a collective experience of the Metaverse. This research has achieved positive results in the fields of XR technology application, education on the history of revolution, and mobile edge computing. It provides a useful reference for the modernization and upgrading of the Zhijiang Peace Culture Memorial Hall, and also provides a new paradigm for the cultural promotion of multi-person linkage experience in Metaverse.
Aiming at the problem of quality assurance of intelligent logistics service in 5G+ edge computing environment, this paper proposes a mechanism based on federated cooperative cache, which aims to utilize the computing and storage resources of edge nodes to realize rapid processing and sharing of logistics data and improve the efficiency and reliability of logistics services. This paper first analyzes the characteristics and challenges of intelligent logistics services under 5G+ edge computing environment, and then introduces the concept and principle of federated cooperative cache, as well as its application scenarios and advantages in intelligent logistics services. Then, this paper designs an intelligent logistics service quality assurance mechanism based on federated cooperative cache, including five modules such as data partitioning, data transmission, data fusion, data access and data update, and gives the corresponding algorithms and processes. Finally, this paper verifies the effectiveness and performance of the proposed mechanism through simulation experiments. Compared with the traditional centralized cache and distributed cache, the proposed mechanism can reduce the data transmission delay, improve the data hit rate and data consistency, so as to ensure the quality of intelligent logistics services. In the future, the federated collaborative cache mechanism can be further optimized to consider the needs of multiple scenarios. And explore the application potential of other areas to drive the continuous development and innovation of intelligent logistics services.
The objective of this research is to provide an overview of digital services in the health sector. The emergence of innovative digital services has been accompanied by a multitude of issues and problems pertaining to privacy and security. To effectively tackle the issues around privacy and security in the realm of digital health, it is important to consider the principles established by international organizations. Moreover, a thorough analysis of the existing regulatory framework and unresolved issues in digital health is essential. Addressing these challenges effectively requires a unified approach that can lead to the implementation of robust solutions. Furthermore, this paper discusses the obstacles both developed and developing countries face regarding digital health, underscoring the need for a unified and international viewpoint.
Nowadays, the need to explain the decisions or predictions made by Artificial Intelligence (AI) is emerging more than ever as AI applications are more complex. The research field of eXplainable Artificial Intelligence (XAI) tries to fulfill this need. XAI provides a way to help humans understand how an AI's predictions and decisions come. The scope of this work is to examine the role of XAI in the field of Education, especially in Educational Data Mining in Vocational Education and Training.
Supervised Machine Learning (ML) algorithms are used for making predictions or decisions based on labeled data. In this paper, an overview about existing supervised ML algorithms is performed. In particular, the algorithms that are studied comprehend the Linear Regression (LR), the Ridge Regression (RR), the Decision Tree (DT), as well as Ensemble algorithms. Subsequently, a comparative analysis of the algorithms is performed using a dataset containing data about ship engines. Effective management of ship engines is important for their robust operation, which can then bring significant economic and environmental benefits. Inferences about the condition of engines and predictions about their performance could prove crucial for specifying optimal cruise parameters, early fault detection and timely service planning. The analysis demonstrates the strength and the weaknesses of each algorithm in terms of predicting decay factors of the ship engine by taking into consideration the data included to the aforementioned dataset.
Nowadays, Vehicle-to-everything (V2X) is one of the main emerging technologies attracting significant interest of researchers and industries who aim to improve traffic efficiency. C-V2X which stands for Cellular Vehicle-to-everything is a technology that enables communication between vehicles (V2V), vehicles and infrastructure (V2I), vehicles and pedestrians (V2P), and vehicles and other devices (V2D) using cellular networks (LTE or 5G). DSRC which stands for Dedicated Short-Range Communication Standard, specifically IEEE 802.11p, is a communication standard enabling vehicles to exchange real-time information with each other and roadside infrastructure within short distances. VANET DSRC's main purpose is to enhance road safety and improve traffic efficiency. Another essential system which in turn contributes to improved traffic efficiency and reduced congestion in urban areas is the Smart Parking System (SPS). SPS is a technology-driven approach to managing parking spaces more efficiently. In This paper, integration of a Smart Parking System with C-V2X and VANET(DSRC) is proposed, we suggest a new C-V2X and VANET (DSRC) based End-to-End guidance scheme for smart parking. The C-V2X network provides external guidance in this system, while the VANET (DSRC) infrastructure handles internal guidance. By integrating both external and internal guidance, we offer parking users a comprehensive End-to-End guidance experience. This paper introduces the fundamental aspects of the proposal, which will be further developed and validated through simulation in our future works.
The transition to an intelligent electrical network that is more respectful to the environment and consumers' needs requires the adoption of renewable energies. However, and despite the progress made in this area, renewable energies present significant constraints, such as their intermittency. Therefore, the convergence between the worlds of energy and 5G/6G network techniques offers relevant solutions, including the use of Virtual Power Plants, SDN technology coupled with network slicing. As a way to achieve power balancing between power generation and demands, this study offers a unique architecture for a smart grid that makes full use of optimization techniques to rationalize the distribution of energy resources. Performance evaluation shows the optimization of resource consumption.
In Machine Learning (ML) the analysis and the preparation of data before their use is considered an important task, in order to improve the performance of the ML algorithms. Techniques like clustering and dimensionality reduction are applied for the preparation of the data offering several advantages for the ML algorithms to which the data will be used. Some indicative advantages include the improved performance and the faster training of the ML algorithms, the handling of missing or corrupted data, the detection of data overfitting and the visualization of the data. In this paper, a dataset that contains plenty of ship engine's data is analyzed. Specifically, methodologies for density estimation, clustering and dimensionality reduction are studied. Subsequently, such methodologies are applied to the aforementioned dataset, providing useful results about the structure of the dataset, about the correlation of its data, as well as about the importance of each feature included in the dataset.
Autonomous driving systems are among the exceptional technological developments of recent times. Such systems gather live information about the vehicle and respond with skilled human drivers’ skills. The pervasiveness of computing technologies has also resulted in serious threats to the security and safety of autonomous driving systems. Adversarial attacks are among one the most serious threats to autonomous driving models (ADMs). The purpose of the paper is to determine the behavior of the driving models when confronted with a physical adversarial attack against end-to-end ADMs. We analyze some adversarial attacks and their defense mechanisms for certain autonomous driving models. Five adversarial attacks were applied to three ADMs, and subsequently analyzed the functionality and the effects of these attacks on those ADMs. Afterward, we propose four defense strategies against five adversarial attacks and identify the most resilient defense mechanism against all types of attacks. Support Vector Machine and neural regression were the two machine learning models that were utilized to categorize the challenges for the model’s training. The results show that we have achieved 95
Spectrum sensing is essential in opportunistic cognitive radio (CR) systems for detecting primary users’ (PUs’) activities and protecting the PUs against harmful interference. By extending the perfect spectrum sensing in the literature to more realistic situations, a new random sensing error framework of secondary users (SUs) is proposed to study the SUs’ behavior so as to make the best benefit for the CR system in this paper. A novel queueing-game theoretical model is formulated first and then various system stationary performance measures are procured. Furthermore, the SU’s equilibrium joining strategies are obtained, the throughput of SUs is derived, and the CR system’s social welfare’s monotonous in terms of the sensing error and the SU’s request frequency are characterized. Particularly, three interesting but counterintuitive results are observed as below: (i) the expected delay for joining SUs can be non-monotone in their effective arrival rate; (ii) multiple equilibrium joining strategies of SUs can always exist; and (iii) the spectrum sensing error does not necessarily worsen the CS system’s social welfare, i.e., some sensing error in the system may possibly lead to more efficient outcomes in terms of throughput and social welfare. The results and observations offered in this paper are expected to extend spectrum sensing research in a more efficient way to better provide CR system services to various users.
Federated learning (FL) is an emerging learning framework that enables decentralized devices to collaboratively train a model without leaking their data to each other. One common problem in FL is class imbalance, in which either the distribution or quantity of the training data varies in different devices. In the presence of class imbalance, the performance of the final model can be negatively affected. A straightforward approach to address class imbalance is up-sampling, by which data of minority classes in each device are augmented independently. However, this up-sampling approach does not allow devices to help each other and therefore its effectiveness can be greatly compromised. In this paper, we propose FED-CGU, a collaborative GAN-based up-sampling strategy in FL. In FED-CGU, devices can help each other during up-sampling via collaboratively training a GAN model which augments data for each device. In addition, some advanced designs of FED-CGU are proposed, including dynamically determining the number of augmented data in each device and selecting complementary devices that can better help each other. We test FED-CGU with benchmark datasets including Fashion-MNIST and CIFAR-10. Experimental results demonstrate that FED-CGU outperforms the state-of-the-art algorithms.
Data is critical for improving an individual’s quality of life. Its value provides opportunities for users to profit from data sales and purchases. Data marketplace users, on the other hand, must share and trade data in a secure and trusted environment while maintaining their privacy. The paper’s first major contribution is to identify enabling technologies and challenges to the development of decentralized data marketplaces. The second major contribution is the proposal of a blockchain-based decentralized data marketplace framework. The proposed framework allows sellers and buyers to transact with greater confidence. The system employs a novel approach to enforcing honesty in data exchange among anonymous individuals by requiring a security deposit. The system has a time frame before the transaction is considered complete.
In smart grids, two-way communication between end-users and the grid allows frequent data exchange, which on one hand enhances users’ experience, while on the other hand increase security and privacy risks. In this paper, we propose an efficient system to address security and privacy problems, in contrast to the data aggregation schemes with high cryptographic overheads. In the proposed system, users are grouped into local communities and trust-based blockchains are formed in each community to manage smart grid transactions, such as reporting aggregated meter reading, in a light-weight fashion. We show that the proposed system can meet the key security objectives with a detailed analysis. Also, experiments demonstrated that the proposed system is efficient and can provide satisfactory user experience, and the trust value design can easily distinguish benign users and bad actors.
Internet protocol (IP) lookup is a key technology that affects network performance. Numerous studies have inves- tigated IP lookup and provided solutions for improving lookup algorithms. Herein, we utilized various state-of-the-art algorithms for improving IP lookup performance and explore trie-based algorithms to understand how these algorithms affect memory access or usage and the resulting reductions in IP lookup times. Moreover, we utizsed parallel data processing for increasing IP lookup throughput. These algorithms are applicable to all tries. Nevertheless, we conducted experiments by using only binary tries for simplicity. IP lookup algorithms were tested through simulations using real IPv4 router tables with 855,997 or 876,489 active prefixes. Finally, a synthetic architecture combining all of the discussed algorithms was proposed and evaluated.
