Transactions on Emerging Telecommunications Technologies最新文献

RETRACTION: Z. Chang, W. Yang, J. Guo, and Y. Cheng, “ Dynamic Flow Scheduling Optimization Based on Intelligent Control for Digital Twins,” Trans Emerg Telecommun Technol (Early View): doi:10.1002/ett.4516.

The above article, published online on 3 May 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, Changqiao Xu; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that the article was accepted solely on the basis of compromised editorial handling and peer review processes, and was found to be out of the scope of the special issue. Therefore, the decision to retract this article was taken.

RETRACTION: M. Kamruzzaman, M. A. Hossin, I. Alrashdi, S. Alanazi, M. Alruwaili, N. Alshammari, S. Elaiwat, and R. Zaman, “ Wireless Communication Channel Modeling Using Machine Learning,” Trans Emerg Telecommun Technol (Early View): doi:10.1002/ett.4661.

The above article, published online on 5 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, Changqiao Xu; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that the article was accepted solely on the basis of compromised editorial handling and peer review processes. Furthermore, parts of the experimental methods in this manuscript lack sufficient detail to interpret and reproduce the findings. Therefore, the decision to retract this article was taken.

RETRACTION: M. Jarrah, A. Abu-Khadrah, H. Alrababah, A. S. B. M. Jaya, and Z. N. M. Alqattan, “ Affirmative Data Analytics Based Data Processing Method for 6G Wireless Network Applications,” Trans Emerg Telecommun Technol (Early View): doi:10.1002/ett.4583.

The above article, published online on 21 June 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, Changqiao Xu; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that the article was accepted solely on the basis of compromised editorial handling and peer review processes. Furthermore, several articles, this one included, submitted as part of the same special issue exhibited an unusually high degree of similarity in both concept and style. Therefore, the decision to retract this article was taken. The authors have been informed of the retraction and Dr. Jarrah, on behalf of the authors, disagrees with this decision.

RETRACTION: M. Jammula, V. M. Vakamulla, and S. K. Kondoju, “ Artificial Intelligence Framework-based Ultra-lightweight Communication Protocol for Prediction of Attacks in Internet of Things Environment,” Trans Emerg Telecommun Technol 34, no. 1 (2023): e4680, doi:10.1002/ett.4680.

The above article, published online on 15 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, Changqiao Xu; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that the article was accepted solely on the basis of compromised editorial handling and peer review processes. Furthermore, Figure 3 was found to have been sourced from a previous publication without clear attribution, and has been presented as a novel protocol. Therefore, the decision to retract this article was taken.

RETRACTION: M. Musa Jaber, S. Yussof, M. H. Ali, S. Khalil Abd, M. M. Jassim, and A. Alkhayyat, “ Internet of Things Security Framework Based on Near Field Communication With Otway Rees Protocol,” Trans Emerg Telecommun Technol (Early View): doi:10.1002/ett.4684.

The above article, published online on 13 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, Changqiao Xu; and John Wiley & Sons Ltd. The article was submitted as part of a guest-edited special issue. Following publication, it has come to the attention of the journal that the article was accepted solely on the basis of compromised editorial handling and peer review processes. Furthermore, the authors included irrelevant references in this article, leaving several scientific claims inadequately supported by the literature. Finally, parts of the experimental methods in this manuscript lack sufficient detail to interpret and reproduce the findings. Therefore, the decision to retract this article was taken. The authors have been informed of the retraction and Dr. Musa Jaber, on behalf of the authors, disagrees with this decision.

The growth of fog computing drives Internet of Things users to outsource data for services like storage and sharing. To ensure secure and efficient data search, extensive research has been conducted on searchable encryption, specifically attribute-based keyword search (ABKS). ABKS facilitates precise access control over encrypted data while maintaining search functionality. However, delegating complex search operations to fog nodes introduces challenges like latency limitations, compromised search results, and unauthorized occupation of server resources. In this manuscript, we propose a Time-Efficient Data Retrieval (TEDR) scheme to address these challenges. Our scheme incorporates two access policies, allowing data owners (DOs) and data users (DUs) to control data access and search queries. TEDR enables fog nodes to verify search queries before decryption, avoiding unnecessary decryption for invalid queries. We introduce a decentralized query submitting framework to allocate search queries among fog nodes, addressing redundancy concerns and latency issues. We analyze the decentralized query submitting game and classify it as a potential game, making it suitable for decentralized decision-making. We evaluate TEDR performance on real-world datasets, demonstrating practicality and efficiency. Our scheme reduces computational and communication costs by approximately 30% compared to existing methods.

Future networks are expected to use Artificial Intelligence and machine learning applications intensely. The primary focus of machine learning techniques will be on intelligent automation and decision-making based on information learned from network functions, resources, and users. The research community has studied several schemes for using machine learning in communications and networks in the last two decades. However, the schemes mainly focus on learning the state information of individual network functions and resources and performing their optimization in a standalone fashion. Recently, a study group from the International Telecommunication Union has outlined machine learning pipeline requirements. It addresses several aspects of machine learning operations, such as model selection, data sources, and action points. However, the mechanisms to define the state of network functions and resources for different network layers and domains are yet to be explored. Therefore, in addition to the above, specific studies are required for cross-layer and administrative domain knowledge sharing and holistic optimization. Without considering the holistic view, applying machine learning to optimizing individual functions and resources may result in nonoptimal and unexpected behaviors instead of having any positive effect. The need for global and deep holistic learning has recently been identified in the literature; however, no implementation of the above scheme has been studied or evaluated for networks. This article proposes a novel approach, implementing global and deep holistic learning in wireless networks toward fulfilling the requirements of future intelligent networks. It also proposes an objective function-based feature engineering for wireless networks. Experimental results of the proposed scheme show significant improvements in the network performance and accuracy of the machine learning models. Moreover, applications of transfer learning to base learners for network functions and resources can significantly expedite decision-making with reduced computational costs.

In this article, a joint relay selection, power control and time allocation problem is studied to maximize the energy efficiency for the cooperative underwater acoustic communication networks. The joint optimization problem is full of challenges due to the strong coupling of multiple resources and the uncertain characteristics of the underwater acoustic communication scenario. To address this issue, the worst-case method is employed to transform an original uncertain problem into a deterministic problem. Furthermore, we propose the block coordinate descent-based method to decouple the strongly coupling multi-resource allocation problem into three relatively independent sub-problems. The coupling of multiple resources is completely decoupled, thereby greatly reducing the solving difficulty. In addition, given that the sub-problems with the fractional objective function are still non-convex and hard to solve, the Dinkelbach-based method is proposed to transform the fractional objective function into a subtractive form. At last, the relay selection sub-problem is transformed into a integer programming problem, and the time allocation sub-problem is transformed into a linear programming problem, whose optimal solutions can be obtained by some well-established solution methods. The power allocation problem is transformed into a convex optimization problem, which can be solved by the Lagrangian dual method. Finally, in the proposed iteration structure, the three sub-problems are alternatingly solved until convergence. Simulation results are presented to demonstrate the efficiency and robustness of the proposed algorithm.

In recent years, the advancements in wireless technologies and sensor networks have promoted the Mobile Internet of Things (MIoT) paradigm. However, the unique characteristics of MIoT networks expose them to significant security vulnerabilities and threats, necessitating robust cybersecurity measures, including effective attack detection and mitigation techniques. Among these strategies, Artificial Intelligence (AI), and particularly Machine Learning- (ML) based approaches, emerge as a pivotal method for bolstering MIoT security. In this paper, we present a comprehensive literature survey regarding the utilization of ML for enhancing security in MIoT. Through an exhaustive review of existing research articles, we analyze the diverse array of ML-based approaches employed to safeguard MIoT ecosystems and provide a holistic understanding of the current landscape, elucidating the strengths and limitations of prevailing methodologies. We propose a structured taxonomy to categorize recent works in this domain, by distinguishing approaches based on Shallow Supervised Learning (SSL), Shallow Unsupervised Learning (SUL), Deep Learning (DL), and Reinforcement Learning (RL). By delineating existing challenges and potential future directions for cybersecurity in MIoT, we aim to stimulate discourse and inspire novel approaches towards more resilient and secure MIoT ecosystems.