Transactions on Emerging Telecommunications Technologies最新文献

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.

Guaranteeing the anonymity of the vehicle and the integrity of the transmitted message are two indispensable conditions in vehicular ad-hoc network. Anonymous signature can achieve the two function. However, existing anonymous signature schemes constructed based on traditional cryptosystems cannot withstand quantum attacks. In addition, in some cases, the schemes need to satisfy the non-transferability of signatures to solve the problem of signature misuse due to publicly verified signatures. In order to resist quantum attacks and address the problem of signature misuse, this article proposes a lattice-based chameleon signcryption scheme, which aims to protect vehicle identity and data security. The scheme is resistant to quantum attacks and satisfies signature non-transferability, signer rejectability and non-repudiation. Especially, we prove that the proposed scheme is secure in the Standard Model based on the error learning problem and the classical lattice small integer solution problem.

Wireless sensor networks have sensing functions which made up of small sensor nodes that are processing and communication capabilities. However, the traditional approach of designating a single sensor node as the cluster head often leads to a faster depletion of its energy resources than another node. So, periodic reassignment of the cluster head role among different sensor nodes is crucial to extend operational lifetime and ensure sustained performance. Therefore, this research proposes an Enhanced Agglomerative Hierarchy Algorithm based on the Low Energy Adaptive Clustering Hierarchy (EAHALEACH) protocol, coupled with an enhanced time-controlled optimization algorithm, to enhance energy efficiency by selecting the optimal cluster head from a candidate pool. The proposed method enhances energy efficiency by optimally selecting cluster heads, which reduces energy consumption and extends the lifespan of the network. Additionally, a lightweight energy-aware cluster head rotation algorithm is introduced to efficiently rotate the cluster head role within the sensor node network, minimizing unnecessary data transmissions and extending the network lifetime. A hybrid approach combining Carrier Sense and Time Division Multiple Access (CSTDMA) optimizes packet forwarding by dynamically allocating time slots, reducing collisions and contention to improve packet forwarding efficiency. Comparative analysis with existing techniques demonstrates that the hybrid clustering-based LEACH protocol achieves superior performance in terms of throughput 4600 bps by 2000 rounds, energy consumption 50 J, latency as 1 ms and network lifetime in 1000th node attains 3500 s. These advancements contribute to prolonged operational efficiency and sustained performance in wireless sensor network deployments.

The Internet of Things (IoT) is a transformative technology that has found applications in diverse domains, including automation, logistics, grid, transportation, healthcare, and more. In these domains, IoT systems generate a significant amount of data, which can be stored in a cloud. However, cloud computing may not be practical in certain delay-sensitive IoT applications with complex operations. To address this, fog and edge computing paradigms have been introduced, but they rely on a reliable internet connection for proper functioning. The dew computing paradigm, a novel concept, allows the execution of various applications in the IoT environment, with or without internet connectivity. However, ensuring data confidentiality and integrity during transmission and storage in such an environment remains a significant challenge. Therefore, a fail-safe and highly effective security mechanism is yet to be proposed. This study introduces a protocol that utilizes the elliptic curve cryptography and secure hash algorithm to design a secure key agreement and lightweight protocol (SKALP). SKALP security is formally analyzed using BAN (Burrows-Abadi-Needham) logic, ROM (Random Oracle Model), RoR (Real-Or-Random) model, and ProVerif (Protocol Verifier) simulation while informally discussing it to evaluate its resistance against well-known attacks. Additionally, the performance analysis of SKALP considers the costs associated with communication and computation. The findings from the comparative analysis indicate that the SKALP demonstrates a higher level of superiority than its competitors.

Modern networking demands efficient and reliable information delivery within Mobile Ad-hoc Network (MANET) and cloud environments. This paper introduces a novel approach that employs Multi-Agent Deep Learning (MADL) for adaptive resource allocation, addressing the challenges of optimizing traffic and ensuring dependable information delivery while adhering to Service Level Agreement (SLA) constraints. Our method dynamically allocates resources across nodes, leveraging the synergy between Advanced Cloud Computing and Edge Computing to balance centralized processing and localized adaptability. The integration of Graph Neural Networks (GNNs) further enhances this process by adapting resource allocation decisions based on network topology. Through iterative learning, our algorithm fine-tunes continuous-time resource optimization policies, resulting in substantial improvements in throughput and latency minimization. Simulations validate the effectiveness of our approach, demonstrating its potential to contribute to the advancement of MANET cloud networks by offering adaptability, efficiency, and real-time optimization for reliable information delivery and dynamic link utilization.

In the complex, critical, and rapidly evolving era of Industry 4.0 manufacturing, mature engineering disciplines can be developed to validate system designs at an abstract level, enabling effective fault-free environments. However, designing integrated IoT platforms presents significant challenges due to their inherent complexity and interoperability issues. To address this challenge, the proposed framework integrates axiomatic design principles with considerations for data integrity and domain ontology. Furthermore, this paper introduces a multi-level integrity rule repository for IoT application to provides a robust foundation for designing and maintaining IoT systems. To validate the effectiveness of this approach, we conducted a study across four domains of smart cities. The design's quality was assessed using model quality parameters, including completeness, consistency and validity. Our analysis shows that the semantic-aware axiomatic design framework can effectively address the problem of inconsistencies and low-quality design in scientific IoT environments, providing a more reliable and efficient solution.

The development of wireless communication systems is a challenging and constantly evolving field and the issue of gaining optimal performance is of utmost importance. This work intends to give a thorough and detailed description of massive MIMO technology and its properties, with a significant emphasis on digital beamforming (FDB) and hybrid beamforming (HBF) techniques and the potential of combining them with the most recent and exciting frontier of research: deep learning. On one hand, FDB provides accurate signal control but, on the other hand, it deals with substantial needs like high-power consumption. This challenge makes the focus shift to HBF—the innovative technology successfully coupled with deep learning's powerful potential. The chosen research explores extensively the major areas of application and compatibility of this operating mode in a diverse range of operational situations in the interference environment as well as in different levels of noise conditions. Moreover, the study offers a comprehensive comparison, which is highly effective in exploring further methods that focus on improving spectral efficiency. Significantly, the “Proposed Method” is suggested to be at the leading position, which demonstrates superior performance. Showing outstanding generalization capability, versatile robustness, and efficiency of usage in the proposed framework rely on EfficientNet-B7 as the major portion. This makes it adaptive to its dynamic surroundings and puts it as a powerful tool in the world of advanced connectivity and massive MIMO technology. Due to its core ability to respond to changes in conditions effectively and efficiently, the proposed framework is seen as one of the most powerful approaches that could be used to change wireless communication systems.