Annals of Telecommunications最新文献

In today’s interconnected world, the line that separates the network perimeter can no longer be identified. This has led to the development of Zero Trust Networking (ZTN) and Software Defined Perimeter (SDP) concepts, which aim to extend the perimeter of trust to every entity connected to the network regardless of their physical location. However, implementing complex security mechanisms and constant trust assurance for every interaction can be challenging. One solution is integrating blockchain technology into Zero Trust to provide security. Blockchain offers features such as data decentralization, anonymity, cryptography, and immutable record of transactions that can be utilized. This work proposes a mechanism for secure service session management using blockchain capabilities. Non-fungible tokens (NFT) are applied to access and provider tokens representing a policy agreement for service consumption. These tokens are mapped to the public addresses of entities registered in the blockchain. The proposal is realized through an open-source Zero Trust platform and a private Ethereum blockchain.

The Internet of Things (IoT) is bringing new ways to collect and analyze data to develop applications answering or anticipating users’ needs. These data may be privacy-sensitive, requiring efficient privacy-preserving mechanisms. The IoT is a distributed system of unprecedented scale, creating challenges for performance and security. Classic blockchains could be a solution by providing decentralization and strong security guarantees. However, they are not efficient and scalable enough for large scale IoT systems, and available tools designed for preserving privacy in blockchains, e.g. coin mixing, have a limited effect due to high transaction costs and insufficient transaction rates. This article provides a framework based on several technologies to address the requirements of privacy, security and performance of the Internet of Things. The basis of the framework is the IOTA technology, a derivative of blockchains relying on a directed acyclic graph to create transactions instead of a linear chain. IOTA improves distributed ledger performance by increasing transaction throughput as more users join the network, making the network scalable. As IOTA is not designed for privacy protection, we complement it with privacy-preserving mechanisms: merge avoidance and decentralized mixing. Finally, privacy is reinforced by introducing usage control mechanisms for users to monitor the use and dissemination of their data. A Proof of Concept is proposed to demonstrate the feasibility of the proposed framework. Performance tests are conducted on this Proof of Concept, showing the framework can work on resource-constrained devices and within a reasonable time. The originality of this contribution is also to integrate an IOTA node within the usage control system, to support privacy as close as possible to the objects that need it.

The Internet of Things (IoT) is an emerging technology expected to play a significant role. The integration of IoT with cloud computing (CC) has enabled the creation of large-scale networks of interconnected smart devices and services. To provide optimal quality of service (QoS) for users, it is necessary to address the conflicting requirements of IoT services. The service selection problem is considered NP-hard and therefore requires using metaheuristic algorithms for efficient resolution. This paper presents a novel hybrid multi-objective metaheuristic, pRTMNSGA-III, which combines the strengths of RNSGA-III and TMNSGA-III to generate solutions that meet user preferences and eliminate unfavorable ones. To further optimize computational time, a parallel solution evaluation approach is employed. In addition, a more effective fuzzy membership function is proposed to select the best solution based on the requester’s preferred QoS. The proposed algorithm is evaluated on multiple datasets, and the results demonstrate its superiority over existing state-of-the-art algorithms, including RNSGA-III, TMNSGA-III, NSGA-III, pNSGA-II, NSGA-II, and NSPSO Experimental results demonstrate that pRTMNSGA-III outperforms existing algorithms by up to 37% in terms of the number of non-dominated solutions generated.

In this article, we implemented a novel blockchain-oriented RAN (BRAN) by allowing multiple players to trade RAN utilities (such as infrastructure) in terms of virtual network functions (VNFs) that are autonomous and dynamic. Particularly, we designed a multi-player network sharing (MPNS) smart contract which enables the automation of RAN-sharing processes using static or free-marketplace-oriented paradigms. The simulation results, acquired through auction-based and free-marketplace RAN-sharing methods, were evaluated based on user equipment (UE) capacity, UE satisfaction rate, base station (BS) efficiency, and end-to-end latency parameters. These results were then compared to those of the existing static RAN. It is observed from the simulation results that the performance of the blockchain-oriented free-marketplace RAN-sharing mechanism is 35% more efficient in terms of UE capacity and 40.5% more efficient in terms of UE satisfaction rate when compared to existing static RAN with the increase as the number of players in RAN environments. The proposed BRAN paradigm has spacious applications in 6G wireless networks, where user’s security, capacity, satisfaction rate, and latency are crucial constraints.

The traditional methods used to optimize energy efficiency in wireless communication systems employ medium access control (MAC) protocols that operate under complex functions that require a high computational cost to optimize a limited number of parameters. Furthermore, the inability of these methods to work under different protocols that involve learning and adapting the device specifications associated with existing problems, such as security, error tolerance, and human interference, makes their implementation in real systems impractical; this limitation mainly applies to networks with high node scalability. This paper presents a novel approach to this problem using machine learning to attain energy savings. The method proposes combining operating information from multiple power consumption control algorithms, CSMA/CA or slotted ALOHA (a variant of ALOHA), benchmark MAC protocols used in WiFi, and LoRaWAN technologies, creating a database of optimized solutions, which serves as a training base for a neural network capable of learning the behavior of all protocols simultaneously and creating a unified self-adaptive energy optimization model that considers multiple physical (PHY) and MAC layer variables for different devices and protocols. The proposed approach simultaneously presents solutions that optimize the energy reduction algorithms for different protocols, approaching or improving the performance of the techniques, saving 97.6% in CPU computation and 113,322,733% of the processing time in the search for the same solutions. The main contribution of this work is the proposal of an adaptable multi-protocol approach based on machine learning, which manages resources in slotted ALOHA and CSMA/CA benchmark protocols for wireless networks. Furthermore, it facilitates multi-objective optimization via machine learning for energy efficiency in real networks. It creates a new intelligent system that promotes efficient communication for multiple MAC protocols and considers the device’s processing capacity limitation. This work also shows that a neural network can approximate and optimize exact functions when the optimal parameters cannot be mapped mathematically.

In this paper, the proposed algorithm based on a novel technique for lowering the Near-Far problem in very high-speed digital subscriber line (VDSL) networks is presented. Through graphical results, it is demonstrated that the proposed technique performs better in terms of data rate for multi-user VDSL networks than the conventional IWF algorithm and comes close to the extremely complex OSB algorithm. The suggested algorithm’s complexity is comparable to that of the standard iterative water filling (IWF) algorithm and significantly less than that of the centralized optimal spectrum balancing (OSB) algorithm.

In this study, the downlink non-orthogonal multiple access (NOMA) system in which successive interference cancellation (SIC) errors exist is discussed. The new closed-form expressions for the bit error rate (BER) performance analysis over cascaded Nakagami-m fading channels are analytically derived for near and far users. In addition, new mathematical equations are obtained for the asymptotic BER as well as the diversity order to analyze the system performance. The theoretical results precisely match the simulations in all cases, validating the effectiveness of the theoretical analysis. The results demonstrate that the system performance decreases as the degree of N cascading increases. As for a fixed degree of cascading, BER performance increases as the m parameter increases. Moreover, for the power allocation coefficient (alpha ) influences the SIC operation performed on the near user, it also substantially affects the BER performance. Monte Carlo simulations using (10^{6}) iterations are presented to validate the derived analytical results.

Wireless technologies are used in almost every application domain. Applications have different requirements in terms of quality of service and network performance. When designing a wireless network, it is important to know the expected performance of the system. Including the application needs in the design of the network would help achieve the required performance. This paper focuses on the high throughput required for a Smart Farming use case. Multiple-Input Multiple-Output (MIMO) technology had greatly boosted the performance of wireless networks by introducing beamforming, which provides many benefits allowing wider coverage and better data rates. We propose a capacity-aware coverage study for Wi-Fi networks deployment in rural areas. We make our coverage estimations based on link budget calculations. We compare different deployment strategies and discuss the added value of beamforming. Our results are based on an analytical link budget estimation and a simulation study using the NS-3 simulator. We added all the needed functionalities on top of the existing Wi-Fi Module in NS-3. Results in terms of capacity, coverage, and number of access points deployed are discussed. We also developed an empirical analytical model that is based on the simulation results, which helps in estimating performance results for any deployment field size.