Transactions on Emerging Telecommunications Technologies最新文献

The rapid evolution of the Internet of Things (IoT) has led to the growth of the Internet of Medical Things (IoMT), encompassing interconnected medical devices, wearable sensors, and healthcare systems. IoMT extends the capabilities of IoT into the healthcare sector, representing a promising technology for the future of healthcare. The underlying technology has transformed the healthcare landscape by continuously collecting patient data in real time and providing a remote monitoring and diagnostic system. However, IoMT introduces significant challenges in data security, privacy, and system reliability due to centralized data storage models, which can create a single point of failure and raise concerns about privacy and security. To address these challenges, this research proposes a blockchain-based framework to enhance data security and privacy by decentralizing data storage and managing device authentication using smart contracts. Given the sensitive nature of medical data and the potential repercussions of security breaches, each medical device has a unique digital identity represented by a blockchain-based smart contract, supporting the multi-device mapping required for managing multiple diseases in healthcare diagnosis and treatment. This approach enhances healthcare security and efficiency. A proof-of-work mechanism ensures secure transaction validation and experimental results demonstrate that the proposed framework significantly improves data integrity and security while optimizing system performance, as measured by gas consumption and latency. The assessment demonstrates the feasibility of employing blockchain technology to enhance the security and privacy of the IoMT healthcare system, providing a robust solution to existing security challenges and protecting patient data.

Utilizing blockchain in vehicular ad-hoc networks (VANETs) can proficiently resolve concerns pertaining to data security and privacy. The limited throughput of blockchain obstructs its extensive implementation in VANETs. Current studies on enhancing blockchain throughput frequently encounter the issue of action space proliferation, leading to inadequate scalability. This research presents a strategy for optimizing blockchain performance on VANETs using deep reinforcement learning (DRL). The suggested method enhances blockchain throughput by selecting block producers and consensus methods, and by modifying block size and intervals, while maintaining decentralization, low latency, and security in VANET-based blockchain systems. Furthermore, to augment network security, an anomaly detection mechanism is incorporated, utilizing machine learning techniques to identify and mitigate potential attacks aimed at VANETs. The proposed system enhances throughput and fortifies resilience against malicious operations by identifying anomalous patterns in network behavior. The method uses the BDQ framework to meticulously partition the action space, tackling the action space explosion issue that occurs with conventional DRL techniques in blockchain throughput optimization. Simulation results indicate that the suggested solution significantly improves the throughput and security of the VANET-based blockchain system.

Smart connected, and autonomous vehicles represent a revolutionary advancement in transportation by incorporating cutting-edge technologies like Internet of Things (IoT), Artificial Intelligence (AI), and 5G/6G wireless communication. These technologies enhance efficiency, reliability, and sustainability in modern vehicular networks. However, the rapid expansion of intelligent vehicles has also led to rising cyber risks, introducing new forms of attacks that threaten security, privacy, and safety. Even minor anomalies in vehicular units may cause severe consequences, including fatalities. To resolve these issues, this study proposes an effective Intrusion Detection System (IDS) using Siamese Gated Memory Networks. The model learns traffic behaviors and generates proximity scores, which are processed through dense feedforward layers for classifying multiple vehicular threats. To further optimize detection, a Modified Beetle Optimization (MBO) technique is integrated into the feedforward layers. The approach is trained and examined on benchmark datasets, comprising NSL-KDD 2019, UNSW-NB-15, and VeReMi, using key metrics like precision, specificity, accuracy, F1 score, and recall. Recommended experimental analysis demonstrates superior performance examined with conventional techniques, achieving 0.993 accuracy, 0.991 precision, 0.99 recall, and 0.992 F1 score. The findings validate the robustness of hybrid Siamese networks with ensemble meta-heuristic optimization in securing Intelligent Transportation Systems.

Due to the shortage of explainable, resource-efficient solutions and the lack of unified multi-attack detection abilities, existing vehicular ad hoc networks (VANET) security frameworks fail to meet the critical requirements of real-time vehicular environments. Most traditional models rely heavily on centralized processing, making them unsuitable for dynamic, latency-sensitive distributed VANET architectures. These limitations create a serious threat to the safety and reliability of vehicular communication systems. To address these challenges, this study proposes an explainable machine learning framework for real-time multi-attack threat detection (EXMAT) in edge-enabled VANET environments. The framework is designed specifically for edge-enabled VANET platforms. EXMAT combines the novel XGBoost classifier with custom-engineered behavioral features and post hoc explainability to provide accurate decisions directly at the vehicular edge. The novelty of the model lies in its combined feature space, which fuses behavioral dynamics, communication patterns, and lightweight Boolean anomaly flags. The simulation of the model is performed under the VeReMi dataset. To strengthen the dataset for precise analysis of the threat, we synthetically extended it with complex attack patterns. Experimental results show that the proposed model achieves an overall classification accuracy of 95.78% with an almost perfect F1-score for standard behavior samples of 99.98% and 94.36% for replay attacks. These results highlight EXMAT's ability to be applied in real-time vehicular networks, enhancing traffic safety and security against unknown cyberattacks.

Aiming at the problems of low data collection security, poor fusion efficiency, and weak dynamic guarantee capability of meteorological equipment in complex communication environments, this paper proposes a multisource meteorological data fusion algorithm system based on the 5th generation mobile communication technology (5G) base station network security framework. The system combines the security architecture of 5G networks, including SUPI/5G AKA authentication and AES-256 end-to-end encryption mechanism, to ensure secure access to sensor nodes, and achieves differentiated quality of service (QoS) guarantees through network slicing technology to meet the needs of multisource heterogeneity and real-time performance of meteorological data. An improved distributed Kalman filter algorithm is deployed at the edge computing node, and the noise covariance matrix is dynamically adjusted through sliding window historical residual analysis, and a smoothing factor is applied to improve the convergence speed. The Dempster–Shafer (D-S) evidence theory is further integrated to construct a data credibility assessment model, and the system's robustness is enhanced by combining the abnormal data dynamic isolation mechanism. The experiment builds a 5G meteorological sensor network environment based on the NS-3 simulation platform, and uses the GHCN historical climate data set for testing. The original meteorological data, such as temperature, humidity, wind speed, and their corresponding metadata, is input, and high-precision meteorological information is output after secure processing and efficient fusion for monitoring and prediction. The results show that the RMSE of temperature and wind speed of this method is 0.82°C and 0.35 m/s, respectively, at the scale of 100 nodes, which is 48.8% and 28.6% lower than that of the Centralized Kalman Filter, and 36.9% and 14.6% lower than that of the distributed Kalman filter with fixed noise covariance. The QoS hierarchical scheduling strategy based on 5G network slicing effectively ensures the transmission stability of key data, and the high-priority packet loss rate is 0.67%. The AES-256 encryption mechanism significantly enhances the anti-attack capability, albeit with a slight increase in transmission delay, and reduces the success rate of a man-in-the-middle attack from 35% to 2%. The results show that the fusion algorithm system proposed in this paper effectively solves the problems of difficult secure access to meteorological data in an open wireless environment, low fusion efficiency, and poor dynamic response, and provides a practical technical path for building a high-reliability, low-latency intelligent meteorological monitoring system.

In smart cities, AI-driven traffic flow prediction and anomaly detection play a crucial role in optimizing urban mobility and reducing congestion. Traditional traffic management systems (TMS) often struggle with real-time adaptability, leading to inefficiencies in traffic prediction and failure to detect anomalies accurately. To address these limitations, we propose the Traffic Flow Prediction using Multi-Agent Approach (TFP-MAA) framework, which leverages multiple intelligent agents to analyze real-time traffic data, predict congestion patterns, and detect anomalies with high precision. The framework integrates deep learning models with a decentralized multi-agent system to enhance prediction accuracy and response efficiency. The proposed method enables adaptive decision-making for traffic authorities by providing real-time congestion forecasts and identifying unusual traffic patterns, such as accidents or roadblocks. This enhances proactive traffic management and reduces delays. Experimental results demonstrate that TFP-MAA significantly outperforms existing models in terms of accuracy (94.7%), response time (96.2%), and anomaly detection efficiency (95.9%). The findings suggest that integrating multi-agent AI systems into smart city infrastructure can lead to improved traffic flow (97.5%), reduced congestion (28.6%), and enhanced road safety.

As a key technology in ocean exploration, underwater wireless sensor networks (UWSNs) are persistently constrained by challenges stemming from limited node energy, namely short network lifespan and low transmission efficiency. Existing routing protocols often fall into local optima due to their inability to effectively balance global exploration and local exploitation. To address these issues, this study proposes a hybrid optimization routing protocol, HFFO-ACRP (hybrid fruit fly optimization-ant colony routing protocol). Its core innovation lies in a synergistic division of labor: the fruit fly optimization algorithm (FOA) performs a global search to generate initial routes by integrating path length, node density, and residual energy. Subsequently, the ant colony optimization (ACO) algorithm conducts local optimization to refine these paths, considering data priority and node energy efficiency. Finally, a unique bidirectional feedback mechanism enables the two algorithms to mutually enhance their search breadth and precision, collectively improving the accuracy and efficiency of route selection. Experimental results demonstrate that, compared to representative protocols, the proposed protocol extends the network lifespan by 9.69%, 16.21%, and 18.68%, respectively, and significantly increases the residual network energy in the later stages of operation. This hybrid strategy provides an efficient and robust routing solution for resolving the critical energy consumption and longevity bottlenecks in UWSNs.

Vehicular ad-hoc networks (VANETs) are critical to intelligent transportation systems yet inherently vulnerable to security threats, including data falsification, message tampering, and denial-of-service attacks. Traditional centralized security mechanisms exhibit limitations in scalability, trustworthiness, and resilience against single points of failure. To address these challenges, this study proposes a blockchain-enabled decentralized framework integrating machine learning for anomaly detection in VANETs. The proposed model employs a multi-layer blockchain architecture to establish transparent and immutable trust among participating nodes, with smart contracts automating security policies, intrusion response, and vehicle reputation management. Machine learning algorithms are incorporated to analyze spatiotemporal traffic patterns, detect abnormal behaviors such as malicious message injection and position spoofing, and differentiate legitimate nodes from intruders. Furthermore, simulation results demonstrate that the proposed framework achieves dual optimization objectives: (1) consensus efficiency improvements reducing verification latency by ≥ 40% through Practical Byzantine Fault Tolerance enhancements; and (2) detection accuracy enhancements reaching 94.2% precision in identifying sophisticated attacks—while maintaining efficient scalability for large-scale VANET environments. This research highlights the synergy of blockchain and machine learning in building a secure, decentralized, and intelligent vehicular network infrastructure, contributing to the safe deployment of next-generation intelligent transportation systems.