Transactions on Emerging Telecommunications Technologies最新文献

In a Distributed Denial of Service (DDoS) attack, the attacker aims to render a network resource unavailable to its intended users. A novel Software Defined Networking (SDN)-centered secured DDoS attack detection system is presented in this paper by utilizing TanhSoftmax-Restricted Boltzmann Dense Machines (TS-RBDM) with a Mean Difference of Public key and Private key based Streebog (MDPP-Streebog) user authentication algorithm. Primarily, in the registration phase, the users have registered their device details. The two-stage login process is performed after successful registration. Then, in the network layer, the nodes are initialized, and via the Gate/Router, the sensed data is transmitted to the SDN controller to enhance network energy efficiency. Later, by using the CIC DDoS 2019 dataset, the DDoS detection system is trained. This dataset undergoes preprocessing, and features are extracted from it. By employing the Adaptive Synthetic (ADASYN) technique, data balancing is achieved. Lastly, by using the TS-RBDM technique, the data is trained. The sensed data is categorized as either attacked or non-attacked data within this trained DDoS detection system. By employing the Entropy Binomial probability-based Shanon-Fano-Elias (EB-SFE) technique, the non-attacked data will be encoded and transmitted to the receiving terminal. Lastly, the experiential assessment illustrated that the proposed DDoS detection system attained 98% accuracy with 37 485 ms minimal training time, thus outperforming all state-of-the-art methods.

The lifetime of a wireless sensor network (WSN) can be impacted by the energy consumption of the routing protocol, because small sensor nodes are typically hard to recharge after deployment. Generally, data aggregation is employed to decrease the data redundancy and save energy at each node in a WSN. Traditional routing protocols frequently fall short of handling the complexities of data aggregation while getting energy efficient. In this paper, Optimized Multi-Stream General Adversarial Network espoused Data-Aggregation-Aware Energy-Efficient Routing Protocol for WSN (MSGAN-RPOA-DAA-EERP) is proposed. Here, Multi-Stream General Adversarial Network (MSGAN) is used for routing protocol. Then the Red Panda Optimization algorithm (RPOA) is proposed to optimize the MSGAN to increase the network lifetime of WSN. The proposed model is used to maximize the parameters such as data aggregation, communication energy and node residual energy. The proposed MSGAN-RPOA-DAA-EERP method attains 20.28%, 27.91% and 17.53% lower energy consumption when compared to the existing methods, like Energy-efficient cross-layer-basis opportunistic routing protocol and partially informed sparse autoencoder for data transfer in WSN (EECOP-PIAS-WSN), Improved buffalo optimized deep feed forward neural learning dependent multiple path routing for energy efficient data accumulation (IBO-DFFNL-EEDA), Effective communication in WSN utilizing optimized energy efficient engroove leach clustering protocol (EC-WSN-EEELCP) respectively.

The study of light is crucial in modern interiors, as it dispels darkness and enhances both functionality and aesthetics. From the primal flicker of flames in ancient caves to the advanced light emitting diode (LED) systems of today, lighting has been pivotal in shaping our surroundings and experiences. In contemporary interior architectural designs, lighting fulfills not only functional roles but also greatly influences the aesthetics and atmosphere of a space. Light has been an integral element in sleek interior architecture. With the advancements in LED technology, the approach to illuminating spaces has been transformed providing energy-efficient solutions. This paper explores the role of LED lighting in modern interior architectural designs focusing on its impact on illumination and communication. Using a weighted sum model and cluster analysis, the various LED lighting configurations are evaluated for their suitability in different spaces. Performance metrics such as illuminance levels, uniformity, adherence to standards, signal-to-noise ratio (SNR) and bit error rate (BER) are analyzed. By varying the configurations of LED array per position from 10 × 10 to 100 × 100 the performance across different scales is assessed. The results offer insights for selecting LED lighting options that optimize functionality, aesthetics and energy efficiency in modern interior spaces.

Energy management inside a blockchain framework developed for smart grids is primarily concerned with improving intrusion detection to protect data privacy. The emphasis is on real-time detection of cyberattacks and preemptive forecasting of possible risks, especially in the realm of electricity theft within smart grid systems. Existing Electricity Theft Detection techniques for smart grids have obstacles such as class imbalance, which leads to poor generalization, increased complexity due to large EC data aspects, and a high false positive rate in supervised models, resulting in incorrect classification of regular customers as abnormal. To provide security in the smart grid, a novel BLS Privacy Blockchain with Siamese Bi-LSTM is proposed. Initially, the privacy-preserving Boneh-Lynn-Shacham blockchain technique is built on BLS Short signature and hash algorithms, which mitigate misclassification rates and false positives in the detection of smart grid attacks. Then, a hybrid framework employs an intrusion detection algorithm based on Siamese Bidirectional Long Short-Term Memory to semantically distinguish between harmful and authentic behaviors, thereby improving data quality and predictive capabilities. Furthermore, a Recurrent Neural Network-Generative Adversarial Network is presented for detecting electricity fraud, which addresses the issue of class imbalance. This uses both supervised and unsupervised loss functions to produce synthetic theft samples that closely resemble actual theft incidents. From the experiment, it is showing that the proposed models perform with high accuracy and low error rates. The proposed model from the outcomes when compared to other existing models achieves high accuracy, detection rate, recall, and low computation time.

Wireless Sensor Networks (WSNs) are extensively used in event monitoring and tracking, particularly in scenarios that require minimal human intervention. However, a key challenge in WSNs is the short lifespan of sensor nodes (SN), as continuous sensing leads to rapid battery depletion. In high-traffic areas, sensors located near the sink node exhaust their energy quickly, creating an energy-hole issue. As a result, optimizing energy usage is a significant challenge for WSN-assisted applications. To address this, this paper proposes an Energy-aware Routing and Cluster Head Selection in Wireless Sensor Network through an Attentive Dual Residual Generative Adversarial Network for Golden Search Optimization Algorithm in Wireless Sensor Network (EAR-WSN-ADRGAN-GSOA). This method involves selecting the Cluster Head (CH) using Attentive Dual Residual Generative Adversarial Network (ADRGAN), minimizing energy consumption, and reducing a number of dead sensor nodes. Subsequently, Golden Search Optimization Algorithm (GSOA) is employed to determine an optimal path for data transmission to the sink node, maximizing energy efficiency, and elongating sensor node lifespan. The proposed EAR-WSN-ADRGAN-GSOA method is simulated in MATLAB. The performance metrics, such as network lifetime, number of alive nodes, number of dead nodes, throughput, energy consumption, and packet delivery ratio is examined. The proposed EAR-WSN-ADRGAN-GSOA demonstrates improved performance, achieving a higher average throughput of 0.93 Mbps, and lower average energy consumption of 0.39 mJ compared with the existing methods. These improvements have significant real-world implications for enhancing the efficiency and longevity of WSNs in applications, such as environmental monitoring, smart cities, and industrial automation.

The Internet of Things (IoT) is transforming numerous sectors but also presents unique security challenges due to its interconnected and resource-constrained devices. This study introduces the Bidirectional Gaussian Hummingbird Optimized End-to-End Blockchain (BGHO-E2EB) model, designed to detect and classify cyberattacks within IoT environments. Unlike preventive approaches, the developed model focuses on real-time detection and categorization of attacks, enabling timely responses to emerging threats. The proposed model integrates blockchain technology through Ethereum-based smart contracts to enhance the security and integrity of data exchanges within IoT networks. Additionally, a Gaussian Artificial Hummingbird Algorithm is employed for optimal feature selection, minimizing data dimensionality and computational load. A Bidirectional Long Short-Term Memory (Bi-LSTM) network further improves the model's capability by accurately detecting and categorizing cyber threats based on selected features. The Adam optimizer is used for efficient parameter tuning within the Bi-LSTM network, ensuring high-performance cyberattack detection. The proposed model was evaluated using established IoT security benchmarks, including the UNSW-NB15, BOT-IoT, and NSL-KDD datasets, accomplishing an accuracy of 98.7%, precision of 96.3%, and security level of 99.5%, significantly outperforming traditional methods. These results demonstrate the effectiveness of BGHO-E2EB as a robust tool for detecting and classifying cyberattacks in IoT networks, making it suitable for real-world deployment in dynamic IoT environments where security is paramount.

The integration of blockchain technology with healthcare digitalization has the potential to improve data management, reduce administrative costs, increase data security and privacy, and enhance querying capabilities. However, in the traditional blockchain model, all data and transactions are processed and stored in a single, unified ledger, and all participants have access to the same data, which raises privacy concerns and poses congestion issues with an increased number of transactions. It becomes even more problematic in healthcare, where data confidentiality is essential. In literature, centralized storage utilizing cloud-based solutions is employed to manage large volumes of data, restricting information sharing beyond the institution. Additionally, the direct storage of massive data on the blockchain impacts the performance and scalability of the system. In this paper, to address these issues and ensure the security and rapid retrieval of healthcare information, a framework is proposed, which involves the implementation of a dual-channel blockchain architecture combined with two robust cryptographic algorithms, i.e., Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES). These encryption techniques deliver safe data transmission via RSA and efficient data storage via AES, offering a secure mechanism to prevent unauthorized access and data breaches. In addition, private data collection is incorporated to securely store confidential patient information, guaranteeing privacy, security, and limited access. Also, an Access Control List (ACL) is defined for different users to implement access permissions, i.e., grant and revoke access to viewers while sharing information. Moreover, an off-chain storage InterPlanetary File System (IPFS) is used to improve scalability. The performance evaluation is performed by conducting experimental simulations, where critical performance indicators such as throughput and latency are measured across different transaction rates, channels, and rate controllers. Moreover, the proposed framework classifies smart contract functions into query and invoke/write transactions, enhancing the efficiency of data retrieval. Further, the functionality and security analysis of the proposed framework is discussed. The results demonstrate that the proposed approach is highly capable of preserving security and privacy standards while also assuring efficient management and accessibility of data in healthcare applications.

Passive optical network (PON) is one of the key enabling technologies to fulfill the latest exceptional bandwidth demand owing to an exponential rise of Internet data traffic induced by the expansion of bandwidth-hungry applications services. The potential of using passive star topology for ITU-T G.989.x standardardized next generation PON incorporating vertical cavity surface emitting laser input source, is proposed in this paper. Mode division multiplexing (MDM) technique employing Hermite-Gaussian (HG₁₀ and HG₂₀) modes are used to enhance the channel capacity at bidirectional 80 Gbps traffic rate. By utilizing polarization mode dispersion (PMD) emulator, this proposed system is promising since it offers good tolerance ability of anti-PMD, anti-dispersion, suppression of nonlinear effects and high spectrum effectiveness at high-speed transmission. Impressively, faithful 600 and 590 m range is obtained at HG₁₀ and HG₂₀ modes respectively, with clear eye patterns at bit error rate of 10⁻⁹ for 1596–1598.4 nm in downlink. In uplink, faithful 600 and 530–600 m distance is achieved for 1524–1526.4 nm at HG₁₀ and HG₂₀ respectively. For variable laser temperature, maximum tolerable temperature of 20°C for both modes in downlink and 20°C at HG₁₀ as well as 14C at HG₂₀ is observed in uplink, over 600 m range. Besides, the proposed design undergoes ∼1e-30 at HG10 and ∼1e-35 at HG20 coupling coefficients for linearly polarized (LP[0,1] to LP[−4,3]) modes. It is also analyzed that maximum 3.99 dB gain and 84.58 dB optical signal-to-noise ratio is obtained for the proposed scheme. This design does not suffer from shot, thermal and phase noise and offers optimum performance than existing systems.

Vehicular edge networks represent a novel architecture that utilizes vehicles as mobile edge nodes, characterized by high-speed dynamic changes. To effectively transmit data in vehicular edge networks, opportunistic routing methods can be employed, selecting suitable relay nodes based on encounter opportunities between nodes. Although existing opportunistic routing algorithms primarily select the optimal transmission path based on the encounter characteristics between nodes, the dynamism of network topology, the uncertainty of node mobility, and the heterogeneity between nodes still pose significant challenges to the implementation of opportunistic routing. In response to this, we propose a probabilistic routing algorithm based on Convolutional Neural Networks (CNN) and Q-learning, named PRCQ. This algorithm predicts node state transition probabilities using decomposed latent node features and dynamically adjusts optimal routing strategies using Q-learning. Extensive simulations were conducted on the NS-2.35 simulator based on two different city scenarios to evaluate the performance of the PRCQ algorithm compared to other existing algorithms. The results indicate that, compared with other existing opportunistic routing algorithms, PRCQ exhibits superior performance in terms of average transmission delay and packet delivery ratio.