IET Networks最新文献

This paper presents an integrated optimisation framework for wireless sensor networks (WSNs) designed to manage the competing demands of energy efficiency, latency reduction, throughput improvement and communication reliability under dynamic and large-scale deployment conditions. The framework reorganises and enhances three core optimisation methods—genetic algorithm (GA), particle swarm optimisation (PSO) and an improved NSGA-II—by embedding adaptive behaviours and topology-aware decision logic. The GA is strengthened through a zone-oriented crossover mechanism and a sink-distribution-based initialisation strategy, which enhance coverage robustness and fault tolerance. The PSO module applies self-adjusting learning coefficients and QoS-aware routing constraints to maintain efficient path selection under varying load conditions. The improved NSGA-II incorporates an adaptive selection mechanism and a direction-guided crossover operator to better balance energy consumption and delay in multi-objective optimisation. Simulation results show that the proposed framework consistently outperforms federated DDQN and adaptive MOPSO across all performance indicators. It also demonstrates superior multi-objective convergence quality, achieving an IGD of 0.03 and an HV of 0.87. Overall, the framework enhances the scalability, resilience and operational efficiency of WSNs and provides practical guidance for adaptive scheduling in complex real-world environments.

Terahertz (THz) communication is a key enabler for 6G wireless networks but suffers from severe path loss and dynamic blockage, making conventional MAC protocols inefficient. This paper proposes a reconfigurable intelligent surface–based deep reinforcement learning MAC (RIS-DRL-MAC) framework that enables cross-layer optimisation between the physical and MAC layers. By embedding RIS perception features—such as equivalent channel gain and link stability—into the state space and jointly optimising beam direction, channel access and RIS phase configuration through a distributed twin delayed deep deterministic policy gradient (TD3) algorithm, the protocol achieves adaptive environment control. Simulation results show that, under dynamic blockage and high-load conditions, RIS-DRL-MAC improves network throughput by up to 90%, reduces access delay by 50% and maintains over 90% link availability compared with baseline schemes. The proposed method establishes a closed loop of sensing, decision and environment reconfiguration, providing an effective solution for reliable and energy-efficient THz mesh networking.

Recently, precision agriculture has used wireless sensor networks (WSNs) to gain valuable insights and improve crop yields, promoting efficient resource use and data-driven decisions. However, WSNs face challenges, such as high power consumption from continuous sensing, data processing and communication, especially in large-scale setups, which limits their lifespan. This paper focuses on reducing power use in agricultural WSN sensor nodes during data transmission of soil moisture, rainfall, light intensity, air temperature and humidity from the transmitting sensor node to the base station. Four algorithms are proposed to cut power consumption. First, a sleep/wake (S/W) scheme using a simple duty cycle called S/W-DC. Second, the S/W scheme combined with adaptive data sampling (ADS) based on redundant data (RD), called S/W-ADS-RD. Third, the S/W scheme integrated with dynamic voltage scaling (DVS), named S/W-DVS. Fourth, a hybrid of all three, called S/W-ADS-RD-DVS. The sensor uses a 12 V/5 W solar panel for energy harvesting to maintain operation. The hybrid algorithm achieved 99.232% power savings and extended battery life to approximately 1.83 years. During a 6-h session, data transmission was reduced by 99.93%. This research could significantly improve WSN efficiency in precision agriculture and can be applied to energy-efficient WSN deployment across various fields, supporting Internet of Things (IoT) applications.

This work considers the use of optical wireless communications (OWC) for transmitting data from medical devices in wireless body-area networks (WBANs) for the purpose of patient vital sign monitoring. In such networks, the design of efficient medium-access control (MAC) protocols is crucial to ensuring reliable and effective data transmission from multiple nodes. Here, IEEE 802.15.6 and IEEE 802.15.7 standards, developed for wireless personal area networks (WPANs), are compared and evaluated through numerical simulations to assess their suitability for the specific use-case under consideration. The former standard was initially developed for radio-frequency (RF) networks, whereas the latter is based on OWC technology. This work also provides insights into the performance of the recently-introduced IEEE 802.15.13 standard, designed for optical WPANs. Our study relies on the Castalia simulator, combined with realistic optical WBAN channel models developed in our team's previous works, with network energy efficiency and quality-of-service (QoS) serving as the primary evaluation criteria. Both cases of intra- and extra-WBAN connectivity are considered, where the former refers to data transmission from medical sensors to a coordinator node (CN), and the latter to transmission from CNs (each corresponding to a patient) to an access point (AP), in a hospital ward, for instance. Additionally, two scenarios are examined: battery-operated CNs and power-outlet-connected CNs, with the latter assumed to be positioned on the patient's beds in an intensive care unit (ICU) room. Our results show the advantage of the IEEE 802.15.7 MAC protocol in terms of both energy consumption and QoS, for all considered scenarios. Finally, because the number of patients may vary across hospital wards, the scalability of the aforementioned MAC protocols is also investigated by varying the number of patients up to 8. The results indicate that IEEE 802.15.13, which relies on time-division multiple access (TDMA), is a viable candidate for optical WBANs despite its limited scalability, which could be resolved using a more flexible allocation of time resources to ensure that all nodes are granted access to the transmission time slots. Overall, this study advances current knowledge and offers new insights into the design of robust optical WBANs that can ensure acceptable QoS under varying conditions while preserving energy efficiency, enabling their practical deployment in real-world healthcare scenarios.

RETRACTION: J.T. Wang and Y. Bu, “Internet of Things-Based Smart Insect Monitoring System Using a Deep Neural Network,” IET Networks. 11, no. 6 (2022): 245–256, https://doi.org/10.1049/ntw2.12046.

The above article, published online on 6th September 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, Christoph Sommer; the Institution of Engineering and Technology; and John Wiley & Sons Ltd.

The retraction has been agreed due to concerns raised by a third party regarding text and image overlap with different sources [1-3].

When the authors were queried regarding the above concerns, they could not address the issues adequately. Accordingly, we cannot vouch for the integrity or reliability of the content and have taken the decision to retract the article. The authors have been informed of the decision, and Jiang Tao Wang disagrees with the retraction. Yufei Bu did not respond.

The fifth-generation (5G) network slicing paradigm promises a customized service delivery through virtualized, isolated network slices. However, its full potential is hindered by inefficient and static resource allocation strategies that often fail to adapt to dynamic traffic and network conditions. This paper proposes a novel two-phase optimization framework to address this challenge. First, an Integer Linear Programming (ILP) model is developed to prioritize high-revenue slice admission, revoke underutilized slices, and reallocate resources for profitability. Simulations using real-world traffic data demonstrate that this proposed approach outperforms static and reactive approaches, achieving up to 24.8% higher resource utilisation and 98.99% higher profitability than the baseline method. The framework also adapts to dynamic traffic patterns and network conditions, balancing profit maximisation with reconfiguration costs. Second, to further improve performance, the paper introduces a deep reconfiguration agent (DRA), a Deep Reinforcement Learning (DRL) model that learns policies for slice admission, resource allocation and reconfiguration, and predicts network slice resource demands and consumption, enabling adaptive reconfiguration based on future demands and long-term profit. The results show that the DRA-based strategy increases the InP's profit by up to 5 times and boosts resource utilisation by 43.71% compared to the ILP model alone and also converges by 38.89% faster compared to using only the DRL model.

The fast evolution of cyberattacks in the Internet of Things (IoT) area, presents new security challenges concerning Zero Day (ZD) attacks, due to the growth of both numbers and the diversity of new cyberattacks. Furthermore, Intrusion Detection System (IDSs) relying on a dataset of historical or signature-based datasets often perform poorly in ZD detection. A new technique for detecting zero-day (ZD) attacks in IoT-based Conventional Spiking Neural Networks (CSNN), termed ZD-CSNN, is proposed. The model comprises three key levels: (1) Data Pre-processing, in this level a thorough cleaning process is applied to the CIC IoT Dataset 2023, which contains both malicious and the most recent attack patterns in network traffic, ensuring data quality for analysis, (2) CSNN-based Detection, where outlier identification is conducted by comparing two dataset groups (the normal set and the attack set) within the same time period to enhance anomaly detection and (3) In the evaluation level, the detection performance of the proposed model is assessed by comparing it with two benchmark models: ZD-Deep Learning (ZD-DL) and ZD- Convolutional Neural Network (ZD-CNN). The implementation results demonstrate that ZD- CSNN achieves superior accuracy in detecting zero-day attacks compared to both ZD-DL and ZD-CNN.

This paper investigates a vulnerability in IEEE 802.11 wireless local area networks, focusing on a MAC sublayer attack known as acknowledgement (Ack) spoofing. The paper delves into the distributed coordination function (DCF) and examines how Ack spoofing attacks affect network performance by manipulating the Ack operation essential for successful data exchange between stations. This manipulation disrupts Ack-based rate control mechanisms and the backoff procedure of the standard, leading to decreased performance for legitimate receivers with lossy links to access points. The paper introduces strategies to perform Ack spoofing attacks. To counter these threats, a novel detection and mitigation technique is proposed that effectively detects and mitigates any of the proposed attack strategies. The introduced technique is simple to implement, compatible with all versions of the existing IEEE 802.11 standard and all rate control mechanisms that rely on Ack frames in their operations. It also requires no modifications to the existing IEEE 802.11 standard, facilitating easy adoption by manufacturers. Moreover, it leverages a unique approach that avoids a cross-layer design, maintaining the integrity of layer abstraction. Through detailed simulations and analysis, the effectiveness of the proposed attack strategies and the detection and mitigation technique is demonstrated under various scenarios.

Coping with the unprecedented surge in traffic volume necessitates a profound overhaul of traditional networking architectures. In response, software-defined networking (SDN) has emerged as a groundbreaking architecture that separates the control plane from the data plane, relocating it to a more computationally capable central controller. This paradigm shift paves the way for integrating recent advancements in reinforcement learning (RL) for traffic engineering and routing. This paper presents a systematic guide to implementing this integration in Java-based, open-source, open-network operating system (ONOS) SDN controllers. The control plane implementation in ONOS and data plane implementation in Mininet constitute a holistic SDN framework for evaluating the performance of RL-based traffic engineering and routing schemes. Furthermore, we implement a direct-policy transfer algorithm to enhance the RL agent's reaction time to link failures in the network topology. Considering end-to-end delay, throughput, and packet-loss ratio as our performance evaluation metrics, we compare and contrast the performance of four existing schemes.

The deployment of unmanned aerial vehicles (UAVs) as aerial base stations in cellular networks presents a dynamic solution to meet the demands of high and fluctuating traffic patterns. Efficient placement of UAVs is crucial to harness their benefits and adapt intelligently to environmental changes. This paper introduces a multi-objective optimisation model aimed at maximising user coverage and minimising overlap among drone-based base stations in 6G networks. To address this optimisation issue, the Nondominated Sorting Genetic Algorithm II (NSGA-II) is deployed, enabling the identification of Pareto optimal solutions that strike a balance between conflicting objectives. Through simulations conducted under various scenarios, the proposed model demonstrated significant improvements in user coverage and reduction of overlap among base stations compared to existing techniques. The findings reveal the effectiveness of the proposed model in balancing the objectives of coverage and overlap, resulting in an enhanced 6G network design. The method achieves an average coverage probability of 98.39% and an average overlap improvement percentage (OIP) of 92.39%, validated through 50 experimental runs. These results underscore the robustness and superiority of the proposed NSGA-II-based strategy in optimising DBS placement, contributing to the advancement of 6G cellular networks.