IET Wireless Sensor Systems最新文献

Cardiovascular diseases (CVDs) represent a significant global health issue, necessitating precise assessment methods. An important factor is vascular ageing, marked by a progressive decline in arterial elasticity, which impairs the ability of arteries to regulate blood flow effectively. Evaluating vascular age by comparing blood vessel health to chronological age offers valuable insights into arterial stiffness, aiding in the prevention of CVDs. This study employs four distinct neural network models to predict an individual's vascular age using photoplethysmography (PPG), a non-invasive, cost-effective, and reliable technique. PPG pulse waves from 4374 healthy adults, aged 25–75, grouped into six 10-year intervals from both radial and digital arteries, are used to explore age-related variations. The neural network models assessed include multilayer perceptron (MLP) and 1D convolutional neural network (CNN 1D) with raw signals, as well as 2D CNN and the pre-trained VGG-16 model with spectrograms as input. Results reveal that MLP achieved 95.3% accuracy for radial and 92.7% for digital arteries, CNN 1D achieved 99.3% for radial and 99.4% for digital arteries, and the 2D CNN model achieved 99.6% accuracy for both arteries. Notably, VGG-16 outperformed all models with an accuracy of 99.9% for radial and 99.8% for digital arteries. However, it is essential to consider that VGG-16's extended training time per epoch may pose limitations when dealing with large datasets and time constraints. In such scenarios, the more efficient 2D CNN, with appropriate hyperparameter tuning, may provide advantages in vascular age prediction. This predictive capability enhances the identification of cardiovascular ageing deviations and underlying disorders, improving assessment methods and proactive cardiovascular health management. By comparing blood vessel health to chronological age, this approach potentially enhances clinical practice, supports early intervention, and facilitates personalised treatment plans.

In this paper, the problem of attack mitigation in an intelligent transportation network or vehicular network is considered as a game. The player’s perception of uncertainty and decision making is studied under a subjective prospect theoretic (PT) model and an objective expected utility theory (EUT) model. A game where each player chooses one of two strategies with certain probabilities is analysed. The case where subjective players bias their choices of the probabilities using the Prelec weighting function $��w��(�.�)��$ is considered and compared with the EUT based decisions and the effect of the framing effect function $��\nu ��(�.�)��$ and $��w��(�.�)��$. The corresponding Nash equilibria (NE) are derived and found through the replicator dynamic equation. Under the Prelec function, the results agree with the previously published results that the defender is biased more into defending the more important road side units. However, under both the $��w��(�.�)��$ function and the framing effect, the players' behaviour does not depend on the loss penalty parameter, and the Prelec function dominates the framing effect. For small $��\alpha �$ values, the players make conservative decisions compared to higher $��\alpha �$

The integration of digital twins (DTs) in healthcare is critical but remains limited in real-time patient monitoring due to challenges in achieving low-latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud-based DT framework that optimises real-time healthcare monitoring, providing a timely solution for critical healthcare needs. The framework incorporates a Pyomo-based dynamic optimisation model, which reduces telemetry latency by 32% and improves response time by 52%, surpassing existing systems. Leveraging low-cost, low-latency multimodal sensors, the system continuously monitors critical physiological parameters, including SpO2, heart rate, and body temperature, enabling proactive health interventions. A DT definition language (Digital Twin Definition Language)-based time series analysis and twin graph platform further enhance sensor connectivity and scalability. Additionally, the integration of machine learning (ML) strengthens predictive accuracy, achieving 98% real-time accuracy and 99.58% under cross-validation (cv = 20) using the XGBoost algorithm. Empirical results demonstrate substantial improvements in processing time, system stability, and learning capacity, with real-time predictions completed in 17 ms. This framework represents a significant advancement in healthcare monitoring, offering a responsive and scalable solution to latency and resource constraints in real-time applications. Future research could explore incorporating additional sensors and advanced ML models to further expand its impact in healthcare applications.

Wireless sensor network (WSN) cluster-based architecture is a system designed to control and monitor specific events or phenomena remotely, and one of the important concerns that need quick attention is security risks such as an intrusion in WSN traffic. At the same time, a high-level security method may refer to an intrusion detection system|intrusion detection systems (IDS), which may be employed effectively to achieve a higher level of security in detecting an intruder attack or any attack initiated within a WSN system. The significance of the detection of network intrusions on heterogeneous cluster-based sensor networks with wireless connections, as well as the approaches to machine learning utilised in IDS model development, were discussed. In addition, this research conducted several comparative studies of feature selection techniques and machine learning methodologies in the development of intrusion detection systems. The authors used a bibliometric indicator to identify the leading trends when it comes to IDS, and the VOS viewer was used to create a spatial mapping of co-authorship, co-occurrence, and citation types of analysis with their respective units of study. The purpose of this research paper is to generate relevant findings and a research problem formulation that can lead to a research gap in the research topic's domain area.

The convergence of Internet of Things (IoT), Cloud computing, and Fog computing, termed as Interconnected Intelligence (II), has revolutionised data management and real-time decision-making across various industries. This study introduces a hybrid architecture that integrates these technologies to optimise resource allocation, reduce latency, and improve decision accuracy. Unlike traditional models that rely heavily on centralised Cloud computing, our approach distributes computational tasks between IoT devices, Fog nodes, and Cloud servers, ensuring efficient real-time processing closer to the data source. The proposed system demonstrated a 20%–30% reduction in latency compared to Cloud-only architectures, and a 25% improvement in resource utilisation through dynamic load balancing between Fog and Cloud layers. Additionally, the system showed an increase in decision accuracy by 15%, enhancing real-time decision-making capabilities in critical applications such as industrial automation, healthcare, and smart urban environments. Data security and privacy were also significantly improved, achieving a 20% reduction in energy consumption by reducing reliance on centralised Cloud resources. These results were validated using real-world datasets from industrial, healthcare, and urban environments, underscoring the architecture's capability to support large-scale IoT deployments. Future research will focus on real-world validation and the development of enhanced dynamic resource management techniques.

Breast cancer is a type of cancer that is common in women worldwide, which emphasises its significance in identification with preventative treatment methods. The invented Photonic Crystal Fibre (PCF) exhibits outstanding performance in detecting Breast Cancer. The suggested model of the authors includes Hybrid layout within clad surface alongside Square Core. Introduced PCF detector exhibits max Relative Sensitivity (RS) of 96.82% as well 96.74% for breast cancer cell MCF-7 as well MDA-MB-231 correspondingly. The authors also investigated the Confinement Loss of 1.642 × 10⁻¹⁰ dB/m, 2.461 × 10⁻¹⁰ dB/m with Effective Material Loss of 0.0473, 0.0565 cm⁻¹ for the mentioned cells. Increased outcomes, customised therapy, plus quick action are made possible by swift identification in breast carcinoma. Timely malignancy detection reduces requirements to severe therapy by enabling simpler medicines. Additionally, making continuous illness detection easier, improving patient treatment. Furthermore, reliable evaluation contributes for investigating advancements that improve worldwide recognition as well as therapy alternatives. The introduced PCF Perhaps crucial in quick identification of these deadly cells as it has an extraordinary sensing ability. In conclusion, it has numerous possibilities in the healthcare sector.

Stress, widely recognised for its profound adverse effects on both physical and mental health, necessitates the development of innovative real-time detection methods. In this context, the escalating prevalence of wearable embedded systems, integrated with artificial intelligence (AI) for the continuous monitoring of critical physiological indicators like heart rate and blood pressure, accentuates their growing relevance in the efficient detection of stress. This article presents an innovative methodology employing deep learning algorithms on the Raspberry Pi 3, a platform distinguished by its cost-effectiveness and limited resources. The authors have developed an advanced AI algorithm that achieves high accuracy in real-time stress detection using photoplethysmography (PPG) sensors while significantly reducing computational demands. The authors’ method utilises an artificial neural network with long short-term memory (LSTM) layers, proving highly effective in time-series data analysis. In this study, the authors implement key TensorFlow toolkit optimisation techniques including quantisation aware training (QAT), Pruning and prune-preserving quantisation aware training. These techniques are applied to refine the authors’ model, decreasing size and latency without sacrificing accuracy. The results highlight the LSTM-based model's proficiency in accurately detecting stress using raw PPG sensor data on the Raspberry Pi 3, a comparatively affordable platform. The model attains an accuracy of 89.32% and an F1 score of 89.55% on the diverse wearable stress and affect detection stress-level dataset. Additionally, the authors’ optimised model exhibits substantial reductions in both size and latency while maintaining high accuracy. This approach shows great potential for various applications, such as stress monitoring in healthcare, sports, and workplace settings. The use of the Raspberry Pi 3 makes the system portable, cost-effective, and energy-efficient, enhancing its potential impact and accessibility.

Emerging wireless communication networks, exemplified by the evolution from 5G to subsequent technologies, necessitate extensive connectivity among myriad devices to fuel the ongoing technological progress. However, the magnitude of this network demands an extensive power source, requiring an advanced and sustainable system to be practically deployable. This study introduces a cutting-edge system utilising ambient RF signals for both wireless information transfer (WIT) and wireless power transfer. The proposed system addresses the energy deficiencies of billions of low-powered wireless devices within the network. Wireless-powered communication networks (WPCN) and simultaneous wireless energy and power transfer (SWIPT) technologies, operating on ambient RF signals, provide a solution for the energy requirements of these devices. Harvesting energy from ambient RF signals is pivotal for the signal transmissions of WPCN and SWIPT systems. The research focuses on enhancing the efficiency and feasibility of such systems, emphasising aspects like maximising energy efficiency (EE) and improving outage performance (OP). The paper underscores the ubiquitous connectivity resulting from node mobility and delves into the emerging models of WPCN and SWIPT, along with collaborative technologies integrated with these models. It explores resource allocation (RA), multiple-input multiple-output (MIMO) technology in the context of WPCN, and various aspects of relaying operations, including SWIPT-MIMO and SWIPT receiver architecture. Conclusively, the comprehensive survey affirms that leveraging ambient RF signals for WIT and power transfer can significantly enhance EE, OP, RA, and overall network capabilities. This improvement positions the proposed system as a promising solution for meeting the connectivity demands of future wireless communication technologies.

Mobile Ad hoc Networks (MANETs) are crucial wireless networks for military, corporate, and emergency use, yet they are vulnerable to disruptions from malicious nodes. The presence of malicious nodes can lead to message transmission and routing disorganisation, and network performance is effectively compromised. Game theory-based fuzzy secure clustering (GTFSC) improves performance metrics in low-scale and high-scale networks. This protocol's novel ability to dynamically scale performance measures as nodes expand improves efficiency and adaptability. While improving performance metrics, the proposed algorithm also efficiently identifies malicious nodes and re-routes the transmission, excluding the found malicious nodes. For any MANET system, secure and successful data transmission is paramount. The proposed protocol integrates various algorithms to fulfil its aim of customised EGT, GWO, and fuzzy clustering. Black hole attacks, grey hole attacks, Sybil attacks, and data tampering attacks are all considered to provide comprehensive attacks on MANET. Every node is assigned trust values, which get updated on data transmission. Fuzzy Clustering is employed to identify malicious nodes. Evolutionary Game Theory (EGT) optimises network organisation by designating cluster heads and clusters as nodes. Additionally, the proposed protocol leverages the Grey Wolf Optimisation Routing Algorithm (GWO), which establishes efficient routes from the source to the sink node. The analysis result shows maximum performance with a packet delivery ratio of around 98%, throughput of 90% end-to-end delay reduced by 15%, and energy consumption reduced by 18%, respectively, compared to an existing protocol.