IET Wireless Sensor Systems最新文献

{"title":"Energy-Efficient M2M Scheduling Over Cellular Networks: A Survey","authors":"Moses Kwasi Torkudzor,&nbsp;Stefan Schwarz,&nbsp;Jamal-Deen Abdulai,&nbsp;Markus Rupp,&nbsp;Margaret Richardson Ansah,&nbsp;Charles Saah","doi":"10.1049/wss2.70041","DOIUrl":"https://doi.org/10.1049/wss2.70041","url":null,"abstract":"<p>The introduction of machine-to-machine communication (M2M), also known as machine-type communication (MTC), is a key enabler of the internet of things (IoT), facilitating large-scale connectivity among heterogeneous devices in cellular networks. Although this technological advancement has opened new revenue opportunities for service providers, the rapid growth in M2M devices introduces significant challenges related to energy consumption, spectrum utilisation, network scalability and heterogeneous quality of service (QoS) requirements. These challenges complicate the task of balancing energy efficiency (EE) with diverse QoS demands. Among the potential solutions, scheduling strategies have emerged as a critical technique for improving the EE problem in M2M communication. This paper presents a comprehensive survey of scheduling techniques for optimising energy consumption in M2M communication. This provides a comprehensive review of energy-efficient scheduling techniques for M2M communications. It further examines emerging technological trends that influence scheduling design, including artificial intelligence (AI), machine learning (ML), simultaneous wireless information and power transfer (SWIPT), renewable energy harvesting and digital twin technologies in 5G/6G networks. Finally, the paper identifies key research challenges and outlines future directions for the development of intelligent, energy-efficient scheduling frameworks in M2M and IoT ecosystems.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Poisoning-Aware Data Cleansing and Restoration for Secure IoT-Enabled Wireless Sensor Systems","authors":"Quazi Mamun","doi":"10.1049/wss2.70040","DOIUrl":"https://doi.org/10.1049/wss2.70040","url":null,"abstract":"<p>Wireless sensor systems increasingly serve as the sensing layer of Internet of Things (IoT), industrial IoT and critical infrastructure applications, where corrupted readings can poison downstream classifiers, anomaly detectors and decision-support models. This paper presents a poisoning-aware data cleansing and restoration framework for secure IoT-enabled wireless sensor systems. The framework treats suspicious readings as potential adversarial data-poisoning attempts rather than solely as benign faults. Mobile Data Collectors (MDCs), static gateways or cluster heads act as edge sanitisation points that detect relationally inconsistent readings, neutralise suspicious values, restore them using co-appearance-, correlation- and similarity-aware imputation, and produce anomaly reports for security-aware decision support. The paper strengthens co-appearance-based cleansing through a richer threat model, robust statistical interpretation, optimisation-based trimmed-loss logic and adaptive thresholding. Using the Intel Lab WSN dataset, the framework is evaluated across 96 poisoning scenarios that cover different poisoning levels, perturbation ranges, distribution models and poisoning patterns. Results show a conservative, precision-oriented detection profile, with an average Error Recall of 0.130 and Error Precision of 0.514, while restoration achieves an index of agreement of 0.888 and an RMSE of 0.121. The framework is best interpreted as a high-confidence edge sanitisation layer within a defence-in-depth strategy.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI-Enhanced Multiband Terahertz Metamaterial Biosensor for Intelligent Leukaemia Detection","authors":"Musa N. Hamza,&nbsp;Mohammad Alibakhshikenari,&nbsp;Bal Virdee,&nbsp;Sunil Lavadiya,&nbsp;Iftikhar ud Din,&nbsp;Bruno Sanches,&nbsp;Slawomir Koziel,&nbsp;Syeda Iffat Naqvi,&nbsp;Abinash Panda,&nbsp;Ali Farmani,&nbsp;Zinelabiddine Mezache,&nbsp;Hassan Zakeri,&nbsp;Mohammad Naser-Moghadasi,&nbsp;Takfarinas Saber","doi":"10.1049/wss2.70039","DOIUrl":"https://doi.org/10.1049/wss2.70039","url":null,"abstract":"<p>This study presents an artificial intelligence (AI)-augmented micron-scale terahertz (THz) metamaterial biosensor for early-stage leukaemia diagnosis. The proposed biosensor employs a tri-negative (<i>ε, μ, n</i>) perfect absorber structure with an optimised multiband spectral response, enabling high-Q narrowband resonances across the 0.5–2 THz frequency range. These engineered electromagnetic characteristics enhance field confinement and analyte interaction, enabling the detection of refractive index variations as small as ∼0.014 RIU between healthy and leukaemia-affected blood samples. Spectral and field analyses reveal distinct resonance shifts, absorption variations and altered electric and magnetic field distributions in the presence of cancerous samples. Quantitative performance evaluation demonstrates excellent sensing characteristics, including a Q-factor of 268.34, a figure of merit (FOM) of 56722.80 RIU⁻¹, and Euclidean sensitivity of 355.859564 THz RIU⁻¹. These values indicate improved performance compared with previously reported THz biosensors for cancer detection. AI integration enables automated classification of healthy and cancerous samples using S-parameter processing and full-spectrum similarity metrics, achieving classification accuracy exceeding 95%. Comparative analysis with state-of-the-art biosensors demonstrates leukaemia-specific dielectric targeting, early-stage detection capability and an AI-assisted analytical framework for enhanced spectral interpretation. The results highlight the potential of the proposed platform as a noninvasive and label-free approach for blood cancer diagnostics, combining high spectral resolution, full-spectrum analysis and automated decision-making within a unified sensing framework.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable Smart Facility: A Real-Time Internet of Things-Based Environmental Monitoring and Predictive Control System Using Amazon Web Services Cloud Services","authors":"Hussein Mohammed,&nbsp;Mohammed Burai,&nbsp;Raied Ibrahim","doi":"10.1049/wss2.70035","DOIUrl":"https://doi.org/10.1049/wss2.70035","url":null,"abstract":"<p>Industrial facilities face critical environmental hazards—including temperature surges, humidity imbalances and combustible gas leaks—that existing monitoring solutions fail to anticipate, responding only after threshold violations occur and leaving a window of risk between hazard onset and system response. This paper presents Sustainable Smart Facility (SSF), a novel Internet of Things (IoT)–cloud architecture combining real-time environmental sensing with a predictive anomaly-detection layer to enable proactive hazard mitigation. An ESP32 microcontroller continuously samples DHT11 temperature/humidity and MQ2 gas sensors, transmitting readings via secure MQTT over transport layer security (TLS) to AWS IoT Core. A serverless AWS Lambda function applies both rule-based threshold logic and a lightweight <i>Z</i>-score anomaly detection algorithm to classify environmental states and issue actuator commands—controlling a ventilation fan and a servo-operated window—before critical thresholds are breached. Sensor data are persisted in Amazon DynamoDB for real-time querying and archived in Amazon S3 in JSON format for longitudinal analysis via AWS Glue and Amazon Athena. Amazon simple notification service (SNS) delivers immediate email alerts to facility managers upon threshold violation or anomaly detection. Comprehensive experiments over a 30-day deployment demonstrate end-to-end latency of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>487</mn>\u0000 <mo>±</mo>\u0000 <mn>63</mn>\u0000 </mrow>\u0000 <annotation> $487pm 63$</annotation>\u0000 </semantics></math> ms, MQTT delivery reliability of 99.3%, Lambda execution time of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>245</mn>\u0000 <mo>±</mo>\u0000 <mn>38</mn>\u0000 </mrow>\u0000 <annotation> $245pm 38$</annotation>\u0000 </semantics></math> ms, anomaly-detection <i>F</i>1-score of 93.0% and a false-positive rate of 2.1%. Scalability tests confirm sub-linear latency growth to 50 concurrent sensor nodes below 800 ms. Comparative evaluation against five state-of-the-art IoT monitoring systems shows SSF achieves the best balance of responsiveness, reliability, predictive capability and cost efficiency ($0.0023 per 1000 readings).</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Efficient Data Collection in Internet of Underwater Things: A Review on Routing Techniques, Acoustic Modems and Simulation Tools","authors":"Dhivya Bharathi R,&nbsp;Jayavignesh Thyagarajan","doi":"10.1049/wss2.70036","DOIUrl":"https://doi.org/10.1049/wss2.70036","url":null,"abstract":"<p>Underwater communication presents significant challenges due to factors such as signal attenuation, absorption, multipath propagation, limited bandwidth, high latency, marine noise and fluctuating channel conditions that make data collection difficult in underwater environments. This work reviews the data collection methods via (i) multi-hop routing and (ii) autonomous underwater vehicles (AUVs) in underwater acoustic sensor networks (UASNs), which are the subset of internet of underwater things (IoUT). We present an in-depth analysis of routing protocols developed over the past two decades (2006–2026) with their advantages and limitations. In addition, we discuss the role of reinforcement learning (RL), a core subfield of machine learning, in enhancing routing efficiency by optimising key metrics such as next-hop selection, energy consumption, packet delivery ratio and network lifetime. Additionally, our work investigates the evolution of acoustic modems from traditional to software-defined and the capabilities of emerging multimodal acoustic modems. Furthermore, we evaluate the capabilities of simulation tools and underwater model libraries available to replicate real-world scenarios. Our analysis presents the need for improved model libraries that include hardware-accurate profiles. This work bridges the gap between theoretical modelling and practical underwater communication requirements by identifying the key research gaps in channel, energy, noise modelling, medium access control (MAC) layer support and RL learning capabilities, along with the integration of next-generation communication technologies, and provides future research directions for data collection in the IoUT environment.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WITHDRAWAL: An Intelligent Detection Device for Remote Fault Diagnosis","authors":"","doi":"10.1049/wss2.12060","DOIUrl":"https://doi.org/10.1049/wss2.12060","url":null,"abstract":"<p><b>WITHDRAWAL:</b> Y. He, X. Jiang, W. Deng, “An Intelligent Detection Device for Remote Fault Diagnosis”, <i>IET Wireless Sensor Systems</i> (2025): e12160. https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/wss2.12060</p><p>The above article, published online on 12 December 2025, on Wiley Online Library (wileyonlinelibrary.com), has been withdrawn by agreement between the Institution of Engineering and Technology and John Wiley &amp; Sons Ltd. UK. The withdrawal has been agreed as this manuscript was under consideration but the authors decided not to proceed with publication. Despite this decision, the article was subsequently published due to a technical error on the part of Wiley, the publisher, as an unedited accepted article prior to the version of record.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WITHDRAWAL: Balanced Optimization Algorithm for Regional Distance in Wireless Sensor Networks","authors":"","doi":"10.1049/wss2.12062","DOIUrl":"https://doi.org/10.1049/wss2.12062","url":null,"abstract":"<p><b>WITHDRAWAL:</b> Y. Dang, “Balanced Optimization Algorithm for Regional Distance in Wireless Sensor Networks”, <i>IET Wireless Sensor Systems</i> (2025): e12062. https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/wss2.12062</p><p>The above article, published online on 12 December 2025, on Wiley Online Library (wileyonlinelibrary.com), has been withdrawn by agreement between the Institution of Engineering and Technology and John Wiley &amp; Sons Ltd. UK. The withdrawal has been agreed as this manuscript was under consideration but the authors decided not to proceed with publication. Despite this decision, the article was subsequently published due to a technical error on the part of Wiley, the publisher, as an unedited accepted article prior to the version of record.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-Efficient Two-Level Epileptic Seizure Detection Approach for Remote Patient Monitoring in Fog Computing-Based IoMT Networks","authors":"Riyam Faisal Alwash,&nbsp;Ali Kadhum Idrees,&nbsp;Salah Al-Obaidi","doi":"10.1049/wss2.70037","DOIUrl":"https://doi.org/10.1049/wss2.70037","url":null,"abstract":"<p>The technologies and services used in the healthcare industry are evolving quickly. Remote patient monitoring is a new advancement in this field that offers several benefits in an ageing global population with rising health issues. The technology has advanced to a level that patients may engage in their regular daily activities at home while still being observed using contemporary communication and sensor technologies, starting with very basic applications to monitor patients inside hospital rooms. A significant amount of important sensor data is produced by biosensors, which contribute to reducing the energy of these devices and thus decreasing their lifetime. Therefore, it is essential to remove redundant data before sending it to the fog gateway. In this paper, we propose an Energy-efficient Two-level Epileptic Seizure Detection Approach (ETESeDA) for remote patient monitoring in fog computing-based IoMT networks. The ETESeDA approach works on two distinct levels. The first level operates at the edge gateway, where the ETESeDA approach applies a data reduction method based on a combination between Short-Time Fourier Transform (STFT), Grey Level Co-occurrence Matrix (GLCM), and Huffman encoding for extracting the useful information features from the EEG signals and compressing them before transferring them to fog gateway. The second level operates at the fog gateway, where the received features are decompressed and sent to machine learning model that has been trained to determine the condition of the patient and provide the suitable decision to medical staff. The proposed ETESeDA framework can alleviate both EEG data transmission and energy consumption with guaranteed precise and reliable epileptic seizure classification in IoMT environments. The performance evaluation using the Bonn EEG Dataset demonstrates that the proposed scheme achieves up to 100% accuracy for two-class classification and 97%–98.3% for the three-class classification, while maintaining superior sensitivity, specificity, precision, recall, and F1-score performance. The scheme also attained more than 94% space and over 80% energy savings without impairing accuracy.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Parameter Adaptive LEACH Routing Framework for Energy-Efficient Wireless Sensor Networks","authors":"Adamu Murtala Zungeru,&nbsp;Koketso Bathamile,&nbsp;Ernest Gomolemo Modise,&nbsp;Ibo Ngebani,&nbsp;Nonofo Ditshego","doi":"10.1049/wss2.70038","DOIUrl":"https://doi.org/10.1049/wss2.70038","url":null,"abstract":"<p>Wireless Sensor Networks (WSNs) are constrained by limited energy resources, making energy-efficient routing a critical challenge. Traditional protocols such as Flooding and AODV suffer from high energy consumption, redundant transmissions and poor scalability. Although LEACH improves efficiency through clustering, its random cluster-head (CH) selection leads to imbalanced energy usage. This paper proposes an Intelligent Adaptive LEACH (IA-LEACH) routing protocol that incorporates a lightweight multi-parameter decision model for adaptive CH selection. The proposed approach computes a cluster-head suitability score based on residual energy, distance to the base station and node density, enabling deterministic CH selection and balanced energy consumption. Simulation results demonstrate that IA-LEACH outperforms LEACH, AODV and Flooding. It achieves the lowest energy consumption (∼0.2 J per round), compared to AODV (∼1.6 J) and Flooding (∼2.8 J), while maintaining higher residual energy. IA-LEACH also exhibits lower latency (∼0.4 s) than LEACH (∼0.5 s), AODV (∼1.0 s) and Flooding (∼2.0 s). Furthermore, it extends network lifetime beyond LEACH and AODV while maintaining a packet delivery ratio of ∼0.9. These results confirm that adaptive clustering significantly improves energy efficiency, network lifetime and overall WSN performance.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing the Comprehensive Intelligent Monitoring System Based on Wireless Sensor Networks (WSNs) for Mango Crop Precision Agriculture","authors":"Wen-Tsai Sung,&nbsp;Indra Griha Tofik Isa,&nbsp;Sung-Jung Hsiao","doi":"10.1049/wss2.70034","DOIUrl":"https://doi.org/10.1049/wss2.70034","url":null,"abstract":"<p>Precision agriculture (PA) is an alternative for increasing agricultural productivity in an efficient and integrated manner. In this study, a comprehensive intelligent monitoring system was developed to capture atmospheric conditions, soil parameters and crop physiology for mango crops, integrating physical sensing devices, object detection models, WSNs and IoT. A physical sensing device validation was conducted to evaluate the performance of the humidity, temperature, soil moisture and soil pH sensors using MAPE analysis, yielding a MAPE of 0.63%. A proposed improved YOLOv10 has been developed to capture the mango fruit condition, whether it has a disease or not, consisting of five classes, including alternaria, anthracnose, black_mould_rot, stem_end_rot and healthy. The improved YOLOv10 leverages the advantages of the StarNet architecture, which is embedded into the backbone structure to achieve high-dimensional features in low-dimensional space, which increases the accuracy of performance without adding computational cost. The improved YOLOv10 achieved an mAP₅₀ value of 0.939, whereas faster R-CNN, SSD, YOLOv5, YOLOv7, YOLOv8, YOLOv10 and YOLOv11 achieved mAP₅₀ values of 0.714, 0.862, 0.880, 0.762, 0.893, 0.905 and 0.921, respectively. Meanwhile, the constructed WSN system implements the Aquila optimiser (AO) method to achieve energy efficiency and an optimal response time, which indicates that at 10 nodes, the energy consumption, delay and loss rate are 165 mJ, 128 ms and 7.6%, respectively. The results of the experiment show that the constructed WSN system has achieved optimal efficiency compared to the existing system.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148463152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}