IET Wireless Sensor Systems最新文献

{"title":"Performance of Charging Batteries by Using Compound Wireless Power Transfer System","authors":"Noor Qasim Atiyah Alsaedi,&nbsp;Muhammet Tahir Guneser,&nbsp;Ameer L. Saleh,&nbsp;László Számel","doi":"10.1049/wss2.70020","DOIUrl":"https://doi.org/10.1049/wss2.70020","url":null,"abstract":"<p>The charging power is being augmented to enhance the ease of electric automobiles. Nonetheless, high-power charging may lead to significant and apparent issues with battery heat production. Consequently, a prominent research concern is achieving an optimal equilibrium between rapid charging and preserving battery performance. An LCLC-compensated combination inductive and capacitive wireless power transfer (COMPT) system for battery charging is a novel system in this work. Inductive and capacitive power transfer (IPT and CPT) systems are combined in the circuit design. A detailed analysis of the combined circuit architecture’s working principle clarifies the link between circuit parameters and system power. For inductive and capacitive coupling design, finite-element analysis is used. The connector plate equivalent circuit model will be created for 100 kW. Design and implementation of the COMPPT system will demonstrate combined inductive and capacitive coupling. Both the inductive and capacitive couplers are 500 mm × 500 mm. Many air gaps are investigated for each coupler, ranging from 150 to 750 mm. Combining IPT and CPT systems yields COMPT’s output power. The prototype will generate 95.46 kW of power with 95.7% efficiency at a 1-MHz switching frequency and operate better under misalignment than the IPT System. The inductive-capacitive combination COMPT technology may be suitable for electric battery charging. The special environmental conditions that affect energy transfer, such as heat, rain, snow, and other important major environmental factors that affect energy transfer this research examines several lithium-ion, lead-acid, and nickel-metal hydride batteries, using experiments to analyze the impact of pulse charging and constant current charging on battery performance. A suggested assessment methodology assesses the viability of various charging techniques, including rapid charging for power batteries, based on charging duration, rechargeable capacity, temperature variations while charging, and battery life degradation during cyclic charging.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666016","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":"IFSTA: A Fuzzy Clustering Scheme With Enhanced SFLA–TDO and DCNN–LSTM Security for Wireless Sensor Networks","authors":"Monica Shivaji Gunjal,&nbsp;Pramodkumar H. Kulkarni","doi":"10.1049/wss2.70025","DOIUrl":"https://doi.org/10.1049/wss2.70025","url":null,"abstract":"<p>Wireless sensor networks (WSNs) are crucial for numerous industrial and commercial applications, driven by the rapid growth of Industry 4.0, advancements in wireless technology, and the Internet of things (IoT). However, the throughput and efficiency of WSNs are limited due to the limited lifetime of battery-operated sensors. Thus, optimal clustering is needed to enhance network performance. This paper presents a novel fuzzy-based clustering scheme (IFSTA) using an improved shuffle frog leaping algorithm (SFLA) based on Tasmanian devil optimisation (TDO) and an analytical hierarchical process (AHP) algorithm. The TDO is used to enhance convergence, solution diversity and the balance between exploitation and exploration. The IFSTA utilises residual energy, the energy GINI coefficient, inter-cluster distance (ICD), intra-cluster distance (ICD), load balancing, coverage and connectivity for optimising the cluster head (CH). The outcomes of the IFSTA are assessed based on network throughput, network lifetime, and residual energy. Further, the deep convolution neural network and long short-term memory (DCNN–LSTM)-based framework is utilised for malicious node detection to enhance security. The results show that the IFSTA helps achieve higher network lifetime, throughput, packet delivery ratio and scalability compared with the existing clustering optimisation techniques. The IFSTA provides a 16.38%–51.37% improvement in delay and a 16.37%–167% improvement in network lifetime compared to traditional techniques. The proposed DCNN–LSTM framework achieves an overall accuracy of 98.80%, an <i>F</i>1-score of 99.29%, a recall of 99.90% and a precision of 98.80% for malicious node detection on the SensorNetGuard dataset, demonstrating a significant improvement over traditional techniques.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147666017","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148091328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148084688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Soft Computing-Based Standard Mobile Sink and Data Fusion Technique for Maximising Lifetime of Rechargeable Wireless Sensor Networks”","authors":"","doi":"10.1049/wss2.70026","DOIUrl":"https://doi.org/10.1049/wss2.70026","url":null,"abstract":"<p>Chandra, Pankaj, Anurag Sinha, Anurag Singh, et al. 2025. “Soft Computing-Based Standard Mobile Sink and Data Fusion Technique for Maximising Lifetime of Rechargeable Wireless Sensor Networks.” <i>IET Wireless Sensor Systems</i>: e70012. https://doi.org/10.1049/wss2.70012.</p><p>The correct affiliation for Saikat Gochhait is provided below; the online version of this article has been corrected accordingly.</p><p>⁴Symbiosis Institute of Digital and Telecom Management, a constituent of Symbiosis International Deemed University, India</p><p>⁵Samara State Medical University, Russia</p><p>We apologise for this error.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567325","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":"Optimised Value and Quantity Balancing for Data Collection in Resource-Constrained UAV-Aided IoT","authors":"Ning An,&nbsp;Yi Hong,&nbsp;Chang Su,&nbsp;Lu Li,&nbsp;Xinyi Xu,&nbsp;Ruyue Wang","doi":"10.1049/wss2.70024","DOIUrl":"https://doi.org/10.1049/wss2.70024","url":null,"abstract":"<p>Unmanned aerial vehicles (UAVs) play a pivotal role in disaster response by enabling rapid information gathering from widely distributed sensor nodes. In resource-constrained scenarios, limitations in the number of UAVs and their onboard energy pose significant challenges in swiftly collecting and analysing high-priority data. In this paper, we tackle the crucial challenge of efficient multi-UAV-aided data collection in resource-constrained Internet of Things (IoT) environments where UAVs function as mobile data collectors. Given the inherent heterogeneity in data value and spatial distribution of IoT devices, a sophisticated approach is necessary to maximise data collection value while optimising energy consumption and coverage. This paper introduces a novel UAV-assisted IoT data collection model tailored for resource-constrained environments. By constructing a virtual backbone network and employing the NSGA-II algorithm for multi-objective optimisation, our model effectively routes data and schedules UAVs to maximise both data value and coverage. Extensive simulations demonstrate that our proposed algorithm achieves a superior balance between value and quantity in resource-constrained scenarios compared to existing data collection schemes.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563393","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148065676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising the Wireless Body Sensors Network (WBSN) of Elderly Health Monitoring System Through Actor-Critic Mechanism","authors":"Indra Griha Tofik Isa,&nbsp;Muhammad Imam Ammarullah,&nbsp;Adhan Efendi,&nbsp;Jasmine Nurul Izza,&nbsp;Yohanes Sinung Nugroho,&nbsp;Hamid Nasrullah,&nbsp;Febie Elfaladonna,&nbsp;Sigit Purnomo,&nbsp;Abdulfatah Abdu Yusuf","doi":"10.1049/wss2.70023","DOIUrl":"https://doi.org/10.1049/wss2.70023","url":null,"abstract":"<p>Elderly people represent a vulnerable population requiring continuous, reliable and timely health monitoring to maintain quality of life and reduce medical risks. Although existing wireless body sensor network (WBSN)-based systems primarily focus on energy efficiency, limited attention has been given to optimising time efficiency and real-time decision-making performance. This study proposes an elderly health monitoring system based on WBSN using an actor-critic deep reinforcement learning (ACDRL) framework to address this gap. The system utilises physiological state parameters, including heart rate, body temperature and oxygen saturation, to dynamically optimise monitoring and data transmission strategies. Device validation experiments demonstrated high sensing accuracy with a mean absolute percentage error (MAPE) of 0.68%. Model optimisation results indicate that a discount factor of 0.4 yields the best performance, achieving a mean absolute error (MAE) of 0.0401. Comparative evaluations against deep Q-network (DQN)-based, deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) algorithms show that the proposed ACDRL model consistently outperforms existing approaches across mean absolute error (MAE), integral absolute error (IAE) and integral squared error (ISE) metrics. Furthermore, real-world WBSN implementation involving multiple sensor nodes confirms that the proposed method significantly reduces time consumption, recording 1104 ms with 10 nodes lower than all benchmark models. These results demonstrate that the proposed ACDRL-based WBSN framework provides a scientifically validated, time-efficient and scalable solution for real-time elderly health monitoring, contributing to the advancement of intelligent healthcare systems.</p>","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315622","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148091252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148080198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}