IET Wireless Sensor Systems最新文献_第4页

Levenberg Marquardt artificial neural network model for self‐organising networks implementation in wireless sensor network 无线传感器网络中自组织网络实现的Levenberg Marquardt人工神经网络模型

IF 1.9

IET Wireless Sensor Systems Pub Date : 2023-02-24 DOI: 10.1049/wss2.12052

G. Hakim, M. Hadi Habaebi, E. A. Elsheikh, F. M. Suliman, M. R. Islam, Siti Hajar Binti Yusoff, E. Adesta, R. Anzum

引用次数: 3

Analysis assaulting pattern for the security problem monitoring in 5G‐enabled sensor network systems with big data environment using artificial intelligence/machine learning 利用人工智能/机器学习分析大数据环境下5G传感器网络系统安全问题监控的攻击模式

IF 1.9

IET Wireless Sensor Systems Pub Date : 2023-02-22 DOI: 10.1049/wss2.12049

A. Rajawat, S. Goyal, P. Bedi, S. Kautish, D. P. Shrivastava

引用次数: 1

Steam computing paradigm: Cross‐layer solutions over cloud, fog, and edge computing 蒸汽计算范式:云、雾和边缘计算的跨层解决方案

IF 1.9

IET Wireless Sensor Systems Pub Date : 2023-02-20 DOI: 10.1049/wss2.12051

Abir Mchergui, Rejab Hajlaoui, T. Moulahi, Abdulatif Alabdulatif, P. Lorenz

引用次数: 1

Novel pedestrian navigation system based on zero velocity update procedure technology and improved Sage-Husa adaptive Kalman filter with index fading memory factor 基于零速度更新过程技术和改进的Sage-Husa指数衰落记忆自适应卡尔曼滤波器的新型行人导航系统

IF 1.9

IET Wireless Sensor Systems Pub Date : 2023-02-17 DOI: 10.1049/wss2.12050

Lei Huang, Yuting Shi, Jianhua Wang, Xiaoqian Zhang, Daming Xu, Di Sang

{"title":"Novel pedestrian navigation system based on zero velocity update procedure technology and improved Sage-Husa adaptive Kalman filter with index fading memory factor","authors":"Lei Huang, Yuting Shi, Jianhua Wang, Xiaoqian Zhang, Daming Xu, Di Sang","doi":"10.1049/wss2.12050","DOIUrl":"https://doi.org/10.1049/wss2.12050","url":null,"abstract":"To acquire an accurate location on the occasions, such as in an indoor, tunnel, and valley, where satellite navigation signals fail. The paper designs a pedestrian navigation system by using the zero velocity update procedure technology (ZUPT) and Kalman filter to reduce the location error. The measurement noise characteristic (mean and variance) of the micro electro mechanical systems gyros is unknown and time variant, but in traditional studies, it is usually thought and calculated as a constant. So the optimality of the error estimation of the Kalman filter cannot be reached. To address this question, this paper proposes the improved Sage–Husa Adaptive Kalman Filter (SHAKF) based on the index fading memory factor to realise the state estimation of the Kalman filter and navigation error correction. The advantage of improved SHAKF is it can accurately estimate the state vector when the measurement noise is unknown and time variant. To verify the validity of novel navigation methods, walking experiments under outdoor environments and indoor environments are carried out. The results of actual walking experiments demonstrate that the proposed method can effectively reduce the pedestrian location error compared with the traditional ZUPT method. The mean location error is reduced by more than 10%, and the variance of the location error is reduced by more than 5%.","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50135777","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}

引用次数: 0

A threshold-based sorting algorithm for dense wireless sensor systems and communication networks 一种用于密集无线传感器系统和通信网络的基于阈值的排序算法

IF 1.9

IET Wireless Sensor Systems Pub Date : 2023-01-16 DOI: 10.1049/wss2.12048

Shahriar Shirvani Moghaddam, Kiaksar Shirvani Moghaddam

{"title":"A threshold-based sorting algorithm for dense wireless sensor systems and communication networks","authors":"Shahriar Shirvani Moghaddam, Kiaksar Shirvani Moghaddam","doi":"10.1049/wss2.12048","DOIUrl":"https://doi.org/10.1049/wss2.12048","url":null,"abstract":"Nowadays, time-varying and high-density data of wireless sensor systems and communication networks compel us to propose and realise low-complexity and time-efficient algorithms for searching, clustering, and sorting. A novel threshold-based sorting algorithm applicable to dense and ultra-dense networks is proposed in this study. Instead of sorting whole data in a large data set and selecting a certain number of them, the proposed algorithm sorts a specific number of elements that are larger or smaller than a threshold level or located between two threshold values. First, based on the mean value and standard deviation of the data, a theoretical analysis to find the exact and approximate thresholds, respectively for known (Gaussian, uniform, Rayleigh, and negative exponential) and unknown probability distributions is presented. Then, an algorithm to sort a predefined number of data is realised. Finally, the effectiveness of the proposed algorithm is shown in the view of the time complexity order, the running time, and the similarity measure. To do this, theoretical and numerical analyses are used to show the superiority of the proposed algorithm in known and unknown distributions to the well-known conventional and gradual conventional versions of Merge, Quick, and K-S mean-based sorting algorithms.","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50151320","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}

引用次数: 0

Research on modelling of landscape architecture layout planning based on multi-sensor data fusion 基于多传感器数据融合的园林布局规划建模研究

IF 1.9

IET Wireless Sensor Systems Pub Date : 2023-01-13 DOI: 10.1049/wss2.12047

Pingfang Wen, Hongwu Xu

{"title":"Research on modelling of landscape architecture layout planning based on multi-sensor data fusion","authors":"Pingfang Wen, Hongwu Xu","doi":"10.1049/wss2.12047","DOIUrl":"https://doi.org/10.1049/wss2.12047","url":null,"abstract":"In order to improve the rationality of landscape architecture layout planning and reduce the energy consumption in the process of data fusion, a landscape architecture layout planning modelling method based on multi-sensor data fusion is proposed in this study. Firstly, the spatial layer of landscape plants is divided, and the quantitative index value of landscape architecture is calculated by Simpson diversity index. Secondly, the weight of plant index is determined by analytic hierarchy process, and the error of garden image is analysed by unsupervised classification of garden image based on multi-sensor data fusion. Then, the garden image is classified according to the existing template, and the training samples of each category of garden are extracted. Finally, the modelling of landscape architecture layout planning is constructed, and the optimal planning method of landscape architecture layout based on maximum likelihood method is proposed. The experimental results show that this method can effectively reduce the energy consumption in the data fusion process, reduce the probability of damaged nodes becoming cluster heads, improve the stability of landscape forest layout planning method, match the plants according to the plant species, and improve the rationality of landscape architecture layout planning.","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50150367","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}

引用次数: 0

Impact of using chirp spread spectrum physical layer and request-to-send/clear-to-send combined with frame concatenation in the IEEE 802.15.4 non-beacon enabled mode performance 在IEEE 802.15.4非信标启用模式性能中，使用线性调频扩频物理层和请求发送/清除发送与帧级联相结合的影响

IF 1.9

IET Wireless Sensor Systems Pub Date : 2023-01-07 DOI: 10.1049/wss2.12045

Norberto Barroca, Luís M. Borges, Periklis Chatzimisios, Fernando J. Velez

引用次数: 0

A priority-based congestion avoidance scheme for healthcare wireless sensor networks 一种基于优先级的医疗保健无线传感器网络拥塞避免方案

IF 1.9

IET Wireless Sensor Systems Pub Date : 2022-12-19 DOI: 10.1049/wss2.12046

Neda Mazloomi, Majid Gholipour, Arash Zaretalab

{"title":"A priority-based congestion avoidance scheme for healthcare wireless sensor networks","authors":"Neda Mazloomi, Majid Gholipour, Arash Zaretalab","doi":"10.1049/wss2.12046","DOIUrl":"https://doi.org/10.1049/wss2.12046","url":null,"abstract":"One of the most important challenges of wireless sensor networks is controlling network congestion and transmitting data in a way that improves the quality of service (QoS) parameters. Thus, it increases network performance and reduces energy consumption. Energy consumption increases due to various reasons, such as unsuccessful delivery of packets to the receiver, congestion in the network, retransmission of packets, delay in delivering packets to the base station, and so on. Given the importance of some data in the field of health, congestion should be avoided and secure data transmission should be ensured. This study divides the collected data into two groups based on their intrinsic characteristics by presenting a congestion management protocol: (1) critical data and (2) non-critical data. The proposed protocol provides a dynamic routing algorithm based on the TOPSIS model for non-critical data transmission. In addition, an algorithm for transmitting critical data through the shortest possible path is also provided based on support vector machines (SVMs). This improves the network performance through using multi-classification that is obtained from SVMs. The simulation results indicate that the proposed method works better than other methods and leads to better performance in delay, network performance, and power consumption.","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50146340","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}

引用次数: 1

Self-supervised multimodal fusion transformer for passive activity recognition 被动活动识别的自监督多模态融合变压器

IF 1.9

IET Wireless Sensor Systems Pub Date : 2022-11-10 DOI: 10.1049/wss2.12044

Armand K. Koupai, Mohammud J. Bocus, Raul Santos-Rodriguez, Robert J. Piechocki, Ryan McConville

{"title":"Self-supervised multimodal fusion transformer for passive activity recognition","authors":"Armand K. Koupai, Mohammud J. Bocus, Raul Santos-Rodriguez, Robert J. Piechocki, Ryan McConville","doi":"10.1049/wss2.12044","DOIUrl":"https://doi.org/10.1049/wss2.12044","url":null,"abstract":"The pervasiveness of Wi-Fi signals provides significant opportunities for human sensing and activity recognition in fields such as healthcare. The sensors most commonly used for passive Wi-Fi sensing are based on passive Wi-Fi radar (PWR) and channel state information (CSI) data, however current systems do not effectively exploit the information acquired through multiple sensors to recognise the different activities. In this study, new properties of the Transformer architecture for multimodal sensor fusion are explored. Different signal processing techniques are used to extract multiple image-based features from PWR and CSI data such as spectrograms, scalograms and Markov transition field (MTF). The Fusion Transformer, an attention-based model for multimodal and multi-sensor fusion is first proposed. Experimental results show that the Fusion Transformer approach can achieve competitive results compared to a ResNet architecture but with much fewer resources. To further improve the model, a simple and effective framework for multimodal and multi-sensor self-supervised learning (SSL) is proposed. The self-supervised Fusion Transformer outperforms the baselines, achieving a macro F1-score of 95.9%. Finally, this study shows how this approach significantly outperforms the others when trained with as little as 1% (2 min) of labelled training data to 20% (40 min) of labelled training data.","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91824585","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}

引用次数: 0

Evaluation and calibration of low-cost off-the-shelf particulate matter sensors using machine learning techniques 使用机器学习技术评估和校准低成本的现成颗粒物传感器

IF 1.9

IET Wireless Sensor Systems Pub Date : 2022-11-04 DOI: 10.1049/wss2.12043

M. Ghamari, H. Kamangir, Keyvan Arezoo, Khalil Alipour

引用次数: 0