IET Wireless Sensor Systems最新文献_第8页

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":"13 1","pages":"9-23"},"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":"12 5-6","pages":"149-160"},"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

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

Mohammad Ghamari, Hamid Kamangir, Keyvan Arezoo, Khalil Alipour

{"title":"Evaluation and calibration of low-cost off-the-shelf particulate matter sensors using machine learning techniques","authors":"Mohammad Ghamari, Hamid Kamangir, Keyvan Arezoo, Khalil Alipour","doi":"10.1049/wss2.12043","DOIUrl":"https://doi.org/10.1049/wss2.12043","url":null,"abstract":"The use of inexpensive, lightweight, and portable particulate matter (PM) sensors is increasingly becoming popular in air quality monitoring applications. As an example, these low-cost sensors can be used in surface or underground coal mines for monitoring of inhalable dust, and monitoring of inhalable particles in real-time can be beneficial as it can possibly assist in preventing coal mine related respiratory diseases such as black lung disease. However, commercially available PM sensors are not inherently calibrated, and as a result, they have vague and unclear measurement accuracy. Therefore, they must initially be evaluated and compared with standardised instruments to be ready to be deployed in the fields. In this study, three different types of inexpensive, light-scattering-based widely available PM sensors (Shinyei PPD42NS, Sharp GP2Y1010AU0F, and Laser SEN0177) are evaluated and calibrated with reference instruments. PM sensors are compared with reference instruments in a controlled environment. The calibration is done by means of different machine learning techniques. The results demonstrate that the calibrated response obtained by fusion of sensors has a higher accuracy in comparison to the calibrated response of each individual sensor.","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"12 5-6","pages":"134-148"},"PeriodicalIF":1.9,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91800940","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

Applications of wireless sensor systems to sleep stage estimation for home sleep monitoring 无线传感器系统在家庭睡眠监测中睡眠阶段估计的应用

IF 1.9

IET Wireless Sensor Systems Pub Date : 2022-10-11 DOI: 10.1049/wss2.12042

Jin-Shyan Lee, Ming-Feng Dong

{"title":"Applications of wireless sensor systems to sleep stage estimation for home sleep monitoring","authors":"Jin-Shyan Lee, Ming-Feng Dong","doi":"10.1049/wss2.12042","DOIUrl":"https://doi.org/10.1049/wss2.12042","url":null,"abstract":"In recent years, research on sleep monitoring and analysis has attracted many scholars. Among them, the polysomnography (PSG) is performed more accurately. However, PSG is not suitable to be used at home due to its complicated operation and expensive cost. On the other hand, although the Pittsburgh sleep quality index (PSQI) is a standardized form for sleep quality assessment, the subjective and backward evaluation may lead to intuitive results. Therefore, this paper is intended to develop a sleep stage estimation system for home health care services. In the proposed platform, the sleep conditions, including the heart rate (HR) and body movement, are collected by an HR monitor and a force sensor array, respectively. Also, the fuzzy inference system is applied to the sleep depth evaluation, and then, the finite state machine is utilised to estimate the sleep stage. Experimental results show that the developed platform not only reduces the burden of PSG measurements, but also provides more convincible and reasonable results, presenting as an assistive tool of the conventional PSQI tests.","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"12 5-6","pages":"123-133"},"PeriodicalIF":1.9,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91558901","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

Applications of wireless sensor systems to sleep stage estimation for home sleep monitoring 无线传感器系统在家庭睡眠监测中睡眠阶段估计的应用

IF 1.9

IET Wireless Sensor Systems Pub Date : 2022-10-11 DOI: 10.1049/wss2.12042

Jin-Shyan Lee, M. Dong

引用次数: 0

LoRaWAN-implemented node localisation based on received signal strength indicator LoRaWAN基于接收信号强度指示符实现节点定位

IF 1.9

IET Wireless Sensor Systems Pub Date : 2022-09-15 DOI: 10.1049/wss2.12039

Ibrahim Aqeel, Ephraim Iorkyase, Hussein Zangoti, Christos Tachtatzis, Robert Atkinson, Ivan Andonovic

{"title":"LoRaWAN-implemented node localisation based on received signal strength indicator","authors":"Ibrahim Aqeel, Ephraim Iorkyase, Hussein Zangoti, Christos Tachtatzis, Robert Atkinson, Ivan Andonovic","doi":"10.1049/wss2.12039","DOIUrl":"https://doi.org/10.1049/wss2.12039","url":null,"abstract":"Long Range Wireless Area Network (LoRaWAN) provides desirable solutions for Internet of Things (IoT) applications that require hundreds or thousands of actively connected devices (nodes) to monitor the environment or processes. In most cases, the location information of the devices arguably plays a critical role and is desirable. In this regard, the physical characteristics of the communication channel can be leveraged to provide a feasible and affordable node localisation solution. This paper presents an evaluation of the performance of LoRaWAN Received Signal Strength Indicator (RSSI)-based node localisation in a sandstorm environment. The authors employ machine learning algorithms, Support Vector Regression and Gaussian Process Regression, which turn the high variance of RSSI due to frequency hopping feature of LoRaWAN to advantage, creating unique signatures representing different locations. In this work, the RSSI features are used as input location fingerprints into the machine learning models. The proposed method reduces node localisation complexity when compared to GPS-based approaches whilst provisioning more extensive connection paths. Furthermore, the impact of LoRa spreading factor and kernel function on the performance of the developed models have been studied. Experimental results show that the SVR-enhanced fingerprint yields the most significant improvement in node localisation performance.","PeriodicalId":51726,"journal":{"name":"IET Wireless Sensor Systems","volume":"13 4","pages":"117-132"},"PeriodicalIF":1.9,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/wss2.12039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50134090","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: 3

WLS algorithm for UAV navigation in satellite-less environments 无卫星环境下无人机导航的WLS算法

IF 1.9

IET Wireless Sensor Systems Pub Date : 2022-08-04 DOI: 10.1049/wss2.12041

Ricardo Santos, J. Matos-Carvalho, Slavisa Tomic, M. Beko

引用次数: 3

Use of wireless sensor network system based on water level, rain, conductivity, oil and turbidity sensors to monitor the storm sewerage 采用基于水位、雨量、电导率、油和浊度传感器的无线传感网络系统对暴雨污水进行监测

IF 1.9

IET Wireless Sensor Systems Pub Date : 2022-07-12 DOI: 10.1049/wss2.12040

Javier Rocher, A. Rego, Jaime Lloret, Luís M. L. Oliveira

引用次数: 1