... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks最新文献_第2页

SLAE6: Secure and Lightweight Authenticated Encryption Scheme for 6LoWPAN Networks SLAE6: 6LoWPAN网络的安全轻量级认证加密方案

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2023-01-01 DOI: 10.5220/0011632200003399

Fatma Foad Ashrif, Elankovan Sundarajan, Rami Ahmed, M Zahid Hasan

引用次数: 5

A Low-Cost Sensors Study Measuring Exposure to Particulate Matter in Mobility Situations 低成本传感器在移动环境中测量暴露于颗粒物的研究

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2023-01-01 DOI: 10.5220/0011747600003399

Marie-Laure Aix, Mélaine Claitte, D. Bicout

引用次数: 0

TSCH Slotframe Optimization Using Differential Evolution Algorithm for Heterogeneous Sensor Networks 基于差分进化算法的异构传感器网络TSCH槽帧优化

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2023-01-01 DOI: 10.5220/0011623400003399

Aida Vatankhah, R. Liscano, Tarana Ara

引用次数: 0

SmartAct: Energy Efficient and Real-Time Hand-to-Mouth Gesture Detection Using Wearable RGB-T. SmartAct:使用可穿戴RGB-T的节能实时手对嘴手势检测。

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2022-09-01 DOI: 10.1109/bsn56160.2022.9928492

Soroush Shahi, Mahdi Pedram, Glenn Fernandes, Nabil Alshurafa

{"title":"SmartAct: Energy Efficient and Real-Time Hand-to-Mouth Gesture Detection Using Wearable RGB-T.","authors":"Soroush Shahi, Mahdi Pedram, Glenn Fernandes, Nabil Alshurafa","doi":"10.1109/bsn56160.2022.9928492","DOIUrl":"https://doi.org/10.1109/bsn56160.2022.9928492","url":null,"abstract":"<p><p>Researchers have been leveraging wearable cameras to both visually confirm and automatically detect individuals' eating habits. However, energy-intensive tasks such as continuously collecting and storing RGB images in memory, or running algorithms in real-time to automate detection of eating, greatly impacts battery life. Since eating moments are spread sparsely throughout the day, battery life can be mitigated by recording and processing data only when there is a high likelihood of eating. We present a framework comprising a golf-ball sized wearable device using a low-powered thermal sensor array and real-time activation algorithm that activates high-energy tasks when a hand-to-mouth gesture is confirmed by the thermal sensor array. The high-energy tasks tested are turning on the RGB camera (Trigger RGB mode) and running inference on an on-device machine learning model (Trigger ML mode). Our experimental setup involved the design of a wearable camera, 6 participants collecting 18 hours of data with and without eating, the implementation of a feeding gesture detection algorithm on-device, and measures of power saving using our activation method. Our activation algorithm demonstrates an average of at-least 31.5% increase in battery life time, with minimal drop of recall (5%) and without impacting the accuracy of detecting eating (a slight 4.1% increase in F1-Score).</p>","PeriodicalId":72028,"journal":{"name":"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks","volume":"2022 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176499/pdf/nihms-1889783.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9460562","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: 2

IoT Application for Monitoring and Storage of Temperature History in Electric Motors 物联网应用于电机温度历史的监测和存储

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2022-01-01 DOI: 10.5220/0010818300003118

Jairovan Denis de Paiva, Carlos Roberto Silveira Junior, Arquimedes Lopes da Silva

引用次数: 0

Semantic Segmentation of Retinal Blood Vessels from Fundus Images by using CNN and the Random Forest Algorithm 基于CNN和随机森林算法的眼底图像视网膜血管语义分割

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2022-01-01 DOI: 10.5220/0010911800003118

Ayoub Skouta, Abdelali Elmoufidi, Said Jai-Andaloussi, O. Ouchetto

{"title":"Semantic Segmentation of Retinal Blood Vessels from Fundus Images by using CNN and the Random Forest Algorithm","authors":"Ayoub Skouta, Abdelali Elmoufidi, Said Jai-Andaloussi, O. Ouchetto","doi":"10.5220/0010911800003118","DOIUrl":"https://doi.org/10.5220/0010911800003118","url":null,"abstract":"Abstract: In this paper, we present a new study to improve the automated segmentation of blood vessels in diabetic retinopathy images. Pre-processing is necessary due to the contrast between the blood vessels and the background, as well as the uneven illumination of the retinal images, in order to produce better quality data to be used in further processing. We use data augmentation techniques to increase the amount of accessible data in the dataset to overcome the data sparsity problem that deep learning requires. We then use the CNN VGG16 architecture to extract the feature from the preprocessed background images. The Random Forest method will then use the extracted attributes as input parameters. We used part of the augmented dataset to train the model (1764 images, representing the training set); the rest of the dataset will be used to test the model (196 images, representing the test set). Regarding the model validation phase, we used the dedicated part for testing the DRIVE dataset. Promising results compared to the state of the art were obtained. The method achieved an accuracy of 98.7%, a sensitivity of 97.4% and specificity of 99.5%. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.","PeriodicalId":72028,"journal":{"name":"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks","volume":"5 1","pages":"163-170"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79854395","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}

引用次数: 4

Underwater Wireless Sensor Networks: A Review 水下无线传感器网络:综述

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2022-01-01 DOI: 10.5220/0010970700003118

Mohammad Alsulami, Rafaat Elfouly, Reda Ammar

{"title":"Underwater Wireless Sensor Networks: A Review","authors":"Mohammad Alsulami, Rafaat Elfouly, Reda Ammar","doi":"10.5220/0010970700003118","DOIUrl":"https://doi.org/10.5220/0010970700003118","url":null,"abstract":"Several disciplines like science, engineering, and biological industry have been influenced by sensor networks which have brought sensing and computation into reality. The possibility of having these sensors physically assigned close to the target whose parameters are to be observed enables remote monitoring of various aspects of the physical world. Wireless channeling of information beneath the ocean or generally underwater has provided the best technological ways of oceanic observations. Ocean bottoms have been monitored traditionally by deploying oceanographic sensors that obtain information at distinct and fixed ocean zones. The oceanographic instruments are then recovered when the tasks are completed. This implies that data cannot be monitored remotely since there is no collaborative communication of obtained data between the collection point and the monitoring end. The data recorded can also be destroyed in case of a non-successful mission. Oceanic observations have been made primarily possible by sensor networks carefully laid out under the waters. Underwater sensor networks can also be achieved wirelessly by establishing communications between sensors and monitors without major cabling. These are known as Underwater Wireless Sensor Networks (UWSNs). The UWSNs are comprised of various gadgets like vehicles that can operate autonomously under the water and sensors. Deployment of these gadgets is done in targeted acoustic zones for the collection of data and monitoring tasks. Bilateral communication is established between stations based on the ground and different UWSNs nodes. This enables instantaneous remote monitoring and communication of information from the specified oceanic zones to engineering personnel based on the shores. This paper looks at the various aspects of Underwater Wireless Sensor Networks UWSNs including their importance, applications, network architecture, requirements, and challenges and in their deployments.","PeriodicalId":72028,"journal":{"name":"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks","volume":"5 1","pages":"202-214"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86815337","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}

引用次数: 13

A Novel Energy-efficient Wormhole Attack Prevention Protocol for WSN based on Trust and Reputation Factors 一种基于信任和声誉因子的无线传感器网络节能虫洞攻击防御协议

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2022-01-01 DOI: 10.5220/0010951400003118

Saad Al-Ahmadi

{"title":"A Novel Energy-efficient Wormhole Attack Prevention Protocol for WSN based on Trust and Reputation Factors","authors":"Saad Al-Ahmadi","doi":"10.5220/0010951400003118","DOIUrl":"https://doi.org/10.5220/0010951400003118","url":null,"abstract":"The deployment of Wireless Sensor Networks (WSNs) for the Internet of Things (IoT) is important, but this also poses some security issues. Wireless Sensor Networks (WSNs) are vulnerable to various attacks, such as the Wormhole attack. The Wormhole attack is one of the most severe attacks on WSNs that is particularly challenging to defend against even when the communication is authentic, and sensors are not compromised. Existing techniques to detect and protect against Wormhole attacks place a substantial burden on the scarce sensor resources and do not consider the dynamic nature of the network. In this paper, a novel Energy Efficient Wormhole Attack Prevention Protocol (EWATR) is proposed to protect WSNs against Wormhole attacks. EWATR is based on trust and reputation among WSN nodes that consider the dynamic nature of the network. This study also compares EWATR against several state-of-the-art trust and reputation models through extensive simulations using the TRMSim-WSN simulator. Eventually, the simulation results show the superiority of EWATR over other proposed protocols in terms of efficient energy consumption and shorter","PeriodicalId":72028,"journal":{"name":"... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks","volume":"21 1","pages":"191-201"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86324565","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}

引用次数: 0

Reaction-Diffusion Inspired Sensor Networking: From Theory to Application 反应扩散启发的传感器网络:从理论到应用

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2022-01-01 DOI: 10.5220/0010993300003118

Shu-Yuan Wu, T. Brown, Hsien-Tseng Wang

引用次数: 0

Flexigy Smart-grid Architecture 灵活的智能电网架构

... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks Pub Date : 2022-01-01 DOI: 10.5220/0010918400003118

Tiago Fonseca, Luís Ferreira, L. Klein, J. Landeck, P. Sousa

引用次数: 2