Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems最新文献

Generative Model Based Fine-Grained Air Pollution Inference for Mobile Sensing Systems 基于生成模型的细粒度空气污染推断移动传感系统

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275216

Rui Ma, Xiangxiang Xu, H. Noh, Pei Zhang, Lin Zhang

引用次数: 6

Energy Efficient LPWAN Decoding via Joint Sparse Approximation 基于联合稀疏逼近的高效LPWAN解码

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275165

Jun Liu, Weitao Xu, Wen Hu

引用次数: 4

Automated Sleep Period Estimation in Wearable Multi-sensor Systems 可穿戴多传感器系统中睡眠周期的自动估计

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275155

Yuezhou Zhang, Zhicheng Yang, Zhengbo Zhang, Xiaoli Liu, Desen Cao, Peiyao Li, Jiewen Zheng, Ke Lan

引用次数: 5

Vehicle Air Pollution Monitoring Using IoTs 利用物联网监测车辆空气污染

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275202

Sujata Pal, A. Ghosh, Vivek Sethi

引用次数: 17

Toward Automated Acupressure Therapy 走向自动指压疗法

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3278481

Kun-Chan Lan, Guan-Sheng Li, Jun-Xiang Zhang

引用次数: 0

SECY APP: Self Configuration and Easy Management for Software Defined Smart Homes SECY APP:软件定义智能家居的自我配置和轻松管理

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275201

G. Maselli, Mauro Piva

引用次数: 2

Sensing Emotion from Voice Jitter 从声音抖动中感知情绪

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275182

Nazia Hossain, Mahmuda Naznin

引用次数: 4

Sentio

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3274843

Salma Elmalaki, H. Tsai, M. Srivastava

{"title":"Sentio","authors":"Salma Elmalaki, H. Tsai, M. Srivastava","doi":"10.1145/3274783.3274843","DOIUrl":"https://doi.org/10.1145/3274783.3274843","url":null,"abstract":"Thanks to the adoption of more sensors in the automotive industry, context-aware Advanced Driver Assistance Systems (ADAS) become possible. On one side, a common thread in ADAS applications is to focus entirely on the context of the vehicle and its surrounding vehicles leaving the human (driver) context out of consideration. On the other side, and due to the increasing sensing capabilities in mobile phones and wearable technologies, monitoring complex human context becomes feasible which paves the way to develop driver-in-the-loop context-aware ADAS that provide personalized driving experience. In this paper, we propose Sentio1; a Reinforcement Learning based algorithm to enhance the Forward Collision Warning (FCW) system leading to Driver-in-the-Loop FCW system. Since the human driving preference is unknown a priori, varies between different drivers, and moreover, varies across time for the same driver, the proposed Sentio algorithm needs to take into account all these variabilities which are not handled by the standard reinforcement learning algorithms. We verified the proposed algorithm against several human drivers. Our evaluation, across distracted human drivers, shows a significant enhancement in driver experience---compared to standard FCW systems---reflected by an increase in the driver safety by 94.28%, an improvement in the driving experience by 20.97%, a decrease in the false negatives from 55.90% down to 3.26%, while adding less than 130 ms runtime execution overhead.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115655291","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: 24

3D Localization for Sub-Centimeter Sized Devices 亚厘米尺寸设备的三维定位

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3274851

R. Nandakumar, Vikram Iyer, Shyamnath Gollakota

{"title":"3D Localization for Sub-Centimeter Sized Devices","authors":"R. Nandakumar, Vikram Iyer, Shyamnath Gollakota","doi":"10.1145/3274783.3274851","DOIUrl":"https://doi.org/10.1145/3274783.3274851","url":null,"abstract":"The vision of tracking small IoT devices runs into the reality of localization technologies --- today it is difficult to continuously track objects through walls in homes and warehouses on a coin cell battery. While Wi-Fi and ultra-wideband radios can provide tracking through walls, they do not last more than a month on small coin and button cell batteries since they consume tens of milliwatts of power. We present the first localization system that consumes microwatts of power at a mobile device and can be localized across multiple rooms in settings like homes and hospitals. To this end, we introduce a multi-band backscatter prototype that operates across 900 MHz, 2.4 and 5 GHz and can extract the backscatter phase information from signals that are below the noise floor. We build sub-centimeter sized prototypes which consume 93 μW and could last five to ten years on button cell batteries. We achieved ranges of up to 60 m away from the AP and accuracies of 2, 12, 50 and 145 cm at 1, 5, 30 and 60 m respectively. To demonstrate the potential of our design, we deploy it in two real-world scenarios: five homes in a metropolitan area and the surgery wing of a hospital in patient pre-op and post-op rooms as well as storage facilities.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"409 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115921551","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: 86

Breathing Disorder Detection Using Wearable Electrocardiogram And Oxygen Saturation 使用可穿戴心电图和血氧饱和度检测呼吸障碍

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems Pub Date : 2018-11-04 DOI: 10.1145/3274783.3275159

Yuezhou Zhang, Zhicheng Yang, Zhengbo Zhang, Peiyao Li, Desen Cao, Xiaoli Liu, Jiewen Zheng, Qian Yuan, Jianli Pan

引用次数: 6