Proceedings of the 17th Conference on Embedded Networked Sensor Systems最新文献

Conflict detection for smart cities services: poster abstract 智慧城市服务的冲突检测:海报摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361966

Shuxin Zhong, Desheng Zhang

引用次数: 0

SmrtFridge

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3360028

Amit Sharma, Archan Misra, Vengateswaran Subramaniam, Youngki Lee

{"title":"SmrtFridge","authors":"Amit Sharma, Archan Misra, Vengateswaran Subramaniam, Youngki Lee","doi":"10.1145/3356250.3360028","DOIUrl":"https://doi.org/10.1145/3356250.3360028","url":null,"abstract":"We present SmrtFridge, a consumer-grade smart fridge prototype that demonstrates two key capabilities: (a) identify the individual food items that users place in or remove from a fridge, and (b) estimate the residual quantity of food items inside a refrigerated container (opaque or transparent). Notably, both of these inferences are performed unobtrusively, without requiring any explicit user action or tagging of food objects. To achieve these capabilities, SmrtFridge uses a novel interaction-driven, multi-modal sensing pipeline, where Infrared (IR) and RGB video sensing, triggered whenever a user interacts naturally with the fridge, is used to extract a foreground visual image of the food item, which is then processed by a state-of-the-art DNN classifier. Concurrently, the residual food quantity is estimated by exploiting slight thermal differences, between the empty and filled portions of the container. Experimental studies, involving 12 users interacting naturally with 19 common food items and a commodity fridge, show that SmrtFridge is able to (a) extract at least 75% of a food item's image in over 97% of interaction episodes, and consequently identify the individual food items with precision/recall values of ~ 85%, and (b) perform robust coarse-grained (3 level) classification of the residual food quantity with an accuracy of ~ 75%.","PeriodicalId":432452,"journal":{"name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","volume":"440 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116715106","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: 5

LTE2B LTE2B

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3360022

Ruofeng Liu, Zhimeng Yin, Wenchao Jiang, T. He

{"title":"LTE2B","authors":"Ruofeng Liu, Zhimeng Yin, Wenchao Jiang, T. He","doi":"10.1145/3356250.3360022","DOIUrl":"https://doi.org/10.1145/3356250.3360022","url":null,"abstract":"Conventional gateway solutions are limited in satisfying the demand for ubiquitous connections among heterogeneous wireless devices, e.g., wide-area and personal-area network devices, due to the deployment complexity, high cost, and the incurred extra traffic. Recent advances propose the physical layer cross-technology communication to address these issues. However, existing CTC techniques commonly emulate the target waveform in the frequency domain (FDE). Despite their success, these FDE based techniques inherently suffer from high quantization errors and are insufficient for IoT applications that require high communication reliability. To improve the emulation accuracy, we are the first to introduce the time-domain emulation (TDE) that significantly outperforms FDE techniques in reducing quantization errors and offers reliable emulation even with limited sources, e.g., low modulation schemes. To validate our idea, we propose LTE2B, the first TDE based CTC work that enables LTE devices (e.g., smartphones) to transmit data frames demodulatable by ZigBee and Bluetooth low energy (BLE) devices. We implement the LTE2B on commodity devices (Nexus 5X smartphone and CC2530/CC1350 ZigBee/BLE SoC) with only payload embedding by penetrating the extremely complicated LTE stack. Our extensive evaluation demonstrates that TDE outperforms FDE, while LTE2B can achieve a robust (> 99% accuracy), long distance (> 400m) CTC performance under a full range of wireless configurations including indoor/outdoor, mobility, and duty-cycle settings.","PeriodicalId":432452,"journal":{"name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116718925","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: 1

BIGHand - A bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise: demo abstract BIGHand -一种用于独立在家锻炼的双边，集成和游戏化的握力中风康复系统:演示摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361949

Emery Comstock, Gabriel Guo, Wenyao Xu

{"title":"BIGHand - A bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise: demo abstract","authors":"Emery Comstock, Gabriel Guo, Wenyao Xu","doi":"10.1145/3356250.3361949","DOIUrl":"https://doi.org/10.1145/3356250.3361949","url":null,"abstract":"Effective home rehabilitation is important for recovery of hand grip ability in post-stroke individuals. This paper presents BIGHand, a bilateral, integrated, and gamified handgrip stroke rehabilitation system for independent at-home exercise. BIGHand consists of affordable sensor-integrated hardware (Vernier hand dynamometers, Arduino Uno, interface shield) used to obtain real-time grip force data, and a set of exergames designed as parts of an interactive structural rehabilitation program. This program pairs targeted difficulty progression with user-ability scaled controls to create an adaptive, challenging, and enticing rehabilitation environment. This training prepares users for the many activities of daily living (ADLs) by targeting strength, bilateral coordination, hand-eye coordination, speed, endurance, precision, and dynamic grip force adjustment. Multiple measures are taken to engage, motivate, and guide users through the at-home rehabilitation process, including \"smart\" post-game feedback and in-game goals. A demo video is available at https://youtu.be/zrLVkZZ4Ukc.","PeriodicalId":432452,"journal":{"name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117036326","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

LuxLink

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3360021

Rens Bloom, M. Z. Zamalloa, C. Pai

{"title":"LuxLink","authors":"Rens Bloom, M. Z. Zamalloa, C. Pai","doi":"10.1145/3356250.3360021","DOIUrl":"https://doi.org/10.1145/3356250.3360021","url":null,"abstract":"Transmitting information with visible light requires controlling the intensity of the light source. Many light sources in our environments, however, cannot be controlled (not only the sun but also plenty of light bulbs). These uncontrollable light sources provide an immense amount of ambient light that could be used for wireless communication, yet few studies are exploiting this opportunity. We provide a detailed analysis of a Hardware- and Physical-Layer to create safe and reliable wireless links relying solely on ambient light and simple photosensors. Motivated by recent studies, our platform builds upon liquid crystal displays (LCDs) to backscatter ambient light, but it provides a unique and novel feature: our platform utilizes frequency signals to modulate ambient light. Compared to the state-of-the-art, which rely on either pulse- or color-based modulation, our approach allows us to provide simultaneously: a simple and energy-efficient platform (no cameras), flicker-free communication (safe), and the ability to work reliably in spite of the interference and light fluctuations caused by uncontrollable light sources (reliable). We test our platform in indoor and outdoor environments, and show that an LCD surface of 6×8 cm can transmit 80 bps at ranges between 4 meters (indoors) and 60 meters (outdoors), consuming a fraction of the energy required by comparable systems using cameras.","PeriodicalId":432452,"journal":{"name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123936240","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: 20

Vehicular mobility modeling based on heterogeneous sensor networks: poster abstract 基于异构传感器网络的车辆移动性建模:海报摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361964

Yu Yang, Fan Zhang, Desheng Zhang

{"title":"Vehicular mobility modeling based on heterogeneous sensor networks: poster abstract","authors":"Yu Yang, Fan Zhang, Desheng Zhang","doi":"10.1145/3356250.3361964","DOIUrl":"https://doi.org/10.1145/3356250.3361964","url":null,"abstract":"Urban mobility especially vehicular mobility is important for many real-world applications. A lot of work has been done based on data from mobile sensor networks and stationary sensor networks, where each of them has its only weakness in spatiotemporal coverage and penetration rates. In this paper, we aim to coordinate the strength of heterogeneous sensor networks to achieve mobility modeling. Specifically, we design a mobility prediction system called Mohen to predict the destination of all the vehicles after they leave the coverage of stationary sensors with (i) only small portion vehicles with mobile sensors (i.e., GPS devices); (ii) historical data from stationary sensors (i.e., toll stations). The key novelty of Mohen is that we utilize the complementary features from heterogeneous sensor networks that mobile sensor networks provide better spatiotemporal coverage whereas stationary sensor networks capture vehicles with high penetration rates. We implement and evaluate Mohen in Guangdong Province, China by two real-world datasets, an electric toll collection (ETC) data set of 2 million vehicles, and an insurance-based vehicular tracking system of 114 thousand vehicles and show the accuracy of 75%.","PeriodicalId":432452,"journal":{"name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","volume":"331 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115971998","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

A swarm of crop spraying drones solution for optimising safe pesticide usage in arable lands: poster abstract 一群农作物喷洒无人机解决方案，优化耕地安全使用农药:海报摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361948

A. Amarasinghe, V. B. Wijesuriya, Dilshan Ganepola, L. Jayaratne

引用次数: 4

SenseHAR SenseHAR

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3360032

J. Jeyakumar, Liangzhen Lai, Naveen Suda, M. Srivastava

{"title":"SenseHAR","authors":"J. Jeyakumar, Liangzhen Lai, Naveen Suda, M. Srivastava","doi":"10.1145/3356250.3360032","DOIUrl":"https://doi.org/10.1145/3356250.3360032","url":null,"abstract":"Modern smartphones and smartwatches are equipped with inertial sensors (accelerometer, gyroscope, and magnetometer) that can be used for Human Activity Recognition (HAR) to infer tasks such as daily activities, transportation modes and, gestures. HAR requires collecting raw inertial sensor values and training a machine learning model on the collected data. The challenge in this approach is that the models are trained for specific devices and device configurations whereas, in reality, the set of devices carried by a person may vary over time. Ideally, one would like activity inferencing to be robust of this variation and provide accurate predictions by making opportunistic use of information from available devices. Moreover, the devices may be located at different parts of the body (e.g. pocket, left and right wrist), may have different sets of sensors (e.g. a smartwatch may not have gyroscope while a smartphone might), and may differ in sampling frequencies. In this paper, we provide a solution which makes use of the information from available devices while being robust to their variations. Instead of training an end-to-end model for every permutation of device combinations and configurations, we propose a scalable deep learning based solution in which each device learns its own sensor fusion model that maps the raw sensor values to a shared low dimensional latent space which we call the 'SenseHAR'-a virtual activity sensor. The virtual sensor has the same format and similar behavior regardless of the subset of devices, sensor's availability, sampling rate, or a device's location. This would help machine learning engineers to develop their application specific (e.g., from gesture recognition to activities of daily life) models in a hardware-agnostic manner based on this virtual activity sensor. Our evaluations show that an application model trained on SenseHAR achieves the state of the art accuracies of 95.32%, 74.22% and 93.13% on PAMAP2, Opportunity(gestures) and our collected datasets respectively.","PeriodicalId":432452,"journal":{"name":"Proceedings of the 17th Conference on Embedded Networked Sensor Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131667991","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: 40

E-Eye: mmWave nonlinear response for hidden electronic device recognition: demo abstract 电子眼:用于隐藏电子设备识别的毫米波非线性响应:演示摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361932

Baicheng Chen, Zhengxiong Li, Zhuolin Yang, Changzhi Li, Feng Lin, Wenyao Xu

引用次数: 0

Two-phase dissemination scheme for CoAP-based firmware-over-the-air update of wireless sensor networks: demo abstract 基于coap的无线传感器网络固件空中更新的两阶段传播方案:演示摘要

Proceedings of the 17th Conference on Embedded Networked Sensor Systems Pub Date : 2019-11-10 DOI: 10.1145/3356250.3361937

Huiung Park, Haeyong Kim, Seon-Tae Kim, P. Mah, Chaedeok Lim

引用次数: 1