Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems最新文献

{"title":"WiFi-Enabled Smart Human Dynamics Monitoring","authors":"Xiaonan Guo, Bo Liu, Cong Shi, Hongbo Liu, Yingying Chen, M. Chuah","doi":"10.1145/3131672.3131692","DOIUrl":"https://doi.org/10.1145/3131672.3131692","url":null,"abstract":"The rapid pace of urbanization and socioeconomic development encourage people to spend more time together and therefore monitoring of human dynamics is of great importance, especially for facilities of elder care and involving multiple activities. Traditional approaches are limited due to their high deployment costs and privacy concerns (e.g., camera-based surveillance or sensor-attachment-based solutions). In this work, we propose to provide a fine-grained comprehensive view of human dynamics using existing WiFi infrastructures often available in many indoor venues. Our approach is low-cost and device-free, which does not require any active human participation. Our system aims to provide smart human dynamics monitoring through participant number estimation, human density estimation and walking speed and direction derivation. A semi-supervised learning approach leveraging the non-linear regression model is developed to significantly reduce training efforts and accommodate different monitoring environments. We further derive participant number and density estimation based on the statistical distribution of Channel State Information (CSI) measurements. In addition, people's walking speed and direction are estimated by using a frequency-based mechanism. Extensive experiments over 12 months demonstrate that our system can perform fine-grained effective human dynamic monitoring with over 90% accuracy in estimating participants number, density, and walking speed and direction at various indoor environments.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114492983","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}

{"title":"Application-Layer Clock Synchronization for Wearables Using Skin Electric Potentials Induced by Powerline Radiation","authors":"Zhenyu Yan, Yang Li, Rui Tan, Jun Huang","doi":"10.1145/3131672.3131681","DOIUrl":"https://doi.org/10.1145/3131672.3131681","url":null,"abstract":"Design of clock synchronization for networked nodes faces a fundamental trade-off between synchronization accuracy and universality for heterogeneous platforms, because a high synchronization accuracy generally requires platform-dependent hardware-level network packet timestamping. This paper presents TouchSync, a new indoor clock synchronization approach for wearables that achieves millisecond accuracy while preserving universality in that it uses standard system calls only, such as reading system clock, sampling sensors, and sending/receiving network messages. The design of TouchSync is driven by a key finding from our extensive measurements that the skin electric potentials (SEPs) induced by powerline radiation are salient, periodic, and synchronous on a same wearer and even across different wearers. TouchSync integrates the SEP signal into the universal principle of Network Time Protocol and solves an integer ambiguity problem by fusing the ambiguous results in multiple synchronization rounds to conclude an accurate clock offset between two synchronizing wearables. With our shared code, TouchSync can be readily integrated into any wearable applications. Extensive evaluation based on our Arduino and TinyOS implementations shows that TouchSync's synchronization errors are below 3 and 7 milliseconds on the same wearer and between two wearers 10 kilometers apart, respectively.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133729818","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}

{"title":"OFDM Visible Light Communication using Off-the-shelf Video Camera","authors":"Shota Shimada, Takayuki Akiyama, H. Hashizume, M. Sugimoto","doi":"10.1145/3131672.3136968","DOIUrl":"https://doi.org/10.1145/3131672.3136968","url":null,"abstract":"This paper describes a rapid and flicker-free visible light communication technique that uses an off-the-shelf video camera. Transfer rates at least 53% faster than existing methods were achieved in experiments. Features of the proposed method are discussed via theoretical analysis.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125839039","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}

{"title":"DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework","authors":"Shuochao Yao, Yiran Zhao, Aston Zhang, Lu Su, T. Abdelzaher","doi":"10.1145/3131672.3131675","DOIUrl":"https://doi.org/10.1145/3131672.3131675","url":null,"abstract":"Recent advances in deep learning motivate the use of deep neutral networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an important impediment. A recently explored solution space lies in compressing (approximating or simplifying) deep neural networks in some manner before use on the device. We propose a new compression solution, called DeepIoT, that makes two key contributions in that space. First, unlike current solutions geared for compressing specific types of neural networks, DeepIoT presents a unified approach that compresses all commonly used deep learning structures for sensing applications, including fully-connected, convolutional, and recurrent neural networks, as well as their combinations. Second, unlike solutions that either sparsify weight matrices or assume linear structure within weight matrices, DeepIoT compresses neural network structures into smaller dense matrices by finding the minimum number of non-redundant hidden elements, such as filters and dimensions required by each layer, while keeping the performance of sensing applications the same. Importantly, it does so using an approach that obtains a global view of parameter redundancies, which is shown to produce superior compression. The compressed model generated by DeepIoT can directly use existing deep learning libraries that run on embedded and mobile systems without further modifications. We conduct experiments with five different sensing-related tasks on Intel Edison devices. DeepIoT outperforms all compared baseline algorithms with respect to execution time and energy consumption by a significant margin. It reduces the size of deep neural networks by 90% to 98.9%. It is thus able to shorten execution time by 71.4% to 94.5%, and decrease energy consumption by 72.2% to 95.7%. These improvements are achieved without loss of accuracy. The results underscore the potential of DeepIoT for advancing the exploitation of deep neural networks on resource-constrained embedded devices.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126218520","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}

{"title":"LoRea: A Backscatter Architecture that Achieves a Long Communication Range","authors":"Ambuj Varshney, C. Pérez-Penichet, C. Rohner, T. Voigt","doi":"10.1145/3131672.3136996","DOIUrl":"https://doi.org/10.1145/3131672.3136996","url":null,"abstract":"We present LOREA an architecture consisting of a backscatter tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing backscatter systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions. Second, by mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, by decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. LOREA's communication range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4km when the tag is located 1 m from the carrier source while consuming 70 μWs at the backscatter tag. We present various ultra-low power and long-range features of the LoRea architecture.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125704057","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}