2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)最新文献

{"title":"Articulation Motion Sensing for Pronunciation Training","authors":"Aslan B. Wong, Xia Chen, Qianru Liao, Kaishun Wu","doi":"10.1109/SECON52354.2021.9491610","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491610","url":null,"abstract":"The vowel is deemed the essence of the syllable in which controls the articulation of each word uttered. However, articulation sensing has not been adequately evaluated. The challenging task is that the speech signal contains insufficient information for articulation analysis. We propose a new approach to identify the articulation of monophthongs in multiple languages. We employ simultaneously two ranges of acoustic signals, both speech and ultrasonic signal, to recognize lip shape and tongue position, which is implemented into an off-the-shelf smartphone to be more accessible. The articulation recognition accuracy is 94.74%. The proposed system also applies to an alternative model for a pronunciation training system that gives articulation feedback to a user.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"68 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132578445","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":"Software-Defined Visible Light Networking for Bi-Directional Wireless Communication Across the Air-Water Interface","authors":"Kerem Enhos, Emrecan Demirors, Deniz Ünal, T. Melodia","doi":"10.1109/SECON52354.2021.9491583","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491583","url":null,"abstract":"Autonomous networks of sensors, unmanned aerial vehicles (UAVs) and unmanned underwater vehicles (UUVs) will play a vital role in scenarios/applications where a plethora of distributed assets across multiple domains – air and water - operate in unison to accomplish a common goal. However, establishing high data rate, robust, and bi-directional communication links across the air-water interface between aerial and underwater assets is still an open problem. In this article, we propose a communication system based on visible (blue) light that enables aerial and underwater assets to establish bi-directional links through the air-water interface without requiring any preexisting communication infrastructure such as buoys acting as relay nodes. We first derive a mathematical model and accordingly build a simulator for the bi-directional air-water visible light communication (VLC) channel accounting for water surface distribution, optical parameters and path losses. Then, we design and prototype a software-defined visible light communication (VLC) modem. We present an extensive experimental evaluation conducted both in a test tank and in the ocean using the proposed VLC modem prototypes.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"85 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125924586","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":"Cut, Distil and Encode (CDE): Split Cloud-Edge Deep Inference","authors":"Marion Sbai, Muhamad Risqi U. Saputra, Niki Trigoni, A. Markham","doi":"10.1109/SECON52354.2021.9491600","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491600","url":null,"abstract":"In cloud-edge environments, running all Deep Neural Network (DNN) models on the cloud causes significant network congestion and high latency, whereas the exclusive use of the edge device for execution limits the size and structure of the DNN, impacting accuracy. This paper introduces a novel partitioning approach for DNN inference between the edge and the cloud. This is the first work to consider simultaneous optimization of both the memory usage at the edge and the size of the data to be transferred over the wireless link. The experiments were performed on two different network architectures, MobileNetV1 and VGG16. The proposed approach makes it possible to execute part of the network on very constrained devices (e.g., microcontrollers), and under poor network conditions (e.g., LoRa) whilst retaining reasonable accuracies. Moreover, the results show that the choice of the optimal layer to split the network depends on the bandwidth and memory constraints, whereas prior work suggests that the best choice is always to split the network at higher layers. We demonstrate superior performance compared to existing techniques.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123647808","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":"DAVE: Dynamic Adaptive Video Encoding for Real-time Video Streaming Applications","authors":"Siqi Huang, Jiang Xie","doi":"10.1109/SECON52354.2021.9491588","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491588","url":null,"abstract":"Real-time video streaming applications have become tremendously popular in recent years, such as remote control and video conferencing applications. A key characteristic that differentiates these applications from traditional live streaming applications is that these applications have a very low-latency requirement for interactivity. The stricter low-latency requirement brings many challenges: the video has to be encoded in a real-time manner; the substantial resources on the server or cloud cannot be utilized for encoding; and the adaptation strategies in live streaming applications are not adequate for real-time video streaming, such as adaptive bitrate selection (ABR). In addition, the video perceptual quality of current real-time video streaming systems is usually sacrificed to meet the very low-latency requirement.To address these challenges, in this paper, a new real-time video streaming protocol, DAVE (Dynamic Adaptive Video Encoding for real-time video streaming applications), is proposed. In the proposed real-time video streaming system, captured video frames are encoded with different configurations. Since the video encoding configuration determines the video data size, quality, and encoding time, we first conduct an experimental study on the impact of each configuration parameter. Based on our experimental findings, we then propose a super resolution based video encoding configuration selection algorithm which does not use a fixed strategy to determine the encoding configurations as in existing real-time video streaming systems but uses a reinforcement learning based model to learn the optimal video encoding configuration that includes the configuration of both regular video encoding parameters and the up-scale of super resolution models. As a result, DAVE can optimize the performance of real-time video streaming systems based on user Quality of Experience (QoE) metrics. To the best of our knowledge, this is the first work that incorporates super resolution and reinforcement learning in the protocol design for real-time video streaming systems. Extensive evaluations show that DAVE can substantially improve the video perceptual quality by 15% and can also reduce the end-to-end latency by 20%, as compared with existing systems1.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115465108","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":"MODELESS: MODulation rEcognition with LimitEd SuperviSion","authors":"Wei Xiong, Petko Bogdanov, M. Zheleva","doi":"10.1109/SECON52354.2021.9491617","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491617","url":null,"abstract":"Modulation recognition (modrec) is an essential transmitter fingerprinting task that enables future spectrum-sharing applications such as access management and enforcement. Traditional supervised modrec requires labeled training data for all target modulations, which cannot be readily met with the advent of new, customized and data-driven waveforms. Thus, a keystone question for the applicability of modrec is: Can we perform automatic recognition of previously unobserved modulations by adapting and reusing models that were trained on different but related modulations?To this end, we develop MODELESS (MODulation rEcognition with LimitEd SuperviSion) that exploits knowledge from observed modulations to classify samples from unobserved ones. Our solution is grounded in zero-shot transfer learning, which employs side information among observed and unobserved classes to transfer learned classifiers. In particular we quantify the similarity among the theoretical constellation diagrams of unobserved and observed modulations and employ them in a zero-shot transfer learning framework. Our framework is general, as it can produce predictions for arbitrary modulations as long as their theoretical constellations can be specified. We evaluate MODELESS on synthetic and real-world traces and in comparison with zero-shot counterparts from the literature. We demonstrate near-ideal classification accuracy in the majority of the testing cases and draw recommendations for future research into classification tasks with sub-par performance.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130834645","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":"VibranSee: Enabling Simultaneous Visible Light Communication and Sensing","authors":"Ila Gokarn, Archan Misra","doi":"10.1109/SECON52354.2021.9491608","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491608","url":null,"abstract":"Driven by the ubiquitous proliferation of low-cost LED luminaires, visible light communication (VLC) has been established as a high-speed communications technology based on the high-frequency modulation of an optical source. In parallel, Visible Light Sensing (VLS) has recently demonstrated how vision-based at-a-distance sensing of mechanical vibrations (e.g., of factory equipment) can be performed using high frequency optical strobing. However, to date, exemplars of VLC and VLS have been explored in isolation, without consideration of their mutual dependencies. In this work, we explore whether and how high-throughput VLC and high-coverage VLS can be simultaneously supported. We first demonstrate the existence of a fundamental VLC-vs.-VLS tradeoff, driven by the duty cycle of the strobing light source: a larger duty cycle results in higher VLC throughput but reduced VLS coverage, and vice versa. To overcome this limitation, we evaluate two approaches: (a) time-multiplexed VLC and VLS on a single strobe, and (b) harmonic multi-strobing, where multiple light sources are strobed synchronously to effectively create low-duty cycle harmonics of the base strobe frequency. Finally, we present VibranSee, an approach that improves harmonic multi-strobing by adaptively tuning both (a) the strobe duty cycle and (b) the number of strobing harmonics used. Using both analytical studies and prototype-based experiments, we show VibranSee’s benefits: it simultaneously achieves VLC data goodput that is ideally only 18.6% lower (and 23.9% lower for an actual working prototype) than the maximum communication rate and infers over 96.6% (100% for the prototype) of possible vibration frequencies.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116653060","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":"Robust RFID-Based Multi-Object Identification and Tracking with Visual Aids","authors":"Junjie Yin, Sicong Liao, Chunhui Duan, Xuan Ding, Zheng Yang, Zuwei Yin","doi":"10.1109/SECON52354.2021.9491612","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491612","url":null,"abstract":"Obtaining fine-grained spatial information is of practical importance in RFID-based applications. However, high-precision positioning remains a challenging task in commercial-off-the-shelf (COTS) RFID systems. Inspired by progress in the computer vision (CV) field, researchers propose to combine CV with RFID systems and turn the positioning problem into a matching problem. Promising though it seems, current methods fuse CV and RFID through converting traces of tagged objects extracted from videos by CV into phase sequences for matching, which is a dimension-reduced procedure causing loss of spatial resolution. Consequently, they fail in more harsh conditions such as small tag intervals and low reading rates of tags. To address the limitation, we propose TagFocus, a more robust RFID-enabled system for fine-grained multi-object identification and tracking with visual aids. The key observation of TagFocus is that traces generated by different methods shall be compatible if they are acquired from one identical object. Leveraging this observation, an attention-based sequence-to-sequence (seq2seq) model is trained to generate a simulated trace for each candidate tag-object pair. And the trace of the right pair shall best match the observed trace directly extracted by CV. A prototype of TagFocus is implemented and extensively assessed in lab environments. Experimental results show that our system maintains a matching accuracy of over 89% in harsh conditions, outperforming state-of-the-art schemes by 25%.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133572539","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":"Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning","authors":"Markus-Philipp Gherman, Yun Cheng, Andres Gomez, O. Saukh","doi":"10.1109/SECON52354.2021.9491586","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491586","url":null,"abstract":"Popular low-cost air quality sensors embedded into IoT and mobile devices are based on metal oxides (MOX) that change their electrical resistance in response to ambient pollutants emitted as gases. Operating MOX sensors continuously is expensive, since it requires to heat up and maintain a hotplate at several hundred degrees. To save energy, sensors are commonly duty cycled with short on-times and long off-times. However, doing so adversely affects the sensor’s chemical reactions, which have slower transients as the off-time increases. As a result, sensor sensitivity to various gases deviates from a continuously powered sensor. In this paper, we show that it is possible to recover accurate continuous-sensor measurements from transient responses obtained from a duty cycled sensor and compensate for an altered multi-gas cross-sensitivity profile using machine learning methods. On a test set, we achieve a mean absolute error (MAE) of 24ppb between continuous ground-truth measurements and obtained model predictions of tVOC. This results in estimating 86.6% of Indoor Air Quality (IAQ) levels correctly compared to 68.1% if no correction is used. Our models are invariant to minor baseline shifts and work for both tVOC and CO2-eq signals provided by the sensor. Thanks to our models, 98.5% of the energy consumption can be reduced while maintaining high accuracy. This optimization enables energy-harvesting-based operation of IAQ sensors in indoor IoT scenarios.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132664164","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":"SECON 2021 Organizing Committee","authors":"","doi":"10.1109/secon52354.2021.9491625","DOIUrl":"https://doi.org/10.1109/secon52354.2021.9491625","url":null,"abstract":"","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"58 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115763010","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":"[SECON 2021 Copyright notice]","authors":"","doi":"10.1109/secon52354.2021.9491613","DOIUrl":"https://doi.org/10.1109/secon52354.2021.9491613","url":null,"abstract":"","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116034579","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}