IEEE INFOCOM 2023 - IEEE Conference on Computer Communications最新文献_第2页

Message from TPC Chairs 来自TPC主席的信息

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/infocom53939.2023.10228942

引用次数: 0

A Decentralized Truth Discovery Approach to the Blockchain Oracle Problem b区块链Oracle问题的去中心化真相发现方法

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10229019

Yang Xiao, Ning Zhang, W. Lou, Y. T. Hou

{"title":"A Decentralized Truth Discovery Approach to the Blockchain Oracle Problem","authors":"Yang Xiao, Ning Zhang, W. Lou, Y. T. Hou","doi":"10.1109/INFOCOM53939.2023.10229019","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10229019","url":null,"abstract":"When a blockchain application runs on data from the real world, it relies on an oracle mechanism that transports data from external sources to the blockchain. The blockchain oracle problem arises around the need to procure trustworthy data from external sources. Previous works have addressed data authenticity/integrity by building a secure channel between blockchain and external sources while employing a decentralized oracle network to avoid a single point of failure. However, the truthful data challenge, which emerges when legitimate external sources submit fraudulent or deceitful data, remains unsolved. In this paper, we introduce a new decentralized truth-discovering oracle architecture called DecenTruth to address the truthful data challenge using a data-centric approach. DecenTruth aims to elevate the \"truthfulness\" of external data input by enabling decentralized oracle nodes to discover and reach consensus on truthful values of common data objects from multi-sourced inputs in an off-chain manner. It harmonizes techniques in both the data plane and consensus plane—truth discovery (TD) and asynchronous BFT consensus—and enables nodes to finalize the same estimated truths on data objects with high accuracy, amid the harsh asynchronous network condition and presence of Byzantine sources and nodes. We implemented DecenTruth and evaluated its performance in a simulated oracle service scenario. The results demonstrate significantly higher Byzantine resilience and long-term data feed accuracy of DecenTruth, compared to existing median-based aggregation methods.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121594865","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

Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning 基于深度强化学习的动态无状态组播机制

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10228869

Lie Lu, Qing Li, Dan Zhao, Yuan Yang, Zeyu Luan, Jianer Zhou, Yong Jiang, Mingwei Xu

{"title":"Hawkeye: A Dynamic and Stateless Multicast Mechanism with Deep Reinforcement Learning","authors":"Lie Lu, Qing Li, Dan Zhao, Yuan Yang, Zeyu Luan, Jianer Zhou, Yong Jiang, Mingwei Xu","doi":"10.1109/INFOCOM53939.2023.10228869","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228869","url":null,"abstract":"Multicast traffic is growing rapidly due to the development of multimedia streaming. Lately, stateless multicast protocols, such as BIER, have been proposed to solve the excessive routing states problem of traditional multicast protocols. However, the high complexity of multicast tree computation and the limited scalability for concurrent requests still pose daunting challenges, especially under dynamic group membership. In this paper, we propose Hawkeye, a dynamic and stateless multicast mechanism with deep reinforcement learning (DRL) approach. For real-time responses to multicast requests, we leverage DRL enhanced by a temporal convolutional network (TCN) to model the sequential feature of dynamic group membership and thus is able to build multicast trees proactively for upcoming requests. Moreover, an innovative source aggregation mechanism is designed to help the DRL agent converge when faced with a large amount of multicast requests, and relieve ingress routers from excessive routing states. Evaluation with real-world topologies and multicast requests demonstrates that Hawkeye adapts well to dynamic multicast: it reduces the variation of path latency by up to 89.5% with less than 12% additional bandwidth consumption compared with the theoretical optimum.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130118802","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

Realizing Uplink MU-MIMO Communication in mmWave WLANs: Bayesian Optimization and Asynchronous Transmission 毫米波无线局域网中上行MU-MIMO通信的实现:贝叶斯优化和异步传输

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10228888

Shichen Zhang, Bo Ji, K. Zeng, Huacheng Zeng

引用次数: 0

Owl: A Pre-and Post-processing Framework for Video Analytics in Low-light Surroundings Owl:低光环境下视频分析的预处理和后处理框架

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10229059

Ruixiao Zhang, Chaoyang Li, Chen Wu, Tianchi Huang, Lifeng Sun

{"title":"Owl: A Pre-and Post-processing Framework for Video Analytics in Low-light Surroundings","authors":"Ruixiao Zhang, Chaoyang Li, Chen Wu, Tianchi Huang, Lifeng Sun","doi":"10.1109/INFOCOM53939.2023.10229059","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10229059","url":null,"abstract":"The low-light environment is an integral surrounding in real-world video analytic applications. Conventional wisdom claims that in order to adapt to the extensive computation requirement of the analytics model and achieve high inference accuracy, the overall pipeline should leverage a client-to-cloud framework that designs a cloud-based inference with on-demand video streaming. However, we show that due to the amplified noise, directly streaming the video in low-light scenarios can introduce significant bandwidth inefficiency.In this paper, we propose Owl, an intelligent framework to optimize the bandwidth utilization and inference accuracy for the low-light video analytic pipeline. The core idea of Owl is two-fold: on the one hand, we will deploy a light-weighted pre-processing module before transmission, through which we will get the denoised video and significantly reduce the transmitted data; on the other hand, we recover the information from the denoised video via an enhancement module in the server-side. Specifically, through well-designed training mechanism and content representation technique, Owl can dynamically select the best configuration for time-varying videos. Experiments with a variety of datasets and tasks show that Owl achieves significant bandwidth benefits, while consistently optimizing the inference accuracy.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128942421","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

Contrastive learning with self-reconstruction for channel-resilient modulation classification 信道弹性调制分类的自重构对比学习

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10228908

Erma Perenda, Sreeraj Rajendran, Gérôme Bovet, M. Zheleva, S. Pollin

{"title":"Contrastive learning with self-reconstruction for channel-resilient modulation classification","authors":"Erma Perenda, Sreeraj Rajendran, Gérôme Bovet, M. Zheleva, S. Pollin","doi":"10.1109/INFOCOM53939.2023.10228908","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228908","url":null,"abstract":"Despite the substantial success of deep learning for Automatic Modulation Classification (AMC), models trained on a specific transmitter configuration and channel model often fail to generalize well to other scenarios with different transmitter configurations, wireless fading channels, or receiver impairments such as clock offset. This paper proposes Contrastive Learning with Self-Reconstruction called CLSR-AMC to learn good representations of signals resilient to channel changes. While contrastive loss focuses on the differences between individual modulations, the reconstruction loss captures representative features of the signal. Additionally, we develop three data augmentation operators to emulate the impact of channel and hardware impairments without exhaustive modeling of different channel profiles. We perform extensive experimentation with commonly used realistic datasets. We show that CLSR-AMC outperforms its counterpart based on contrastive learning for the same amount of labeled data by significant average accuracy gains of 24.29%, 17.01%, and 15.97% in the Additive White Gaussian Noise (AWGN), Rayleigh, and Rician channels, respectively.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130761729","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

TomoID: A Scalable Approach to Device Free Indoor Localization via RFID Tomography TomoID:一种可扩展的基于RFID断层扫描的室内定位方法

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10228938

Yang-Hsi Su, Jingliang Ren, Zi Qian, D. Fouhey, Alanson P. Sample

{"title":"TomoID: A Scalable Approach to Device Free Indoor Localization via RFID Tomography","authors":"Yang-Hsi Su, Jingliang Ren, Zi Qian, D. Fouhey, Alanson P. Sample","doi":"10.1109/INFOCOM53939.2023.10228938","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228938","url":null,"abstract":"Device-free localization methods allow users to benefit from location-aware services without the need to carry a transponder. However, conventional radio sensing approaches using active wireless devices require wired power or continual battery maintenance, limiting deployability. We present TomoID, a real-time multi-user UHF RFID tomographic localization system that uses low-level communication channel parameters such as RSSI, RF Phase, and Read Rate, to create probability heatmaps of users' locations. The heatmaps are passed to our custom-designed signal processing and machine learning pipeline to robustly predict users' locations. Results show that TomoID is highly accurate, with an average mean error of 17.1 cm for a stationary user and 18.9 cm when users are walking. With multiuser tracking, results showing an average mean error of <72 cm for five individuals in constant motion. Importantly, TomoID is specifically designed to work in real-world multipath-rich indoor environments. Our signal processing and machine learning pipeline allows a pre-trained localization model to be applied to new environments of different shapes and sizes, while maintaining good accuracy sufficient for indoor user localization and tracking. Ultimately, TomoID enables a scalable, easily deployable, and minimally intrusive method for locating uninstrumented users in indoor environments.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132452114","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

LigBee: Symbol-Level Cross-Technology Communication from LoRa to ZigBee LigBee:从LoRa到ZigBee的符号级跨技术通信

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10229005

Zhe Wang, L. Kong, Longfei Shangguan, Liang He, Kangjie Xu, Yifeng Cao, Hui Yu, Qiao Xiang, Jiadi Yu, Tengyu Ma, Zhuo Song, Zheng Liu, Guihai Chen

{"title":"LigBee: Symbol-Level Cross-Technology Communication from LoRa to ZigBee","authors":"Zhe Wang, L. Kong, Longfei Shangguan, Liang He, Kangjie Xu, Yifeng Cao, Hui Yu, Qiao Xiang, Jiadi Yu, Tengyu Ma, Zhuo Song, Zheng Liu, Guihai Chen","doi":"10.1109/INFOCOM53939.2023.10229005","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10229005","url":null,"abstract":"Low-power wide-area networks (LPWAN) evolve rapidly with advanced communication primitives (e.g., coding, modulation) being continuously invented. This rapid iteration on LPWAN, however, forms a communication barrier between legacy wireless sensor nodes deployed years ago (e.g., ZigBee-based sensor node) with their latest competitor running a different communication protocol (e.g., LoRa-based IoT node): they work on the same frequency band but share different MAC- and PHY-layer regulations and thus cannot talk to each other directly. To break this barrier, we propose LigBee, a cross-technology communication (CTC) solution that enables symbol-level communication from the latest LPWAN LoRa node to legacy ZIGBEE node. We have implemented LigBee on both software-defined radios and commercial-off-the-shelf (COTS) LoRa and ZigBee nodes, and demonstrated that LigBee builds a reliable CTC link from LoRa node to ZigBee node on both platforms. Our experimental results show that i) LigBee achieves a bit error rate (BER) in the order of 10−3 with 70 ∼ 80% frame reception ratio (FRR), ii) the range of LigBee link is over 300m, which is 6 ∼ 7.5× the typical range of legacy ZigBee and state-of-the-art solution, and iii) the throughput of LigBee link is maintained on the order of kbps, which is close to the LoRa’s throughput.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133175744","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

Copyright Page 版权页

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/infocom53939.2023.10228931

引用次数: 0

On Design and Performance of Offline Finding Network 离线搜索网络的设计与性能研究

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Pub Date : 2023-05-17 DOI: 10.1109/INFOCOM53939.2023.10228880

Tong Li, Jiaxin Liang, Yukuan Ding, Kai Zheng, Xu Zhang, Ke Xu

{"title":"On Design and Performance of Offline Finding Network","authors":"Tong Li, Jiaxin Liang, Yukuan Ding, Kai Zheng, Xu Zhang, Ke Xu","doi":"10.1109/INFOCOM53939.2023.10228880","DOIUrl":"https://doi.org/10.1109/INFOCOM53939.2023.10228880","url":null,"abstract":"Recently, such industrial pioneers as Apple and Samsung have offered a new generation of offline finding network (OFN) that enables crowd search for missing devices without leaking private data. Specifically, OFN leverages nearby online finder devices to conduct neighbor discovery via Bluetooth Low Energy (BLE), so as to detect the presence of offline missing devices and report an encrypted location back to the owner via the Internet. The user experience in OFN is closely related to the success ratio (possibility) of finding the lost device, where the latency of the prerequisite stage, i.e., neighbor discovery, matters. However, the crowd-sourced finder devices show diversity in scan modes due to different power modes or different manufacturers, resulting in local optima of neighbor discovery performance. In this paper, we present a brand-new broadcast mode called ElastiCast to deal with the scan mode diversity issues. ElastiCast captures the key features of BLE neighbor discovery and globally optimizes the broadcast mode interacting with diverse scan modes. Experimental evaluation results and commercial product deployment experience demonstrate that ElastiCast is effective in achieving stable and bounded neighbor discovery latency within the power budget.","PeriodicalId":387707,"journal":{"name":"IEEE INFOCOM 2023 - IEEE Conference on Computer Communications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114721964","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