2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)最新文献_第2页

CORE: Connectivity Optimization via REinforcement Learning in WANETs 核心:通过wanet中的强化学习进行连接优化

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491597

A. Gorovits, Karyn Doke, Lin Zhang, M. Zheleva, Petko Bogdanov

{"title":"CORE: Connectivity Optimization via REinforcement Learning in WANETs","authors":"A. Gorovits, Karyn Doke, Lin Zhang, M. Zheleva, Petko Bogdanov","doi":"10.1109/SECON52354.2021.9491597","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491597","url":null,"abstract":"While mobile devices are ubiquitous, their supporting communication infrastructure is cost-effective only in densely populated urban areas and is often lacking in rural settings. This lack of connectivity leads to lost opportunities in applications such as rural emergency preparedness and response. Peer-to-peer exchange that uses predictable human mobility can enable delay-tolerant information access in rural settings. We propose, an adaptive distributed solution for device-to-device Connectivity Optimization via REinforcement Learning (CORE) in wireless adhoc networks. Our solution is designed for collaborative distributed agents with intermittent connectivity and limited battery power, but predictable mobility within short temporal horizons. We seek to maximize the utility of connection attempts while keeping the power expenditure within a predefined battery budget. Agents learn to adaptively make automated decisions for when to attempt connections and exchange information, based on a local RL model of their mobility and that of other agents they learn about from exchanges. Using both synthetic and real-world mobility traces, we demonstrate that agents are able to materialize 95% of the possible connections using 20% of their battery and successfully adapting to changes in the underlying mobility patterns within several days of learning.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"6 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":"126515317","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

MagThief: Stealing Private App Usage Data on Mobile Devices via Built-in Magnetometer MagThief:通过内置磁力计窃取移动设备上的私人应用程序使用数据

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491601

Hao Pan, Lanqing Yang, Honglu Li, Chuang-Wen You, Xiaoyu Ji, Yi-Chao Chen, Zhenxian Hu, Guangtao Xue

{"title":"MagThief: Stealing Private App Usage Data on Mobile Devices via Built-in Magnetometer","authors":"Hao Pan, Lanqing Yang, Honglu Li, Chuang-Wen You, Xiaoyu Ji, Yi-Chao Chen, Zhenxian Hu, Guangtao Xue","doi":"10.1109/SECON52354.2021.9491601","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491601","url":null,"abstract":"Various characteristics of mobile applications (apps) and associated in-app services have been used reveal potentially-sensitive user information; however, privacy concerns have prompted third-party apps to rigorously restrict access to data related to mobile app usage. This paper outlines a novel approach to the extraction of detailed app usage information based on analysis of the electromagnetic (EM) signals emitted from mobile devices when executing app-related tasks. Note that this type of EM leakage becomes high-complex when multiple apps are used simultaneously and is subject to interference from geomagnetic signals generated by device movement. This paper proposes a deep learning-based multi-label classification system to identify apps and in-app services based on magnetometer readings. The proposed MAGTHIEF system uses accelerometer and gyroscope data to cancel out the offset in geomagnetic signals followed by an elaborate deep region convolution neural network (DRCNN) to differentiate among multiple apps and the corresponding inapp services. Experiments on 50 apps demonstrated the efficacy of MAGTHIEF in identifying multiple apps and in-app services, achieving high average macro F1 scores of 0.87 and 0.95, respectively. MAGTHIEF also achieved time duration accuracy of 89.5% in recognizing app trajectory in the real-world scene.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"166 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114106437","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: 7

SwarmShare: Mobility-Resilient Spectrum Sharing for Swarm UAV Networking in the 6 GHz Band SwarmShare: 6ghz频段蜂群无人机网络的移动弹性频谱共享

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491602

Jiangqi Hu, Sabarish Krishna Moorthy, Ankush Harindranath, Zhangyu Guan, Nicholas Mastronarde, E. Bentley, Scott M. Pudlewski

{"title":"SwarmShare: Mobility-Resilient Spectrum Sharing for Swarm UAV Networking in the 6 GHz Band","authors":"Jiangqi Hu, Sabarish Krishna Moorthy, Ankush Harindranath, Zhangyu Guan, Nicholas Mastronarde, E. Bentley, Scott M. Pudlewski","doi":"10.1109/SECON52354.2021.9491602","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491602","url":null,"abstract":"To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively controlled below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"34 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":"116667524","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: 4

WiBle: Physical-Layer Cross-Technology Communication with Symbol Transition Mapping 使用符号转换映射的物理层跨技术通信

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491581

Lingang Li, Yongrui Chen, Zhijun Li

{"title":"WiBle: Physical-Layer Cross-Technology Communication with Symbol Transition Mapping","authors":"Lingang Li, Yongrui Chen, Zhijun Li","doi":"10.1109/SECON52354.2021.9491581","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491581","url":null,"abstract":"Recent advances on Physical-layer Cross-Technology Communication (PHY-CTC) have achieved high throughput direct communication across different wireless technologies. These PHY-CTC works are commonly achieved by emulating the target signal waveform of the receiver. However, signal emulation suffers from inherent unreliability due to imperfect emulation, and it only supports few communication channels. When applied in WiFi to Bluetooth Low Energy (BLE) scenario, it will face two challenges: i) a BLE receiver can not tolerate any bit error in a frame, while emulation errors are easy to appear; and ii) the BLE device performs channel hopping while most BLE channels are unavailable for emulation based CTC.To address these challenges, we present WiBle, a high reliable and all-channel supporting PHY-CTC from WiFi to BLE. The key technical insight of WiBle is symbol transition mapping: When a symbol is transmitted by a WiFi sender and flows into a BLE receiver, it will leave some unique signatures which can be leveraged to extract information. More specifically, it is observed that the phase shifts of BLE received signal can be mapped to the transitions of WiFi symbols. Therefore, by carefully selecting the symbols at the WiFi sender, we can generate the desired phase shifts for correct BLE GFSK demodulation and achieve reliable CTC. Evaluation results on both USRP and commodity chip show that WiBle outperforms state-of-the-art CTCs by higher reliability (> 95% frame reception ratio), wider channel coverage (supporting all 40 BLE channels), and higher throughput (974.3Kbps), under a full range of configurations including indoor/outdoor and LoS/NLoS settings.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"39 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":"123847136","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

An RL Approach for Radio Resource Management in the O-RAN Architecture O-RAN架构下无线电资源管理的RL方法

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491579

Federico Mungari

引用次数: 7

VarLenMARL: A Framework of Variable-Length Time-Step Multi-Agent Reinforcement Learning for Cooperative Charging in Sensor Networks 基于变长时间步长多智能体强化学习的传感器网络协同充电框架

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491594

Yuxin Chen, He Wu, Yongheng Liang, G. Lai

{"title":"VarLenMARL: A Framework of Variable-Length Time-Step Multi-Agent Reinforcement Learning for Cooperative Charging in Sensor Networks","authors":"Yuxin Chen, He Wu, Yongheng Liang, G. Lai","doi":"10.1109/SECON52354.2021.9491594","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491594","url":null,"abstract":"This paper studies cooperative charging, in which multiple mobile chargers cooperatively provide wireless charging services in a Wireless Rechargeable Sensor Network (WRSN). The ultimate goal of this cooperative charging is the long-term optimization that maximizes both the lifetime of all sensor nodes and the charging utility of each Mobile Charger (MC). We have attempted to apply Multi-Agent Reinforcement Learning (MARL) algorithms to this problem. Unfortunately, similar to existing methods, MARL algorithms also fail early in cooperative charging. We found that an MARL algorithm trained in each time-step of fixed length is neither accurate nor efficient in cooperative charging. We propose a new MARL framework, called VarLenMARL. For the accuracy of reward estimation, VarLenMARL allows each MC completes an action within a time-step of variable length before estimating rewards. Furthermore, we design a special mechanism in VarLenMARL for the long-term optimality of cooperative charging within a WRSN. Our results show that algorithms implemented on VarLenMARL achieved both higher charging utility of MCs and longer lifetime of sensor nodes.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"18 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":"133253391","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: 3

mmFlow: Facilitating At-Home Spirometry with 5G Smart Devices mmFlow:通过5G智能设备促进家庭肺活量测定

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491616

Aakriti Adhikari, A. Hetherington, Sanjib Sur

{"title":"mmFlow: Facilitating At-Home Spirometry with 5G Smart Devices","authors":"Aakriti Adhikari, A. Hetherington, Sanjib Sur","doi":"10.1109/SECON52354.2021.9491616","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491616","url":null,"abstract":"Respiratory diseases, like Asthma, COPD, have been a significant public health challenge over decades. Portable spirometers are effective in continuous monitoring of respiratory syndromes out-of-clinic. However, existing systems are either costly or provide limited information and require extra hardware. In this paper, we present mmFlow, a low-barrier means to perform at-home spirometry tests using 5G smart devices. mmFlow works like regular spirometers, where a user forcibly exhales onto a device; but instead of relying on special-purpose hardware, mmFlow leverages built-in millimeter-wave technology in general-purpose, ubiquitous mobile devices. mmFlow analyzes the tiny vibrations created by the airflow on the device surface and combines wireless signal processing with deep learning to enable a software-only spirometry solution. From empirical evaluations, we find that, when device distance is fixed, mmFlow can predict the spirometry indicators with performance comparable to inclinic spirometers with <5% prediction errors. Besides, mmFlow generalizes well under different environments and human conditions, making it promising for out-of-clinic daily monitoring.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"40 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":"133392770","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: 4

Collecting High-Dimensional and Correlation-Constrained Data with Local Differential Privacy 基于局部差分隐私的高维关联约束数据采集

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491591

Rong Du, Qingqing Ye, Yue Fu, Haibo Hu

{"title":"Collecting High-Dimensional and Correlation-Constrained Data with Local Differential Privacy","authors":"Rong Du, Qingqing Ye, Yue Fu, Haibo Hu","doi":"10.1109/SECON52354.2021.9491591","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491591","url":null,"abstract":"Local differential privacy (LDP) is a promising privacy model for distributed data collection. It has been widely deployed in real-world systems (e.g. Chrome, iOS, macOS). In LDP-based mechanisms, an aggregator collects private values perturbed by each user and then analyses these values to estimate their statistics, such as frequency and mean. Most existing works focus on simple scalar value types, such as boolean and categorical values. However, with the emergence of smart sensors and internet of things, high-dimensional data are gaining increasing popularity. In many cases, correlations exist between various attributes of such data, e.g. temperature and luminance. To ensure LDP for high-dimensional data, existing solutions either partition the privacy budget ϵ among these correlated attributes or adopt sampling, both of which dilute the density of useful information and thus result in poor data utility.In this paper, we propose a relaxed LDP model, namely, univariate dominance local differential privacy (UDLDP), for high-dimensional data. We quantify the correlations between attributes and present a correlation-bounded perturbation (CBP) mechanism that optimizes the partitioning of privacy budget on each correlated attribute. Furthermore, we extend CBP to support sampling, which is a common bandwidth reduction technique in sensor networks and Internet of Things. We derive the best allocation strategy of sampling probabilities among attributes in terms of data utility, which leads to the correlation-bounded perturbation mechanism with sampling (CBPS). The performance of both mechanisms is evaluated and compared with state-of-the-art LDP mechanisms on real-world and synthetic datasets.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"140 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":"114656591","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: 12

Deploying Hybrid Network Services: Mixing VNFs and CNFs in Multi-site Infrastructures 部署混合网络服务:在多站点基础设施中混合VNFs和CNFs

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491619

J. Baranda, J. Mangues‐Bafalluy, L. Vettori, R. Martínez, E. Zeydan

引用次数: 2

UD-MIMO: Uplink Distributed MIMO for Wireless LANs UD-MIMO:用于无线局域网的上行分布式MIMO

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491622

Hossein Pirayesh, Pedram Kheirkhah Sangdeh, Qiben Yan, Huacheng Zeng

{"title":"UD-MIMO: Uplink Distributed MIMO for Wireless LANs","authors":"Hossein Pirayesh, Pedram Kheirkhah Sangdeh, Qiben Yan, Huacheng Zeng","doi":"10.1109/SECON52354.2021.9491622","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491622","url":null,"abstract":"Wireless local area networks (WLANs) are a key component of the telecommunications infrastructure in our society. While many solutions have been produced to improve their downlink throughput, the techniques for enhancing their uplink throughput remain limited. The stagnation can be attributed to the lack of fine-grained inter-node synchronization due to the hardware limitation of most devices. In this paper, we present an uplink distributed multiple-input-and-multiple-output scheme (termed UD-MIMO) for WLANs to enable concurrent uplink transmission in the absence of fine-grained inter-node synchronization. The enabling technique behind UD-MIMO is a practical solution to decoding uplink packets from asynchronous users. UD-MIMO makes it possible for WLANs to significantly improve their uplink throughput while not requiring tight internode synchronization. We have built a prototype of UD-MIMO on a wireless testbed and demonstrate its compatibility with commercial off-the-shelf Atheros 802.11 client devices (with modified Linux driver). Our experimental results show that, for a WLAN with 8 APs in a conference room, UD-MIMO offers 3.4× throughput compared to interference-avoidance approach.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"5 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":"114906360","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