2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)最新文献_第4页

A Reinforcement Learning based Decision-making System with Aggressive Driving Behavior Consideration for Autonomous Vehicles 一种考虑攻击性驾驶行为的自动驾驶汽车强化学习决策系统

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491587

Liuwang Kang, Haiying Shen

{"title":"A Reinforcement Learning based Decision-making System with Aggressive Driving Behavior Consideration for Autonomous Vehicles","authors":"Liuwang Kang, Haiying Shen","doi":"10.1109/SECON52354.2021.9491587","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491587","url":null,"abstract":"With the fast development of autonomous vehicle (AV) technology and possible popularity of AVs in the near future, a mixed-vehicle type driving environment where both AVs and their surrounding human-driving vehicles drive on the same road will exist and last for a long time. An AV measures its driving environments in real time and make control decisions to ensure driving safety. However, surrounding human-driving vehicles may conduct aggressive driving behaviors (e.g., sudden deceleration, sudden acceleration, sudden left or right lane change) in practice, which requires an AV to make correct control decisions to eliminate the effect of aggressive driving behaviors on its driving safety. In this paper, we propose a reinforcement learning based decision-making system (ReDS) which considers aggressive driving behaviors of surrounding human-driving vehicles during the decision making process. In ReDS, we firstly build a mixture density network based aggressive driving behavior detection method to detect possible aggressive driving behaviors among surrounding vehicles of an AV. We then build a reward function based on aggressive driving behavior detection results and incorporate the reward function into a reinforcement learning model to make optimal control decisions considering aggressive driving behaviors. We use a real-world traffic dataset from the United States Department of Transportation Federal Highway Administration to evaluate optimal control decision determination performance of ReDS in comparison with the state-of-the-art methods. The comparison results show that ReDS can improve optimal control decision success rate by 43% compared with existing methods, which demonstrates that ReDS has good optimal control decision determination performance.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"49 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":"116744376","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

Asynchronous Online Service Placement and Task Offloading for Mobile Edge Computing 移动边缘计算的异步在线服务放置和任务卸载

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491595

Xin Li, Xinglin Zhang, Tiansheng Huang

{"title":"Asynchronous Online Service Placement and Task Offloading for Mobile Edge Computing","authors":"Xin Li, Xinglin Zhang, Tiansheng Huang","doi":"10.1109/SECON52354.2021.9491595","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491595","url":null,"abstract":"Mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading schemes. Many existing works focus on offloading tasks to the edge servers while ignoring the heterogeneity and diversity of computation services, which is also important in MEC. In this paper, we investigate the joint problem of online task offloading and service placement—downloading and deploying the service-related resources at edge servers—in the dense MEC network. Our MEC system aims to maximize the long-term average network utility while maintaining the stability of the edge network. Due to the uncertainty of task demands, it is impossible to make an online long-term optimal decision. Therefore, we propose an online algorithm based on the two-timescale Lyapunov optimization without requiring the future information. By making asynchronous decisions on service placement and task offloading, we can achieve a time-average sub-optimal solution that is close to the offline optimum. In addition, rigorous theoretical analysis and extensive trace-driven experimental results show that the proposed algorithm is more competitive than benchmarks.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"30 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":"121114863","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

Deep Learning Video Analytics on Edge Computing Devices 边缘计算设备上的深度学习视频分析

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491614

Tianxiang Tan, G. Cao

{"title":"Deep Learning Video Analytics on Edge Computing Devices","authors":"Tianxiang Tan, G. Cao","doi":"10.1109/SECON52354.2021.9491614","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491614","url":null,"abstract":"The rapid progress of deep learning-based techniques such as Convolutional Neural Network (CNN) has enabled many emerging applications related to video analytics and running them on mobile devices can help improve our daily lives in many ways. However, there are many challenges for video analytics on mobile devices using multiple CNN models. CNN models are resource hungry, and each model requires a large amount of computational power and occupies a large portion of memory space. Although video processing can be offloaded to reduce the computation time, transmitting large amount of video data is time consuming. Thus, offloading is not always the best option. Moreover, different CNN models have different memory usage and processing time, making the scheduling problem more complex. As a result, besides deciding which task to be offloaded, we must decide which CNN model should reside in the memory and for how long, and which CNN model should be switched out due to memory constraint. In this paper, we propose resource aware scheduling algorithms to address these challenges. We identify the task scheduling problem for running multiple CNN models on mobile devices under resource constraints and formulate it as an integer programming problem. We propose resource-aware scheduling algorithms which combine offloading and local processing methods to minimize the completion time of video processing. We implement the proposed scheduling algorithms on Android-based smartphones and demonstrate its effectiveness through extensive experiments.","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":"130269652","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: 9

A Context-aware Black-box Adversarial Attack for Deep Driving Maneuver Classification Models 深度驾驶机动分类模型的上下文感知黑盒对抗攻击

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491584

Ankur Sarker, Haiying Shen, Tanmoy Sen

{"title":"A Context-aware Black-box Adversarial Attack for Deep Driving Maneuver Classification Models","authors":"Ankur Sarker, Haiying Shen, Tanmoy Sen","doi":"10.1109/SECON52354.2021.9491584","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491584","url":null,"abstract":"In a connected autonomous vehicle (CAV) scenario, each vehicle utilizes an onboard deep neural network (DNN) model to understand its received time-series driving signals (e.g., speed, brake status) from its nearby vehicles, and then takes necessary actions to increase traffic safety and roadway efficiency. In the scenario, it is plausible that an attacker may launch an adversarial attack, in which the attacker adds unnoticeable perturbation to the actual driving signals to fool the DNN model inside a victim vehicle to output a misclassified class to cause traffic congestion and/or accidents. Such an attack must be generated in near real-time and the adversarial maneuver must be consistent with the current traffic context. However, previously proposed adversarial attacks fail to meet these requirements. To handle these challenges, in this paper, we propose a Context- aware Black-box Adversarial Attack (CBAA) for time-series DNN models in CAV scenarios. By analyzing real driving datasets, we observe that specific driving signals at certain time points have a higher impact on the DNN output. These influential spatio-temporal factors differ in different traffic contexts (a combination of different traffic factors (e.g., congestion, slope, and curvature)). Thus, CBAA first generates the perturbation only on the influential spatio-temporal signals for each context offline. In generating an attack online, CBAA uses the offline perturbation for the current context to start searching the minimum perturbation using the zeroth-order gradient descent method that will lead to the misclassification. Limiting the spatio-temporal searching scope with the constraint of context greatly expedites finding the final perturbation. Our extensive experimental studies using two different real driving datasets show that CBAA requires 43% fewer queries (to the DNN model to verify the attack success) and 53% less time than existing adversarial attacks.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"23 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":"124483438","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

Traffic-Driven Sounding Reference Signal Resource Allocation in (Beyond) 5G Networks (超)5G网络中流量驱动的探测参考信号资源分配

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491611

Claudio Fiandrino, Giulia Attanasio, M. Fiore, J. Widmer

{"title":"Traffic-Driven Sounding Reference Signal Resource Allocation in (Beyond) 5G Networks","authors":"Claudio Fiandrino, Giulia Attanasio, M. Fiore, J. Widmer","doi":"10.1109/SECON52354.2021.9491611","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491611","url":null,"abstract":"Beyond 5G mobile networks have to support a wide range of performance requirements and unprecedented levels of flexibility. To this end, massive MIMO is a critical technology to improve spectral efficiency and thus scale up network capacity, by increasing the number of antenna elements. This also increases the overhead of Channel State Information (CSI) estimation and obtaining accurate CSI is a fundamental problem in massive MIMO systems. In this paper, we focus on scheduling uplink Sounding Reference Signals (SRSs) that carry pilot symbols for CSI estimation. Under the large number of users and high load that are expected to characterize beyond 5G systems, the limited amount of resources available for SRSs makes the legacy 3GPP periodic allocation scheme largely inefficient. We design TRADER, an SRS resource allocation framework that minimizes the age of channel estimates by taking advantage of machine learning-based short-term traffic forecasts at the base station level. By anticipating traffic bursts, TRADER schedules SRS resources so as to obtain CSI for each user right before the corresponding traffic arrives. Experiments with extensive real-world mobile network traces show that our solution is efficient and robust in high load scenarios: with respect to a round robin schedule of aperiodic SRS, TRADER provides more often CSI within the coherence time (up to 5× for given scenarios), leading to channel gains of up to 2 dB.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"29 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":"123553755","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

Vehicle In-Cabin Contactless WiFi Human Sensing 车内非接触式WiFi人体感应

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491580

Mohammed Ibrahim, K. Brown

引用次数: 1

SECON 2021 Program

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/secon52354.2021.9491620

引用次数: 0

Joint Space-Frequency Rendezvous for Multi-UAV Relaying Systems 多无人机中继系统的联合空频交会

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491598

Zixuan Zhang, Yunlong Wu, Qinhao Wu, Jinlin Peng, Bo Zhang

{"title":"Joint Space-Frequency Rendezvous for Multi-UAV Relaying Systems","authors":"Zixuan Zhang, Yunlong Wu, Qinhao Wu, Jinlin Peng, Bo Zhang","doi":"10.1109/SECON52354.2021.9491598","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491598","url":null,"abstract":"This paper investigates the multi-channel access and rendezvous problem in unmanned aerial vehicle (UAV) relaying system in the absence of pre-allocated control channel. Both the geographical sensing range and the spectrum sensing bandwidth of each UAV are limited due to onboard payload constraints, hence it becomes challenging to design effective and efficient channel rendezvous mechanisms. To address the challenge, this paper first observes and analyzes the effects of UAV relaying network topology and geographical sensing range on the rendezvous, and it is found that a joint exploitation of motion and frequency control is essential to achieve efficient rendezvous. Based on the important insight, this paper formulates the UAV relaying rendezvous problem and proposes a novel joint space-frequency rendezvous (JSFR) method for multi-UAV networks, incorporating with distributed reinforcement learning techniques. The simulation results show that the JSFR method may significantly improve the effectiveness and efficiency of rendezvous in UAV relaying networks, in terms of rendezvous probabilities and convergence rates.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"17 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":"126764803","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

Improving Quality Control with Industrial AIoT at HP Factories: Experiences and Learned Lessons 改进惠普工厂的工业AIoT质量控制:经验和教训

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491592

Joy Qiping Yang, Siyuan Zhou, D. V. Le, Daren Ho, Rui Tan

{"title":"Improving Quality Control with Industrial AIoT at HP Factories: Experiences and Learned Lessons","authors":"Joy Qiping Yang, Siyuan Zhou, D. V. Le, Daren Ho, Rui Tan","doi":"10.1109/SECON52354.2021.9491592","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491592","url":null,"abstract":"Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers wide interest of applying the resulting Artificial Intelligence of Things (AIoT) systems in industrial applications. The in situ inference and decision made based on the sensor data containing patterns with certain sophistication allow the industrial system to address a variety of heterogeneous, local-area non-trivial problems in the last hop of the IoT networks, avoiding the wireless bandwidth bottleneck and unreliability issues and also the cumbersome cloud. However, the literature still lacks presentations of industrial AIoT system developments that provide insights into the challenges and offer important lessons for the relevant research and engineering communities, no matter the development is successful or not. In light of this, we present the design, deployment, and evaluation of an industrial AIoT system for improving the quality control of Hewlett-Packard’s ink cartridge manufacturing lines. While our development has obtained promising results, we also discuss the lessons learned from the whole course of the effort, which could be useful to the developments of other industrial AIoT systems.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"50 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":"114081707","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

EFCam: Configuration-Adaptive Fog-Assisted Wireless Cameras with Reinforcement Learning EFCam:配置自适应雾辅助无线相机与强化学习

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2021-07-06 DOI: 10.1109/SECON52354.2021.9491609

Siyuan Zhou, D. V. Le, Joy Qiping Yang, Rui Tan, Daren Ho

{"title":"EFCam: Configuration-Adaptive Fog-Assisted Wireless Cameras with Reinforcement Learning","authors":"Siyuan Zhou, D. V. Le, Joy Qiping Yang, Rui Tan, Daren Ho","doi":"10.1109/SECON52354.2021.9491609","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491609","url":null,"abstract":"Visual sensing has been increasingly employed in industrial processes. This paper presents the design and implementation of an industrial wireless camera system, namely, EFCam, which uses low-power wireless communications and edge-fog computing to achieve cordless and energy-efficient visual sensing. The camera performs image pre-processing (i.e., compression or feature extraction) and transmits the data to a resourceful fog node for advanced processing using deep models. EFCam admits dynamic configurations of several parameters that form a configuration space. It aims to adapt the configuration to maintain desired visual sensing performance of the deep model at the fog node with minimum energy consumption of the camera in image capture, pre-processing, and data communications, under dynamic variations of application requirement and wireless channel conditions. However, the adaptation is challenging due primarily to the complex relationships among the involved factors. To address the complexity, we apply deep reinforcement learning to learn the optimal adaptation policy. Extensive evaluation based on trace-driven simulations and experiments show that EFCam complies with the accuracy and latency requirements with lower energy consumption for a real industrial product object tracking application, compared with four baseline approaches incorporating hysteresis-based adaptation.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"58 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":"124066543","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