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

{"title":"Bandwidth-Efficient Precoding in Cell-Free Massive MIMO Networks with Rician Fading Channels","authors":"Li Sun, Jing Hou, Tao Shu","doi":"10.1109/SECON52354.2021.9491585","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491585","url":null,"abstract":"Global precoding is an effective way to suppress interference in cell-free massive MIMO systems. However, it requires all access points (APs) to upload their local instantaneous channel state information (CSI) to a central processor via capacity-constrained fronthaul links, consuming significant bandwidth resources. Such overhead may become unaffordable in an ultra-dense network (UDN) in future 5G systems, due to the large number of APs and the frequent CSI uploads required to combat the fast-changing state of the high-frequency channels. In order to address this issue, we propose a novel bandwidth-efficient global zero-forcing precoding strategy for downlink transmission in cell-free massive MIMO systems. By exploiting the physical structure of Rician fading channels, we propose a novel model-based CSI compression mechanism, which decomposes a channel matrix into a line-of-sight (LoS) and a non-line-of-sight (NLoS) components, and then compresses them using a model-based method and a singular-value-decomposition (SVD)-based method, respectively. We also present two optimization-based algorithms to obtain the phase information of the LoS component of the channel, which is then used by the proposed channel matrix decomposition. The simulation results demonstrate the efficiency of the proposed precoding strategy on reducing the upload overhead and improving the bandwidth efficiency.","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":"131177366","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":"VibWriter: Handwriting Recognition System based on Vibration Signal","authors":"D. Ding, Lanqing Yang, Yi-Chao Chen, Guangtao Xue","doi":"10.1109/SECON52354.2021.9491615","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491615","url":null,"abstract":"The efficiency of human-computer interaction is greatly hindered by the small size of the touchscreens on mobile devices, such as smart phones and watches. This has prompted widespread interest in handwriting recognition systems, which can be divided into active and passive systems. Active systems require additional hardware devices to perceive movements of handwriting or the tracking accuracy is not adequate for hand-writing recognition. Passive methods use the acoustic signal of pen rubbing and are susceptible to environmental noise (above 60dB). This paper presents a novel handwriting recognition system based on vibration signals detected by the built-in accelerometer of smart phones. VibWriter is highly resistant to interference since the normal environmental noise will not cause the vibration of the accelerometer. Extensive experiments demonstrated the efficacy of the system in terms of accuracy in letter recognition (76.15%) and word recognition (88.14%) when dealing with words of various lengths written by various users in a variety of writing positions under a variety of environmental conditions.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"31 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":"133344473","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":"Log Management in NFV Service Orchestration","authors":"E. Zeydan, J. Baranda, J. Mangues‐Bafalluy, R. Martínez, L. Vettori","doi":"10.1109/SECON52354.2021.9491606","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491606","url":null,"abstract":"Measuring several relevant metrics related to Network Function Virtualization (NFV) service lifecycle management in real time brings an enhanced monitoring of the operation of the network service orchestrator (SO) and the NFV infrastructure. In this demonstration, we integrate a complete data engineering pipeline in an operational management and orchestration stack (that of EU 5Growth project) to analyze lifecycle management metrics in real-time, in this case the network service instantiation time related metrics. In our demonstration, a data connection module instance continuously monitors the NFV SO log files and sends the log changes to the data ingestion layer, where log files are temporarily stored to be fetched by Apache Spark jobs. After utilizing Spark jobs to cleanse the log files and to obtain the service instantiation times, the metrics are sent back to the data ingestion layer to be transferred to the Elasticsearch (ELK) stack for data indexing and visualization purposes. Furthermore, the statistical information of network service instantiation (in total and its components) of studied metrics inside the network can also be profiled via a separate data analysis layer connected to the ELK stack.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"48 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":"131818602","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":"A Practical Secret Key Management for Multihop Drone Relay Systems based on Bluetooth Low Energy","authors":"Kai Li, N. Lu, Jingjing Zheng, Pei Zhang, Wei Ni, E. Tovar","doi":"10.1109/SECON52354.2021.9491599","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491599","url":null,"abstract":"In this paper, we present a practical secret key management for data relay security of bluetooth-connected drones. Time-varying received signal strengths between the drones and the ground sensing nodes are quantized to generate the secret key pairs, where the quantization interval is adjusted to reduce the number of mismatched secret key bits. To validate the key management performance, a multihop aerial relay system testbed is developed based on the MX400 drone platform and the bluetooth low energy radio transceiver.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"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":"127707157","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":"Incentivizing Opportunistic Data Collection for Time-Sensitive IoT Applications","authors":"P. Kortoçi, Abbas Mehrabi, Carlee Joe-Wong, M. D. Francesco","doi":"10.1109/SECON52354.2021.9491593","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491593","url":null,"abstract":"Urban environments are the most prevalent application scenario for the Internet of Things (IoT). In this context, effective data collection and forwarding to a cloud (or edge) server are particularly important. This work leverages opportunistic data collection based on the mobile crowd sourcing (MCS) paradigm for time-sensitive IoT applications. Specifically, it introduces an incentive mechanism for the crowd to collect data that are valuable to data consumers in terms of regions of interest and time constraints. The proposed approach successfully incorporates the willingness of the crowd to participate in the data collection as part of the related incentives. It also ensures collection of valuable data via selective user incentivization. Accordingly, a weighted social welfare maximization problem is defined for users to decide which sensors to visit subject to deadline constraints. Following the NP-hardness of the problem, an online heuristic algorithm is proposed for sensors to dynamically incentivize mobile users with a low message and time complexity. The proposed solution is shown to be effective for time-sensitive quality data collection through extensive simulations on realistic mobility traces. It significantly increases the overall social welfare as well as the amount of collected data compared to other approaches.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"11 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":"126291185","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":"SoundFence: Securing Ultrasonic Sensors in Vehicles Using Physical-Layer Defense","authors":"Jianzhi Lou, Qiben Yan, Qing Hui, Huacheng Zeng","doi":"10.1109/SECON52354.2021.9491590","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491590","url":null,"abstract":"Autonomous vehicles (AVs), equipped with numerous sensors such as camera, LiDAR, radar, and ultrasonic sensor, are revolutionizing the transportation industry. These sensors are expected to sense reliable information from a physical environment, facilitating the critical decision-making process of the AVs. Ultrasonic sensors, which detect obstacles in a short distance, play an important role in assisted parking and blind spot detection events. However, due to their weak security level, ultrasonic sensors are particularly vulnerable to signal injection attacks, when the attackers inject malicious acoustic signals to create fake obstacles and intentionally mislead the vehicles to make wrong decisions with disastrous aftermath. In this paper, we systematically analyze the attack model of signal injection attacks toward moving vehicles. By considering the potential threats, we propose SoundFence, a physical-layer defense system which leverages the sensors’ signal processing capability without requiring any additional equipment. SoundFence verifies the benign measurement results and detects signal injection attacks by analyzing sensor readings and the physical-layer signatures of ultrasonic signals. Our experiment with commercial sensors shows that SoundFence detects most (more than 95%) of the abnormal sensor readings with very few false alarms, and it can also accurately distinguish the real echo from injected signals to identify injection attacks.","PeriodicalId":120945,"journal":{"name":"2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130605214","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":"SeReMAS: Self-Resilient Mobile Autonomous Systems Through Predictive Edge Computing","authors":"Davide Callegaro, M. Levorato, Francesco Restuccia","doi":"10.1109/SECON52354.2021.9491618","DOIUrl":"https://doi.org/10.1109/SECON52354.2021.9491618","url":null,"abstract":"Edge computing enables Mobile Autonomous Systems (MASs) to execute continuous streams of heavy-duty mission-critical processing tasks, such as real-time obstacle detection and navigation. However, in practical applications, erratic patterns in channel quality, network load, and edge server load can interrupt the task flow’s execution, which necessarily leads to severe disruption of the system’s key operations. Existing work has mostly tackled the problem with reactive approaches, which cannot guarantee task-level reliability. Conversely, in this paper we focus on learning-based predictive edge computing to achieve self-resilient task offloading. By conducting a preliminary experimental evaluation, we show that there is no dominant feature that can predict the edge-MAS system reliability, which calls for an ensemble and selection of weaker features. To tackle the complexity of the problem, we propose SeReMAS, a data-driven optimization framework. We first mathematically formulate a Redundant Task Offloading Problem (RTOP), where a MAS may connect to multiple edge servers for redundancy, and needs to select which server(s) to transmit its computing tasks in order to maximize the probability of task execution while minimizing channel and edge resource utilization. We then create a predictor based on Deep Reinforcement Learning (DRL), which produces the optimum task assignment based on application-, network- and telemetry-based features. We prototype SeReMAS on a testbed composed by a Tarot650 quadcopter drone, mounting a PixHawk flight controller, a Jetson Nano board, and three 802.11n WiFi interfaces. We extensively evaluate SeReMAS by considering an application where one drone offloads high-resolution images for real-time analysis to three edge servers on the ground. Experimental results show that SeReMAS improves the task execution probability by 17% with respect to existing reactive-based approaches. To allow full reproducibility of results, we share the dataset and code with the research community.","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":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123333695","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}