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Robust Environmental Sensing Using UAVs 利用无人机进行鲁棒环境感知
IF 2.7 Pub Date : 2021-07-15 DOI: 10.1145/3464943
Ahmed Boubrima, E. Knightly
In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.
在本文中,我们首先研究了空中空气污染测量的质量,并描述了无人机安装的气体传感器的主要误差来源。为此,我们建立了ASTRO+,一个空中-地面污染监测平台,并使用它来收集空中和参考空气污染测量的综合数据集。我们表明,无人机引起的动态气流会影响周围空气的温度和湿度水平,进而影响气体传感器的测量质量。然后,在本文的第二部分,我们利用天气条件对污染测量质量的影响,以设计一种无人机任务规划算法,该算法在考虑航空测量质量的同时适应无人机的轨迹。我们基于挥发性有机化合物污染数据集评估了我们的任务规划方法,并显示了即使在污染动态高的情况下也能保持高性能的改进。
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引用次数: 1
ASTRO 阿斯特罗
IF 2.7 Pub Date : 2021-07-15 DOI: 10.1145/3464942
Riccardo Petrolo, Zhambyl Shaikhanov, Yingyan Lin, E. Knightly
We present the design, implementation, and experimental evaluation of ASTRO, a modular end-to-end system for distributed sensing missions with autonomous networked drones. We introduce the fundamental system architecture features that enable agnostic sensing missions on top of the ASTRO drones. We demonstrate the key principles of ASTRO by using on-board software-defined radios to find and track a mobile radio target. We show how simple distributed on-board machine learning methods can be used to find and track a mobile target, even if all drones lose contact with a ground control. Also, we show that ASTRO is able to find the target even if it is hiding under a three-ton concrete slab, representing a highly irregular propagation environment. Our findings reveal that, despite no prior training and noisy sensory measurements, ASTRO drones are able to learn the propagation environment in the scale of seconds and localize a target with a mean accuracy of 8 m. Moreover, ASTRO drones are able to track the target with relatively constant error over time, even as it moves at a speed close to the maximum drone speed.
我们介绍了ASTRO的设计、实现和实验评估,ASTRO是一个模块化的端到端系统,用于自主联网无人机的分布式传感任务。我们介绍了在ASTRO无人机上实现不可知论传感任务的基本系统架构特征。我们通过使用机载软件定义无线电来发现和跟踪移动无线电目标来演示ASTRO的关键原理。我们展示了如何使用简单的分布式机载机器学习方法来查找和跟踪移动目标,即使所有无人机都与地面控制失去联系。此外,我们还展示了ASTRO能够找到目标,即使它隐藏在3吨重的混凝土板下,这代表了一个高度不规则的传播环境。我们的研究结果表明,尽管没有事先的训练和嘈杂的感官测量,ASTRO无人机能够在秒的尺度上学习传播环境,并以8米的平均精度定位目标。此外,ASTRO无人机能够在一段时间内以相对恒定的误差跟踪目标,即使它以接近无人机最大速度的速度移动。
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引用次数: 155
A Novel Insider Attack and Machine Learning Based Detection for the Internet of Things 一种新的内部攻击和基于机器学习的物联网检测
IF 2.7 Pub Date : 2021-07-15 DOI: 10.1145/3466721
Morshed U. Chowdhury, B. Ray, Sujan Chowdhury, S. Rajasegarar
Due to the widespread functional benefits, such as supporting internet connectivity, having high visibility and enabling easy connectivity between sensors, the Internet of Things (IoT) has become popular and used in many applications, such as for smart city, smart health, smart home, and smart vehicle realizations. These IoT-based systems contribute to both daily life and business, including sensitive and emergency situations. In general, the devices or sensors used in the IoT have very limited computational power, storage capacity, and communication capabilities, but they help to collect a large amount of data as well as maintain communication with the other devices in the network. Since most of the IoT devices have no physical security, and often are open to everyone via radio communication and via the internet, they are highly vulnerable to existing and emerging novel security attacks. Further, the IoT devices are usually integrated with the corporate networks; in this case, the impact of attacks will be much more significant than operating in isolation. Due to the constraints of the IoT devices, and the nature of their operation, existing security mechanisms are less effective for countering the attacks that are specific to the IoT-based systems. This article presents a new insider attack, named loophole attack, that exploits the vulnerabilities present in a widely used IPv6 routing protocol in IoT-based systems, called RPL (Routing over Low Power and Lossy Networks). To protect the IoT system from this insider attack, a machine learning based security mechanism is presented. The proposed attack has been implemented using a Contiki IoT operating system that runs on the Cooja simulator, and the impacts of the attack are analyzed. Evaluation on the collected network traffic data demonstrates that the machine learning based approaches, along with the proposed features, help to accurately detect the insider attack from the network traffic data.
由于物联网(IoT)具有广泛的功能优势,例如支持互联网连接,具有高可视性和实现传感器之间的轻松连接,因此物联网(IoT)已变得流行并用于许多应用,例如智能城市,智能健康,智能家居和智能车辆实现。这些基于物联网的系统有助于日常生活和业务,包括敏感和紧急情况。一般来说,物联网中使用的设备或传感器的计算能力、存储容量和通信能力非常有限,但它们有助于收集大量数据并保持与网络中其他设备的通信。由于大多数物联网设备没有物理安全性,并且通常通过无线电通信和互联网向所有人开放,因此它们极易受到现有和新兴的新型安全攻击。此外,物联网设备通常与企业网络集成;在这种情况下,攻击的影响将比孤立运作严重得多。由于物联网设备的限制及其操作的性质,现有的安全机制对于对抗针对基于物联网的系统的攻击不太有效。本文提出了一种新的内部攻击,称为漏洞攻击,它利用了在基于物联网的系统中广泛使用的IPv6路由协议中存在的漏洞,称为RPL(低功耗和有损网络路由)。为了保护物联网系统免受这种内部攻击,提出了一种基于机器学习的安全机制。采用运行在Cooja模拟器上的Contiki IoT操作系统实施了该攻击,并分析了攻击的影响。对收集的网络流量数据的评估表明,基于机器学习的方法以及所提出的功能有助于从网络流量数据中准确检测内部攻击。
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引用次数: 10
A Survey of On-Device Machine Learning 设备上机器学习的调查
IF 2.7 Pub Date : 2021-07-01 DOI: 10.1145/3450494
Sauptik Dhar, Junyao Guo, Jiayi Liu, S. Tripathi, Unmesh Kurup, Mohak Shah
The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing numbers of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state of the art and for identifying open challenges and future avenues of research. However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.). Hence, covering such a large number of topics in a single survey is impractical. This survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state of the art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects of on-device learning.
在设备上使用机器学习模型的主要范例是在云中训练模型,并使用设备上的训练模型执行推理。然而,随着智能设备数量的增加和硬件的改进,人们对在设备上进行模型训练很感兴趣。鉴于这种兴趣激增,从设备不可知论的角度对该领域进行全面调查,为理解技术现状、确定开放的挑战和未来的研究途径奠定了基础。然而,设备上学习是一个广阔的领域,与人工智能和机器学习中的大量相关主题(包括在线学习、模型自适应、一次/几次学习等)有联系。因此,在一次调查中涵盖如此多的主题是不切实际的。这项调查通过将设备上学习的问题重新表述为资源约束学习,其中资源是计算和内存,从而找到了一个中间立场。这种重新表述允许来自各种研究领域的工具、技术和算法进行公平比较。除了总结目前的现状外,该调查还指出了设备上学习在算法和理论方面面临的一些挑战和下一步。
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引用次数: 30
Enabling Service Cache in Edge Clouds 启用边缘云服务缓存
IF 2.7 Pub Date : 2021-07-01 DOI: 10.1145/3456564
Chih-Kai Huang, Shan-Hsiang Shen
The next-generation 5G cellular networks are designed to support the internet of things (IoT) networks; network components and services are virtualized and run either in virtual machines (VMs) or containers. Moreover, edge clouds (which are closer to end users) are leveraged to reduce end-to-end latency especially for some IoT applications, which require short response time. However, the computational resources are limited in edge clouds. To minimize overall service latency, it is crucial to determine carefully which services should be provided in edge clouds and serve more mobile or IoT devices locally. In this article, we propose a novel service cache framework called S-Cache, which automatically caches popular services in edge clouds. In addition, we design a new cache replacement policy to maximize the cache hit rates. Our evaluations use real log files from Google to form two datasets to evaluate the performance. The proposed cache replacement policy is compared with other policies such as greedy-dual-size-frequency (GDSF) and least-frequently-used (LFU). The experimental results show that the cache hit rates are improved by 39% on average, and the average latency of our cache replacement policy decreases 41% and 38% on average in these two datasets. This indicates that our approach is superior to other existing cache policies and is more suitable in multi-access edge computing environments. In the implementation, S-Cache relies on OpenStack to clone services to edge clouds and direct the network traffic. We also evaluate the cost of cloning the service to an edge cloud. The cloning cost of various real applications is studied by experiments under the presented framework and different environments.
下一代5G蜂窝网络旨在支持物联网(IoT)网络;网络组件和服务是虚拟化的,可以在虚拟机或容器中运行。此外,利用边缘云(更接近最终用户)来减少端到端延迟,特别是对于一些需要短响应时间的物联网应用程序。然而,边缘云的计算资源是有限的。为了最大限度地减少整体服务延迟,必须仔细确定应该在边缘云中提供哪些服务,并在本地为更多的移动或物联网设备提供服务。在本文中,我们提出了一个名为S-Cache的新型服务缓存框架,它可以自动缓存边缘云中流行的服务。此外,我们还设计了一种新的缓存替换策略来最大化缓存命中率。我们的评估使用来自Google的真实日志文件来形成两个数据集来评估性能。将提出的缓存替换策略与其他策略(如贪心双大小频率(GDSF)和最少使用频率(LFU))进行了比较。实验结果表明,在这两个数据集上,我们的缓存替换策略的缓存命中率平均提高了39%,缓存替换策略的平均延迟平均降低了41%和38%。这表明我们的方法优于其他现有的缓存策略,更适合于多访问边缘计算环境。在实现过程中,S-Cache依靠OpenStack将服务克隆到边缘云,引导网络流量。我们还评估了将服务克隆到边缘云的成本。在提出的框架和不同的环境下,通过实验研究了各种实际应用的克隆成本。
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引用次数: 7
Living on the Edge 生活在边缘
IF 2.7 Pub Date : 2021-07-01 DOI: 10.1145/3450767
Thilina Buddhika, Matthew Malensek, S. Pallickara, S. Pallickara
Voluminous time-series data streams produced in continuous sensing environments impose challenges pertaining to ingestion, storage, and analytics. In this study, we present a holistic approach based on data sketching to address these issues. We propose a hyper-sketching algorithm that combines discretization and frequency-based sketching to produce compact representations of the multi-feature, time-series data streams. We generate an ensemble of data sketches to make effective use of capabilities at the resource-constrained edge devices, the links over which data are transmitted, and the server pool where this data must be stored. The data sketches can be queried to construct datasets that are amenable to processing using popular analytical engines. We include several performance benchmarks using real-world data from different domains to profile the suitability of our design decisions. The proposed methodology can achieve up to ∼ 13 × and ∼ 2, 207 × reduction in data transfer and energy consumption at edge devices. We observe up to a ∼ 50% improvement in analytical job completion times in addition to the significant improvements in disk and network I/O.
在连续传感环境中产生的大量时间序列数据流对摄取、存储和分析提出了挑战。在本研究中,我们提出了一种基于数据草图的整体方法来解决这些问题。我们提出了一种结合离散化和基于频率的素描的超素描算法,以产生多特征时间序列数据流的紧凑表示。我们生成一组数据草图,以便有效地利用资源受限的边缘设备上的功能、传输数据的链接以及必须存储数据的服务器池。可以查询数据草图以构建适合使用流行的分析引擎处理的数据集。我们使用来自不同领域的真实数据包括了几个性能基准,以分析我们的设计决策的适用性。所提出的方法可以在边缘设备上实现高达~ 13 ×和~ 2,207 ×的数据传输和能量消耗减少。除了磁盘和网络I/O方面的显著改进外,我们还观察到分析作业完成时间提高了50%。
{"title":"Living on the Edge","authors":"Thilina Buddhika, Matthew Malensek, S. Pallickara, S. Pallickara","doi":"10.1145/3450767","DOIUrl":"https://doi.org/10.1145/3450767","url":null,"abstract":"Voluminous time-series data streams produced in continuous sensing environments impose challenges pertaining to ingestion, storage, and analytics. In this study, we present a holistic approach based on data sketching to address these issues. We propose a hyper-sketching algorithm that combines discretization and frequency-based sketching to produce compact representations of the multi-feature, time-series data streams. We generate an ensemble of data sketches to make effective use of capabilities at the resource-constrained edge devices, the links over which data are transmitted, and the server pool where this data must be stored. The data sketches can be queried to construct datasets that are amenable to processing using popular analytical engines. We include several performance benchmarks using real-world data from different domains to profile the suitability of our design decisions. The proposed methodology can achieve up to ∼ 13 × and ∼ 2, 207 × reduction in data transfer and energy consumption at edge devices. We observe up to a ∼ 50% improvement in analytical job completion times in addition to the significant improvements in disk and network I/O.","PeriodicalId":29764,"journal":{"name":"ACM Transactions on Internet of Things","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74694599","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
Exploiting Multi-modal Contextual Sensing for City-bus’s Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction 基于多模态上下文感知的城市公交停留位置表征:迈向60秒以下准确到达时间预测
IF 2.7 Pub Date : 2021-05-24 DOI: 10.1145/3549548
Ratna Mandal, Prasenjit Karmakar, S. Chatterjee, Debaleen Das Spandan, S. Pradhan, Sujoy Saha, Sandip Chakraborty, S. Nandi
Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transport like public buses, allowing them to pre-plan their travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations where a public bus stops. Although straightforward factors like stay duration extracted from unimodal sources like GPS at these locations look erratic, a thorough analysis of public bus GPS trails for 1,335.365 km at the city of Durgapur, a semi-urban city in India, reveals that several other fine-grained contextual features can characterize these locations accurately. Accordingly, we develop BuStop, a system for extracting and characterizing the stay-locations from multi-modal sensing using commuters’ smartphones. Using this multi-modal information BuStop extracts a set of granular contextual features that allows the system to differentiate among the different stay-location types. A thorough analysis of BuStop using the collected in-house dataset indicates that the system works with high accuracy in identifying different stay-locations such as regular bus stops, random ad hoc stops, stops due to traffic congestion, stops at traffic signals, and stops at sharp turns. Additionally, we develop a proof-of-concept setup on top of BuStop to analyze the potential of the framework in predicting expected arrival time, a critical piece of information required to pre-plan travel at any given bus stop. Subsequent analysis of the PoC framework, through simulation over the test dataset, shows that characterizing the stay-locations indeed helps make more accurate arrival time predictions with deviations less than 60 seconds from the ground-truth arrival time.
智慧城市交通系统是智慧城市的核心基础设施之一。这种基础设施的真正独创性在于为通勤者提供全市交通(如公交车)的实时信息,使他们能够提前计划自己的出行。然而,为公交等交通系统提供实时的先验信息本身就具有挑战性,因为公交停站的不同停留位置具有多样性。尽管从这些地点的GPS等单一模式来源提取的停留时间等直接因素看起来不稳定,但对印度杜尔加普尔市1335.365公里公共汽车GPS轨迹的全面分析显示,其他几个细粒度的背景特征可以准确地描述这些地点。因此,我们开发了BuStop,这是一个使用通勤者智能手机从多模态传感中提取和表征停留位置的系统。利用这些多模态信息,BuStop提取了一组细粒度的上下文特征,使系统能够区分不同的停留位置类型。利用收集的内部数据对BuStop进行深入分析,结果表明,该系统在识别常规公交车站、随机临时车站、交通拥堵停车、交通信号停车、急转弯停车等不同停车地点方面具有很高的准确性。此外,我们在BuStop之上开发了一个概念验证设置,以分析该框架在预测预期到达时间方面的潜力,这是在任何给定的公共汽车站预先计划旅行所需的关键信息。通过对测试数据集的模拟,对PoC框架的后续分析表明,描述停留位置确实有助于更准确地预测到达时间,与地面真实到达时间的偏差小于60秒。
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引用次数: 3
WiFi-Enabled User Authentication through Deep Learning in Daily Activities 通过深度学习在日常活动中支持wifi的用户认证
IF 2.7 Pub Date : 2021-05-04 DOI: 10.1145/3448738
Cong Shi, Jian Liu, Hongbo Liu, Yingying Chen
User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This article supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV, and smart thermostat, and so on. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep-learning-based user authentication scheme to accurately identify each individual user. To mitigate the signal distortion caused by surrounding people’s movements, our deep learning model exploits a CNN-based architecture that constructively combines features from multiple receiving antennas and derives more reliable feature abstractions. Furthermore, a transfer-learning-based mechanism is developed to reduce the training cost for new users and environments. Extensive experiments in various indoor environments are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% authentication accuracy with 11 subjects through different activities.
由于日益增长的安全和隐私问题,用户身份验证在企业和家庭环境中都是一个关键过程。随着智慧城市和家庭环境的发展,用户认证的概念不断发展,不仅可以防止未经授权的用户访问机密信息,还可以为特定用户提供相应的定制服务。传统的用户认证方法要么需要安装专门的设备,要么不方便安装可穿戴传感器。本文通过利用物联网设备(如智能冰箱、智能电视和智能恒温器等)提供的流行WiFi信号,通过无设备方法支持用户身份验证的扩展概念。该系统利用WiFi信号捕捉从日常活动中继承的独特的人类生理和行为特征,包括步行和静止的活动。特别是,我们从WiFi信号的信道状态信息(CSI)测量中提取代表性特征,并开发了基于深度学习的用户认证方案,以准确识别每个用户。为了减轻由周围人的运动引起的信号失真,我们的深度学习模型利用了基于cnn的架构,该架构建设性地结合了来自多个接收天线的特征,并派生出更可靠的特征抽象。此外,开发了一种基于迁移学习的机制,以降低新用户和新环境的培训成本。在各种室内环境中进行了大量实验,以证明所提出的认证系统的有效性。特别是,我们的系统可以通过11个主体通过不同的活动实现94%以上的认证准确率。
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引用次数: 13
Environment-driven Communication in Battery-free Smart Buildings 无电池智能建筑中的环境驱动通信
IF 2.7 Pub Date : 2021-04-22 DOI: 10.1145/3448739
Mauro Piva, Andrea Coletta, G. Maselli, J. Stankovic
Recent years have witnessed the design and development of several smart devices that are wireless and battery-less. These devices exploit RFID backscattering-based computation and transmissions. Although singular devices can operate efficiently, their coexistence needs to be controlled, as they have widely varying communication requirements, depending on their interaction with the environment. The design of efficient communication protocols able to dynamically adapt to current device operation is quite a new problem that the existing work cannot solve well. In this article, we propose a new communication protocol, called ReLEDF, that dynamically discovers devices in smart buildings and their active and nonactive status and when active their current communication behavior (through a learning-based mechanism) and schedules transmission slots (through an Earliest Deadline First-- (EDF) based mechanism) adapt to different data transmission requirements. Combining learning and scheduling introduces a tag starvation problem, so we also propose a new mode-change scheduling approach. Extensive simulations clearly show the benefits of using ReLEDF, which successfully delivers over 95% of new data samples in a typical smart home scenario with up to 150 heterogeneous smart devices, outperforming related solutions. Real experiments are also conducted to demonstrate the applicability of ReLEDF and to validate the simulations.
近年来,人们设计和开发了几种无线和无电池的智能设备。这些设备利用基于RFID反向散射的计算和传输。尽管单个设备可以高效运行,但它们的共存需要控制,因为它们根据与环境的交互而具有广泛不同的通信需求。设计能够动态适应当前设备运行的高效通信协议是一个现有工作无法很好解决的新问题。在本文中,我们提出了一种新的通信协议,称为ReLEDF,它动态地发现智能建筑中的设备及其活动和非活动状态,以及激活时它们当前的通信行为(通过基于学习的机制)和调度传输插槽(通过基于最早截止日期优先(EDF)的机制)以适应不同的数据传输需求。结合学习和调度引入了标签饥饿问题,因此我们也提出了一种新的模式改变调度方法。广泛的模拟清楚地显示了使用ReLEDF的好处,它在典型的智能家居场景中成功地提供了超过95%的新数据样本,其中多达150个异构智能设备,优于相关解决方案。通过实际实验验证了该方法的适用性和仿真结果的正确性。
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引用次数: 0
Elk Audio OS
IF 2.7 Pub Date : 2021-03-01 DOI: 10.1145/3446393
L. Turchet, C. Fischione
As the Internet of Musical Things (IoMusT) emerges, audio-specific operating systems (OSs) are required on embedded hardware to ease development and portability of IoMusT applications. Despite the increasing importance of IoMusT applications, in this article, we show that there is no OS able to fulfill the diverse requirements of IoMusT systems. To address such a gap, we propose the Elk Audio OS as a novel and open source OS in this space. It is a Linux-based OS optimized for ultra-low-latency and high-performance audio and sensor processing on embedded hardware, as well as for handling wireless connectivity to local and remote networks. Elk Audio OS uses the Xenomai real-time kernel extension, which makes it suitable for the most demanding of low-latency audio tasks. We provide the first comprehensive overview of Elk Audio OS, describing its architecture and the key components of interest to potential developers and users. We explain operational aspects like the configuration of the architecture and the control mechanisms of the internal sound engine, as well as the tools that enable an easier and faster development of connected musical devices. Finally, we discuss the implications of Elk Audio OS, including the development of an open source community around it.
随着音乐物联网(IoMusT)的出现,嵌入式硬件需要音频专用操作系统(os)来简化IoMusT应用程序的开发和可移植性。尽管IoMusT应用程序的重要性日益增加,但在本文中,我们表明没有一种操作系统能够满足IoMusT系统的各种需求。为了解决这样的差距,我们提出Elk Audio OS作为这个领域的一个新颖的开源操作系统。它是一个基于linux的操作系统,针对嵌入式硬件上的超低延迟和高性能音频和传感器处理,以及处理本地和远程网络的无线连接进行了优化。Elk Audio OS使用Xenomai实时内核扩展,这使得它适合最苛刻的低延迟音频任务。我们提供了Elk Audio OS的第一个全面概述,描述了它的架构和潜在开发人员和用户感兴趣的关键组件。我们解释了操作方面,如架构的配置和内部声音引擎的控制机制,以及工具,使连接的音乐设备更容易和更快的发展。最后,我们讨论了Elk Audio OS的含义,包括围绕它开发的开源社区。
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引用次数: 22
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ACM Transactions on Internet of Things
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