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Managing Heterogeneous and Time-Sensitive IoT Applications through Collaborative and Energy-Aware Resource Allocation 通过协作和能源感知资源分配管理异构和时间敏感的物联网应用
IF 2.7 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-02-15 DOI: 10.1145/3488248
Tiago C. S. Xavier, Flávia Coimbra Delicato, Paulo F. Pires, Cláudio L. Amorim, Wei Li, Albert Y. Zomaya
In the Internet of Things (IoT) environment, the computing resources available in the cloud are often unable to meet the latency constraints of time critical applications due to the large distance between the cloud and data sources (IoT devices). The adoption of edge computing can help the cloud deliver services that meet time critical application requirements. However, it is challenging to meet the IoT application demands while using the resources smartly to reduce energy consumption at the edge of the network. In this context, we propose a fully distributed resource allocation algorithm for the IoT-edge-cloud environment, which (i) increases the infrastructure resource usage by promoting the collaboration between edge nodes, (ii) supports the heterogeneity and generic requirements of applications, and (iii) reduces the application latency and increases the energy efficiency of the edge. We compare our algorithm with a non-collaborative vertical offloading and with a horizontal approach based on edge collaboration. Results of simulations showed that the proposed algorithm is able to reduce 49.95% of the IoT application request end-to-end latency, increase 95.35% of the edge node utilization, and enhance the energy efficiency in terms of the edge node power consumption by 92.63% in comparison to the best performances of vertical and collaboration approaches.
在物联网(IoT)环境中,由于云与数据源(IoT设备)之间的距离较大,云中可用的计算资源往往无法满足时间关键型应用的延迟限制。采用边缘计算可以帮助云提供满足时间关键型应用程序需求的服务。然而,在满足物联网应用需求的同时,如何巧妙地利用资源来降低网络边缘的能耗是一个挑战。在此背景下,我们提出了一种针对物联网边缘云环境的全分布式资源分配算法,该算法(i)通过促进边缘节点之间的协作来增加基础设施资源的使用,(ii)支持应用程序的异构性和通用需求,(iii)减少应用程序延迟并提高边缘的能源效率。我们将我们的算法与非协作的垂直卸载和基于边缘协作的水平方法进行了比较。仿真结果表明,与垂直和协作方法相比,该算法能够降低49.95%的物联网应用请求端到端延迟,提高95.35%的边缘节点利用率,提高92.63%的边缘节点功耗能效。
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引用次数: 2
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series 基于表示学习和非线性关系的时间序列域自适应
IF 2.7 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-02-15 DOI: 10.1145/3502905
A. Hussein, Hazem Hajj
In many real-world scenarios, machine learning models fall short in prediction performance due to data characteristics changing from training on one source domain to testing on a target domain. There has been extensive research to address this problem with Domain Adaptation (DA) for learning domain invariant features. However, when considering advances for time series, those methods remain limited to the use of hard parameter sharing (HPS) between source and target models, and the use of domain adaptation objective function. To address these challenges, we propose a soft parameter sharing (SPS) DA architecture with representation learning while modeling the relation as non-linear between parameters of source and target models and modeling the adaptation loss function as the squared Maximum Mean Discrepancy (MMD). The proposed architecture advances the state-of-the-art for time series in the context of activity recognition and in fields with other modalities, where SPS has been limited to a linear relation. An additional contribution of our work is to provide a study that demonstrates the strengths and limitations of HPS versus SPS. Experiment results showed the success of the method in three domain adaptation cases of multivariate time series activity recognition with different users and sensors.
在许多现实场景中,由于数据特征从一个源域的训练变化到目标域的测试,机器学习模型在预测性能上存在不足。为了解决这个问题,已经有大量的研究使用领域自适应(DA)来学习领域不变特征。然而,当考虑到时间序列的进展时,这些方法仍然局限于源模型和目标模型之间的硬参数共享(HPS)和领域自适应目标函数的使用。为了解决这些挑战,我们提出了一种带有表示学习的软参数共享(SPS)数据分析架构,同时将源模型和目标模型参数之间的关系建模为非线性关系,并将自适应损失函数建模为最大平均差异(MMD)的平方。所提出的架构在活动识别和其他模式领域中推进了时间序列的最新技术,其中SPS仅限于线性关系。我们工作的另一个贡献是提供了一项研究,证明了HPS与SPS的优势和局限性。实验结果表明,该方法在不同用户和传感器的多变量时间序列活动识别的三个领域自适应案例中取得了成功。
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引用次数: 6
Next2You: Robust Copresence Detection Based on Channel State Information nextyou:基于信道状态信息的鲁棒共现检测
IF 2.7 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2021-11-09 DOI: 10.1145/3491244
Mikhail Fomichev, L. F. Abanto-Leon, Maximilian Stiegler, Alejandro Molina, Jakob Link, M. Hollick
Context-based copresence detection schemes are a necessary prerequisite to building secure and usable authentication systems in the Internet of Things (IoT). Such schemes allow one device to verify proximity of another device without user assistance utilizing their physical context (e.g., audio). The state-of-the-art copresence detection schemes suffer from two major limitations: (1) They cannot accurately detect copresence in low-entropy context (e.g., empty room with few events occurring) and insufficiently separated environments (e.g., adjacent rooms), (2) They require devices to have common sensors (e.g., microphones) to capture context, making them impractical on devices with heterogeneous sensors. We address these limitations, proposing Next2You, a novel copresence detection scheme utilizing channel state information (CSI). In particular, we leverage magnitude and phase values from a range of subcarriers specifying a Wi-Fi channel to capture a robust wireless context created when devices communicate. We implement Next2You on off-the-shelf smartphones relying only on ubiquitous Wi-Fi chipsets and evaluate it based on over 95 hours of CSI measurements that we collect in five real-world scenarios. Next2You achieves error rates below 4%, maintaining accurate copresence detection both in low-entropy context and insufficiently separated environments. We also demonstrate the capability of Next2You to work reliably in real-time and its robustness to various attacks.
基于上下文的身份检测方案是在物联网(IoT)中构建安全可用的身份验证系统的必要前提。这样的方案允许一个设备验证另一个设备的接近度,而无需用户帮助利用其物理环境(例如,音频)。最先进的共现检测方案有两个主要限制:(1)它们不能准确地检测低熵环境(例如,发生事件很少的空房间)和不充分分离的环境(例如,相邻的房间)中的共现,(2)它们要求设备具有通用传感器(例如,麦克风)来捕获上下文,这使得它们在具有异构传感器的设备上不切实际。我们解决了这些限制,提出了Next2You,一种利用信道状态信息(CSI)的新型共现检测方案。特别是,我们利用指定Wi-Fi信道的一系列子载波的幅度和相位值来捕获设备通信时创建的强大无线环境。我们在现成的智能手机上实现Next2You,只依赖于无处不在的Wi-Fi芯片组,并根据我们在五个真实场景中收集的超过95小时的CSI测量结果对其进行评估。Next2You的错误率低于4%,在低熵环境和分离程度不够的环境中都能保持准确的共现检测。我们还演示了nextyou的实时可靠工作能力及其对各种攻击的鲁棒性。
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引用次数: 3
Autonomic Security Management for IoT Smart Spaces 物联网智能空间的自主安全管理
IF 2.7 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2021-08-16 DOI: 10.1145/3466696
Chang-Yang Lin, Hamzeh Khazaei, Andrew Walenstein, A. Malton
Embedded sensors and smart devices have turned the environments around us into smart spaces that could automatically evolve, depending on the needs of users, and adapt to the new conditions. While smart spaces are beneficial and desired in many aspects, they could be compromised and expose privacy, security, or render the whole environment a hostile space in which regular tasks cannot be accomplished anymore. In fact, ensuring the security of smart spaces is a very challenging task due to the heterogeneity of devices, vast attack surface, and device resource limitations. The key objective of this study is to minimize the manual work in enforcing the security of smart spaces by leveraging the autonomic computing paradigm in the management of IoT environments. More specifically, we strive to build an autonomic manager that can monitor the smart space continuously, analyze the context, plan and execute countermeasures to maintain the desired level of security, and reduce liability and risks of security breaches. We follow the microservice architecture pattern and propose a generic ontology named Secure Smart Space Ontology (SSSO) for describing dynamic contextual information in security-enhanced smart spaces. Based on SSSO, we build an autonomic security manager with four layers that continuously monitors the managed spaces, analyzes contextual information and events, and automatically plans and implements adaptive security policies. As the evaluation, focusing on a current BlackBerry customer problem, we deployed the proposed autonomic security manager to maintain the security of a smart conference room with 32 devices and 66 services. The high performance of the proposed solution was also evaluated on a large-scale deployment with over 1.8 million triples.
嵌入式传感器和智能设备将我们周围的环境变成了智能空间,可以根据用户的需求自动进化,并适应新的条件。虽然智能空间在许多方面都是有益的和令人向往的,但它们可能会受到损害,暴露隐私和安全,或者使整个环境成为一个无法完成常规任务的敌对空间。事实上,由于设备的异构性、巨大的攻击面和设备资源的限制,确保智能空间的安全是一项非常具有挑战性的任务。本研究的主要目标是通过在物联网环境管理中利用自主计算范式,最大限度地减少强制执行智能空间安全的人工工作。更具体地说,我们努力构建一个自主管理器,可以持续监控智能空间,分析上下文,计划和执行对策,以保持所需的安全级别,并减少安全漏洞的责任和风险。我们遵循微服务架构模式,提出了一种通用本体——安全智能空间本体(SSSO),用于描述安全增强智能空间中的动态上下文信息。基于SSSO,我们构建了一个包含四层的自主安全管理器,该管理器可以持续监控被管理空间,分析上下文信息和事件,并自动规划和实现自适应安全策略。作为评估,专注于当前的黑莓客户问题,我们部署了拟议的自主安全管理器来维护一个拥有32台设备和66项服务的智能会议室的安全。在超过180万个三元组的大规模部署中,还对所提出的解决方案的高性能进行了评估。
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引用次数: 2
Cognitive Robotics on 5G Networks 5G网络上的认知机器人
IF 2.7 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2021-07-16 DOI: 10.1145/3414842
Zhihan Lv, Liang Qiao, Qingjun Wang
Emotional cognitive ability is a key technical indicator to measure the friendliness of interaction. Therefore, this research aims to explore robots with human emotion cognitively. By discussing the prospects of 5G technology and cognitive robots, the main direction of the study is cognitive robots. For the emotional cognitive robots, the analysis logic similar to humans is difficult to imitate; the information processing levels of robots are divided into three levels in this study: cognitive algorithm, feature extraction, and information collection by comparing human information processing levels. In addition, a multi-scale rectangular direction gradient histogram is used for facial expression recognition, and robust principal component analysis algorithm is used for facial expression recognition. In the pictures where humans intuitively feel smiles in sad emotions, the proportion of emotions obtained by the method in this study are as follows: calmness accounted for 0%, sadness accounted for 15.78%, fear accounted for 0%, happiness accounted for 76.53%, disgust accounted for 7.69%, anger accounted for 0%, and astonishment accounted for 0%. In the recognition of micro-expressions, humans intuitively feel negative emotions such as surprise and fear, and the proportion of emotions obtained by the method adopted in this study are as follows: calmness accounted for 32.34%, sadness accounted for 34.07%, fear accounted for 6.79%, happiness accounted for 0%, disgust accounted for 0%, anger accounted for 13.91%, and astonishment accounted for 15.89%. Therefore, the algorithm explored in this study can realize accuracy in cognition of emotions. From the preceding research results, it can be seen that the research method in this study can intuitively reflect the proportion of human expressions, and the recognition methods based on facial expressions and micro-expressions have good recognition effects, which is in line with human intuitive experience.
情感认知能力是衡量互动友好性的关键技术指标。因此,本研究旨在对具有人类情感的机器人进行认知探索。通过讨论5G技术与认知机器人的前景,研究的主要方向是认知机器人。对于情感认知机器人来说,类似人类的分析逻辑难以模仿;本研究通过对人类信息处理水平的比较,将机器人的信息处理水平分为认知算法、特征提取和信息收集三个层次。此外,采用多尺度矩形方向梯度直方图进行面部表情识别,采用鲁棒主成分分析算法进行面部表情识别。在人类在悲伤情绪中直观感受到微笑的图片中,本研究方法获得的情绪比例为:平静占0%,悲伤占15.78%,恐惧占0%,快乐占76.53%,厌恶占7.69%,愤怒占0%,惊讶占0%。在对微表情的识别中,人类直观地感受到惊讶、恐惧等负面情绪,本研究采用的方法得到的情绪比例为:冷静占32.34%,悲伤占34.07%,恐惧占6.79%,快乐占0%,厌恶占0%,愤怒占13.91%,惊讶占15.89%。因此,本研究探索的算法可以实现对情绪认知的准确性。从前面的研究结果可以看出,本研究的研究方法可以直观地反映人类表情的比例,基于面部表情和微表情的识别方法具有较好的识别效果,符合人类的直觉经验。
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引用次数: 5
Fast Video Facial Expression Recognition by a Deeply Tensor-Compressed LSTM Neural Network for Mobile Devices 基于深度张量压缩LSTM神经网络的移动设备快速视频面部表情识别
IF 2.7 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2021-07-15 DOI: 10.1145/3464941
Peining Zhen, Hai-Bao Chen, Yuan Cheng, Zhigang Ji, Bin Liu, Hao Yu
Mobile devices usually suffer from limited computation and storage resources, which seriously hinders them from deep neural network applications. In this article, we introduce a deeply tensor-compressed long short-term memory (LSTM) neural network for fast video-based facial expression recognition on mobile devices. First, a spatio-temporal facial expression recognition LSTM model is built by extracting time-series feature maps from facial clips. The LSTM-based spatio-temporal model is further deeply compressed by means of quantization and tensorization for mobile device implementation. Based on datasets of Extended Cohn-Kanade (CK+), MMI, and Acted Facial Expression in Wild 7.0, experimental results show that the proposed method achieves 97.96%, 97.33%, and 55.60% classification accuracy and significantly compresses the size of network model up to 221× with reduced training time per epoch by 60%. Our work is further implemented on the RK3399Pro mobile device with a Neural Process Engine. The latency of the feature extractor and LSTM predictor can be reduced 30.20× and 6.62× , respectively, on board with the leveraged compression methods. Furthermore, the spatio-temporal model costs only 57.19 MB of DRAM and 5.67W of power when running on the board.
移动设备通常受限于有限的计算和存储资源,这严重阻碍了深度神经网络的应用。在本文中,我们介绍了一种深度张量压缩的长短期记忆(LSTM)神经网络,用于移动设备上基于视频的快速面部表情识别。首先,通过提取人脸片段的时间序列特征映射,建立时空面部表情识别LSTM模型;基于lstm的时空模型通过量化和张张化进一步深度压缩,以便移动设备实现。基于Wild 7.0的扩展科恩-卡纳德(CK+)、MMI和动作面部表情数据集,实验结果表明,该方法的分类准确率分别达到97.96%、97.33%和55.60%,网络模型的大小显著压缩到221x,每个历元的训练时间减少了60%。我们的工作在带有神经处理引擎的RK3399Pro移动设备上进一步实现。使用杠杆压缩方法,特征提取器和LSTM预测器的延迟可以分别减少30.20倍和6.62倍。此外,时空模型在板上运行时仅消耗57.19 MB的DRAM和5.67W的功耗。
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引用次数: 8
Robust Environmental Sensing Using UAVs 利用无人机进行鲁棒环境感知
IF 2.7 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS 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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS 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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS 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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS 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
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ACM Transactions on Internet of Things
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