Pervasive and Mobile Computing最新文献_第4页

WiKAN: Lightweight Kolmogorov–Arnold Networks for accurate indoor WiFi localization WiKAN：轻量级Kolmogorov-Arnold网络，用于精确的室内WiFi定位

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-10-04 DOI: 10.1016/j.pmcj.2025.102121

Yunlong Gu , Meng Xu , Jiguang Li , Qilei Li , Zhao Huang , Mengshan Li , Lixin Guan , Mikko Valkama

With the growing demand for location-based services, WiFi localization plays a critical role in indoor environments. While most existing methods rely on Multi-Layer Perceptrons (MLPs), these models often suffer from limited accuracy and poor generalization across diverse deployment conditions. Kolmogorov–Arnold Networks (KANs), with their B-spline-based basis functions, better capture complex nonlinear relationships while reducing overfitting risks. However, original KANs still incur high computational costs. To address this, we propose WiKAN(WiFi KAN), a lightweight KAN-based model for indoor WiFi localization. WiKAN reduces computational complexity by simplifying the network structure to just two KANLinear layers and replacing parameter-intensive operations with optimized matrix multiplications using reconstructed basis functions. Compared to conventional computation of basis coefficients, matrix operations enable faster inference on modern hardware and improve scalability. Furthermore, WiKAN integrates SiLU and B-spline activations through a learnable linear combination, balancing smooth approximation and nonlinear representation. Experiments on three benchmark datasets (UJIIndoorLoc, Tampere, and JARIL) demonstrate that WiKAN achieves superior performance to both MLP and standard KAN models: over 99.9% building accuracy, up to 100% floor classification, and average positioning error reduced to 5.91 meters. Additionally, runtime analysis and parameter count comparisons confirm the model’s computational efficiency. Code is publicly available at: https://github.com/gyl555666/WiKAN.

随着定位服务需求的不断增长，WiFi定位在室内环境中发挥着至关重要的作用。虽然大多数现有的方法依赖于多层感知器（mlp），但这些模型在不同的部署条件下往往存在精度有限和泛化能力差的问题。Kolmogorov-Arnold网络（KANs），其基于b样条的基函数，更好地捕捉复杂的非线性关系，同时降低过拟合风险。然而，原始的KANs仍然会产生很高的计算成本。为了解决这个问题，我们提出了WiKAN(WiFi KAN)，这是一种轻量级的基于KAN的室内WiFi定位模型。WiKAN通过将网络结构简化为两个KANLinear层，并用重构基函数优化矩阵乘法取代参数密集型操作，从而降低了计算复杂度。与传统的基系数计算相比，矩阵运算可以在现代硬件上更快地进行推理并提高可扩展性。此外，WiKAN通过可学习的线性组合集成了SiLU和b样条激活，平衡了光滑逼近和非线性表示。在UJIIndoorLoc、Tampere和JARIL三个基准数据集上的实验表明，WiKAN在MLP和标准KAN模型上都取得了卓越的性能：超过99.9%的建筑精度，高达100%的楼层分类，平均定位误差降低到5.91米。此外，运行时分析和参数计数比较证实了模型的计算效率。代码可在https://github.com/gyl555666/WiKAN公开获取。

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引用次数: 0

Construction of Wi-Fi fingerprint database based on WGAN-PSO: A method to alleviate signal sparsity and environmental noise 基于WGAN-PSO的Wi-Fi指纹库构建：一种缓解信号稀疏性和环境噪声的方法

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-10-01 DOI: 10.1016/j.pmcj.2025.102120

Heng Xu, Fanyu Meng, Long Sun, Hui Shao, Cheng Wang

To address signal sparsity and environmental noise in offline fingerprint database construction, this paper proposes a collaborative optimization method that integrates Wasserstein Generative Adversarial Network (WGAN) and Particle Swarm Optimization (PSO). First, the target area is gridded, and mobile-collected coordinates/RSSI data form the base fingerprint database. WGAN then expands the fingerprints of the sparse region under geographic boundary constraints, enhancing data diversity and spatial coverage. Finally, PSO optimizes the parameters of the path loss model through a comprehensive objective function that minimizes the RSSI estimation error. The experiments were carried out in two scenarios, in which 4 AP nodes were deployed, respectively. Data from 114 and 99 reference points were collected, generating 338,710 and 517,332 fingerprint data entries, respectively. The results demonstrate that the optimized database retains the original data features while reducing the positioning error caused by multipath effects and signal fading. Compared to traditional methods, RMSE is improved by 7.16% and 2.46% in two distinct scenarios, validating the efficacy of the proposed co-optimization framework.

针对离线指纹数据库构建中的信号稀疏性和环境噪声问题，提出了一种融合Wasserstein生成对抗网络（WGAN）和粒子群算法（PSO）的协同优化方法。首先，对目标区域进行网格化，并将手机采集的坐标/RSSI数据组成基本指纹数据库。然后，WGAN在地理边界约束下扩展稀疏区域的指纹，增强数据的多样性和空间覆盖。最后，粒子群算法通过综合目标函数对路径损失模型的参数进行优化，使RSSI估计误差最小。实验分为两种场景，分别部署4个AP节点。收集了114个和99个参考点的数据，分别生成了338,710和517,332个指纹数据条目。结果表明，优化后的数据库在保留原始数据特征的同时，减小了多径效应和信号衰落引起的定位误差。与传统方法相比，在两种不同的场景下，RMSE分别提高了7.16%和2.46%，验证了所提出的协同优化框架的有效性。

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引用次数: 0

Home activity recognition using infrequently-monitored HEMS Data 使用不经常监测的医疗卫生系统数据进行家庭活动识别

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-09-27 DOI: 10.1016/j.pmcj.2025.102119

Fukuharu Tanaka , Teruhiro Mizumoto , Hirozumi Yamaguchi

This paper proposes a method for estimating household activities based only on the cumulative power consumption data obtained from the HEMS home distribution board every 30 min. The proposed method predicts the activity of each 30 min timeslot from the eight activity labels; household-level waking-up, household-level going-to-bed, room-level waking-up, room-level going-to-bed, cooking, laundry, dishwashing, and bathing. For the prediction, we first identify the branch circuit that is strongly correlated with each activity label and detect the turn-on/off of home appliances on the circuit to detect those activities. We also incorporate machine learning for estimating the other activities based on the circuit’s time series of power consumption. Furthermore, to cope with the difference among households, we apply transfer learning to the constructed model. In collaboration with a Japanese home builder, we conducted an experiment on five households using their HEMS data. In parallel, we obtained verifiable activity labels as our ground truth by the installation of specialized sensors in the respective homes. Under a ±30 min tolerance (i.e. allowing a prediction in the immediately preceding or following half-hour slot), our model achieved an average F1 score of 0.689 across all activities. We also confirmed that transfer learning improved the F1 score of each activity recognition and achieved an average improvement of 0.260 in household-level waking-up, household-level going-to-bed, room-level waking-up, room-level going-to-bed, and bathing activities.

本文提出了一种仅基于HEMS家庭配电板每30分钟获得的累计电力消耗数据来估计家庭活动的方法。该方法从8个活动标签中预测每个30分钟时间段的活动；家庭级别的起床，家庭级别的睡觉，房间级别的起床，房间级别的睡觉，做饭，洗衣，洗碗，洗澡。为了预测，我们首先确定与每个活动标签密切相关的分支电路，并检测电路上家用电器的开关以检测这些活动。我们还结合了机器学习来估计基于电路功耗时间序列的其他活动。此外，为了处理家庭之间的差异，我们将迁移学习应用于所构建的模型。我们与一家日本房屋建筑商合作，对五户家庭进行了实验，使用他们的医疗卫生系统数据。与此同时，我们通过在各自的家庭中安装专门的传感器，获得了可验证的活动标签作为我们的基础事实。在±30分钟的误差范围内（即允许在之前或之后的半小时时段进行预测），我们的模型在所有活动中获得了0.689的平均F1分数。我们还证实，迁移学习提高了各项活动识别的F1得分，在家庭级起床、家庭级上床睡觉、房间级起床、房间级上床睡觉和洗澡活动中平均提高了0.260。

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引用次数: 0

On-device indoor place prediction using WiFi-RTT and inertial sensors 使用WiFi-RTT和惯性传感器的设备室内位置预测

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-09-22 DOI: 10.1016/j.pmcj.2025.102118

Pritam Sen , Xiaopeng Jiang , Qiong Wu , Manoop Talasila , Wen-Ling Hsu , Cristian Borcea

High-accuracy and low-latency indoor place prediction for mobile users can enable a wide range of applications for domains such as assisted living and smart homes. In this paper, we propose GoPlaces, a practical indoor place prediction system that works on mobile devices without requiring any new infrastructure. GoPlaces does not rely on servers or specialized localization infrastructure, except for a single cheap off-the-shelf WiFi access point that supports ranging with Round Trip Time (RTT) protocol. GoPlaces enables personalized place naming and prediction, and it protects users’ location privacy. It fuses inertial sensor data with distances estimated using the WiFi-RTT protocol to predict the indoor places a user will visit. GoPlaces employs an attention-based BiLSTM model to detect user’s current trajectory, which is then used together with historical information stored in a prediction tree to infer user’s future places. We implemented GoPlaces in Android and evaluated it in several indoor spaces. The experimental results demonstrate prediction accuracy as high as 86%. Furthermore, they show GoPlaces is feasible in real life because it has low latency and low resource consumption on the phones.

移动用户的高精度和低延迟室内位置预测可以为辅助生活和智能家居等领域提供广泛的应用。在本文中，我们提出了GoPlaces，这是一个实用的室内位置预测系统，可以在移动设备上工作，而不需要任何新的基础设施。GoPlaces不依赖于服务器或专门的定位基础设施，除了一个廉价的现成WiFi接入点，它支持往返时间（RTT）协议。GoPlaces支持个性化的地点命名和预测，并保护用户的位置隐私。它将惯性传感器数据与使用WiFi-RTT协议估计的距离融合在一起，以预测用户将访问的室内位置。GoPlaces采用基于注意力的BiLSTM模型来检测用户当前的轨迹，然后将其与存储在预测树中的历史信息一起使用，以推断用户未来的位置。我们在Android上实现了GoPlaces，并在几个室内空间中进行了评估。实验结果表明，预测准确率高达86%。此外，他们还表明，GoPlaces在现实生活中是可行的，因为它在手机上具有低延迟和低资源消耗。

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引用次数: 0

Coordinated Q-learning based Multi-hop Routing for UAV-assisted communication 基于协同q学习的无人机辅助通信多跳路由

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-09-15 DOI: 10.1016/j.pmcj.2025.102105

N.P. Sharvari , Dibakar Das , Jyotsna Bapat , Debabrata Das

Unmanned Aerial Vehicle (UAV) assisted communication is gaining prominence as a vital solution for establishing effective emergency communication during disaster management operations. UAVs are essential for enhancing and expanding communication systems, acting as relays to boost data transmission to ground stations, extend network coverage, and provide connectivity. However, the dynamic and resource-limited nature of aerial networks necessitates robust routing mechanisms to facilitate seamless data dissemination. While existing Q-learning-based routing protocols are adaptive to changing network conditions and resilient to failures, they often lead to suboptimal network-wide decisions due to UAVs operating independently, each maximizing its gains. This paper proposes a novel Coordinated Q-learning-based Multi-hop Routing (CQMR) algorithm for multi-UAV networks. To the best of our knowledge, this is the first time a routing algorithm introduces UAV coordination for data routing through utility function approximation with a message-passing scheme, enabling the selection of globally optimal joint actions. This novel approach meticulously considers a comprehensive set of parameters for data routing, including minimizing the expected number of hops to the destination, monitoring energy usage, maintaining network connectivity, preventing UAV collisions, and supporting adaptive network reorganization. This integrated consideration of multiple factors positions the proposed solution as superior to existing work, offering a uniquely robust and highly effective strategy for UAV-assisted communication in dynamic, resource-constrained environments, such as emergency scenarios. CQMR builds upon and extends the Improved Q-learning-based Multi-hop Routing (IQMR) algorithm, demonstrating a 12.47% increase in energy efficiency and a 13.34% higher success rate in data transmission compared to IQMR while requiring 40% fewer hops to reach the destination.

无人机（UAV）辅助通信作为在灾害管理行动中建立有效应急通信的重要解决方案，正日益受到重视。无人机对于增强和扩展通信系统至关重要，充当中继器，促进数据传输到地面站，扩展网络覆盖范围，并提供连接。然而，空中网络的动态性和资源有限性需要强大的路由机制来促进无缝数据传播。虽然现有的基于q学习的路由协议可以适应不断变化的网络条件，并且能够适应故障，但由于无人机独立运行，每个无人机的收益最大化，它们通常会导致网络范围内的次优决策。针对多无人机网络，提出了一种基于协同q学习的多跳路由算法。据我们所知，这是路由算法首次通过消息传递方案的效用函数近似引入无人机协调数据路由，从而实现全局最优联合动作的选择。这种新颖的方法仔细考虑了数据路由的一组综合参数，包括最小化到目的地的预期跳数，监控能源使用，维护网络连接，防止无人机碰撞，并支持自适应网络重组。综合考虑多种因素，提出的解决方案优于现有工作，为在动态、资源受限环境（如紧急情况）下的无人机辅助通信提供了一种独特、强大和高效的策略。CQMR建立并扩展了基于改进q学习的多跳路由（IQMR）算法，与IQMR相比，能效提高了12.47%，数据传输成功率提高了13.34%，到达目的地所需的跳数减少了40%。

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引用次数: 0

IDENTIFY: Intelligent device identification using device fingerprints and machine learning 识别：使用设备指纹和机器学习的智能设备识别

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-09-15 DOI: 10.1016/j.pmcj.2025.102103

Liwei Liu , Muhammad Ajmal Azad , Harjinder Lallie , Hany Atlam

The Internet of Things (IoT) consists of a rapidly growing network of heterogeneous devices that autonomously monitor, collect, and exchange data across a wide range of application domains. The rapid increase of IoT devices highlighted the importance of scalable, secure, and adaptive network management strategies for dynamic networks. A key challenge in this context is the automatic identification of devices, which is critical for detecting and mitigating malicious devices that can compromise network integrity. Accurate device identification strengthens the security of dynamic IoT environments by facilitating early detection of anomalous or adversarial traffic. Device fingerprinting offers a non-intrusive solution by leveraging protocol and traffic characteristics, without relying on vendor-specific identifiers. In this work, we propose a lightweight and efficient framework for IoT device identification based on machine learning. Our model utilises a Random Forest classifier in conjunction with a data-driven feature selection strategy that emphasises low-overhead features derived from packet headers and traffic flow statistics. The proposed approach achieves high classification performance, attaining 97.32% accuracy in identifying general device categories and 94.39% accuracy for specific device types. It also demonstrates approximately a 40% improvement in computational efficiency compared to traditional classifiers, making it well-suited for deployment in resource-constrained edge environments. We evaluate the model under various real-world conditions, including spatiotemporal traffic variations, changes in operational modes, and different sampling intervals. Comparative experiments with established classifiers—such as J48, SMO, BayesNet, and Naive Bayes—are performed using standard metrics, including precision, recall, F1-score, and inference latency. Our approach strengthens network security by automatically identifying and classifying IoT devices in dynamic, heterogeneous environments. It is lightweight, scalable, and well-suited for deployment in resource-constrained IoT scenarios.

物联网（IoT）由快速增长的异构设备网络组成，这些设备在广泛的应用领域中自主监控、收集和交换数据。物联网设备的快速增长凸显了可扩展、安全和自适应网络管理策略对动态网络的重要性。在这种情况下的一个关键挑战是设备的自动识别，这对于检测和减轻可能危及网络完整性的恶意设备至关重要。准确的设备识别通过促进早期发现异常或敌对流量来增强动态物联网环境的安全性。设备指纹识别通过利用协议和流量特征提供了一种非侵入性的解决方案，而不依赖于特定于供应商的标识符。在这项工作中，我们提出了一个基于机器学习的轻量级高效物联网设备识别框架。我们的模型将随机森林分类器与数据驱动的特征选择策略相结合，该策略强调来自数据包头和交通流量统计的低开销特征。该方法具有较高的分类性能，识别一般设备类别的准确率为97.32%，识别特定设备类型的准确率为94.39%。与传统分类器相比，它的计算效率提高了大约40%，使其非常适合在资源受限的边缘环境中部署。我们在不同的现实条件下评估了该模型，包括时空交通变化、操作模式变化和不同的采样间隔。与已建立的分类器（如J48、SMO、BayesNet和朴素贝叶斯）进行比较实验，使用标准指标，包括精度、召回率、f1分数和推理延迟。我们的方法通过自动识别和分类动态异构环境中的物联网设备来增强网络安全性。它轻量级、可扩展，非常适合在资源受限的物联网场景中部署。

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引用次数: 0

TDoA localization in wireless sensor networks using constrained total least squares, Newton’s methods, and alternating direction method of multipliers 基于约束总最小二乘、牛顿法和乘法器交替方向法的无线传感器网络TDoA定位

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-09-09 DOI: 10.1016/j.pmcj.2025.102108

Bamrung Tausiesakul, Krissada Asavaskulkiet

An important service in the wireless systems for human daily life is the information of a mobile user’s location. Wireless sensor network is a structure that can be deployed to determine a mobile user position. Time-difference-of-arrival (TDoA) technique is often considered for wireless localization due to the low cost of the sensor network. In this work, three new Newton’s methods are proposed for computing the constrained total least squares solution in TDoA localization. Numerical simulation is conducted to demonstrate the performance of the three proposed techniques. It is found that most of them can provide better performance, in terms of about 30% lower bias and root mean square error, approximately 50% to 75% less computational time, and around 50% more reliability, than the former Newton-based algorithms.

无线系统中为人类日常生活提供的一项重要服务是移动用户的位置信息。无线传感器网络是一种可以部署以确定移动用户位置的结构。由于传感器网络的低成本，TDoA技术常被用于无线定位。本文提出了三种新的牛顿方法来计算TDoA定位中的约束总最小二乘解。通过数值模拟验证了这三种方法的性能。研究发现，与以前的基于牛顿的算法相比，大多数算法可以提供更好的性能，偏差和均方根误差降低约30%，计算时间减少约50%至75%，可靠性提高约50%。

引用次数: 0

Optimization of safety energy efficiency of alternating relay communication systems for UAVs 无人机交变中继通信系统安全能效优化

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-09-09 DOI: 10.1016/j.pmcj.2025.102110

Jianbin Xue, Qingdou Chen, Xiangrui Guan, Han Zhang

The UAV alternate relay communication system shows significant advantages in the field of information transmission, as it efficiently transmits information from the sending end to the receiving end through the cooperative work of two UAVs, effectively improving the band utilization. However, this system also faces two major challenges: first, due to the limited energy on-board the UAVs, how to effectively improve the energy efficiency has become a key issue; second, there may be malicious eavesdroppers during the information transmission process, making the information security issue not to be ignored. In order to address the above issues, this paper explores a model for an alternate relay communication system for UAVs in the presence of eavesdroppers. Our aim is to improve the energy efficiency of the system by means of optimization while ensuring information security. To this end, this paper studies the joint optimization problem of the transmit power and the UAV trajectory, aiming to maximize the safety energy efficiency of the system. To solve this complex optimization problem, we first formalize it as a nonconvex mixed integer nonlinear fractional programming (MINLFP) problem. Since it is extremely challenging to solve such a problem directly, we further decompose it into more tractable optimization subproblems and propose a set of efficient iterative methods for solving it. Simulation experimental outcomes indicate that as compared to the baseline scheme, our proposed algorithm not only excels in convergence, but also significantly enhances the safety energy efficiency. In summary, the research in this paper not only proposes an effective solution to the energy efficiency and information security problems in the UAV alternate relay communication as well as improves the overall performance of the system through algorithm optimization, which provides valuable references for research in related fields.

无人机交替中继通信系统在信息传输领域具有显著的优势，通过两架无人机的协同工作，将信息从发送端高效地传输到接收端，有效地提高了频段利用率。然而，该系统也面临两大挑战：一是由于无人机机载能量有限，如何有效提高能效成为关键问题；其次，在信息传输过程中可能存在恶意窃听者，使得信息安全问题不容忽视。为了解决上述问题，本文探讨了存在窃听者的无人机备用中继通信系统模型。我们的目标是在确保信息安全的同时，通过优化的方式提高系统的能源效率。为此，本文研究了发射功率与无人机轨迹的联合优化问题，以实现系统的安全能效最大化。为了解决这个复杂的优化问题，我们首先将其形式化为一个非凸混合整数非线性分式规划问题。由于直接求解这类问题极具挑战性，我们进一步将其分解为更易于处理的优化子问题，并提出了一组高效的迭代求解方法。仿真实验结果表明，与基线方案相比，本文提出的算法不仅收敛性好，而且显著提高了安全能效。综上所述，本文的研究不仅有效解决了无人机交替中继通信中的能效和信息安全问题，而且通过算法优化提高了系统的整体性能，为相关领域的研究提供了有价值的参考。

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引用次数: 0

AerialDB: A federated peer-to-peer spatio-temporal edge datastore for drone fleets AerialDB：无人机编队的联邦点对点时空边缘数据存储

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-09-02 DOI: 10.1016/j.pmcj.2025.102109

Shashwat Jaiswal , Suman Raj , Subhajit Sidhanta , Yogesh Simmhan

Recent years have seen an unprecedented explosion in research that leverages the newest computing paradigm of Internet of Drones comprised of a fleet of connected Unmanned Aerial Vehicles (UAVs) used for a wide range of tasks such as monitoring and analytics in highly mobile and changing environments characteristic of disaster regions. Given that the typical data (i.e., videos and images) collected by the fleet of UAVs deployed in such scenarios can be considerably larger than what the onboard computers can process, the UAVs need to offload their data in real-time to the edge and the cloud for further processing. To that end, we present the design of AerialDB- a lightweight decentralized data storage and query system that can store and process time series data on a multi-UAV system comprising: (A) a fleet of hundreds of UAVs fitted with onboard computers, and (B) ground-based edge servers connected through a cellular link. Leveraging lightweight techniques for content-based replica placement and indexing of shards, AerialDB has been optimized for efficient processing of different possible combinations of typical spatial and temporal queries performed by real-world disaster management applications. Using containerized deployment spanning up to 400 drones and 80 edges, we demonstrate that AerialDB is able to scale efficiently while providing near real-time performance with different realistic workloads. Further, AerialDB comprises a decentralized and locality-aware distributed execution engine which provides graceful degradation of performance upon edge failures with relatively low latency while processing large spatio-temporal data. AerialDB exhibits comparable insertion performance and 100 times improvement in query performance against state-of-the-art baseline. Moreover, it experiences a 10 times improvement in performance with insertion workloads and 100 times improvement with query workloads over the cloud baseline.

近年来，利用无人机互联网的最新计算范式的研究出现了前所未有的爆炸式增长，该范式由一组连接的无人机（uav）组成，用于广泛的任务，例如在高度移动和不断变化的灾区环境中进行监测和分析。考虑到在这种情况下部署的无人机编队收集的典型数据（即视频和图像）可能比机载计算机可以处理的数据大得多，无人机需要将其数据实时卸载到边缘和云端进行进一步处理。为此，我们提出了AerialDB的设计，这是一个轻量级的分散数据存储和查询系统，可以在多无人机系统上存储和处理时间序列数据，该系统包括：(a)配备机载计算机的数百架无人机的机队，以及(B)通过蜂窝链路连接的地面边缘服务器。利用基于内容的副本放置和碎片索引的轻量级技术，AerialDB已经进行了优化，可以有效地处理现实世界灾难管理应用程序执行的典型空间和时间查询的不同可能组合。通过使用多达400架无人机和80个边缘的容器化部署，我们证明了AerialDB能够有效扩展，同时在不同的实际工作负载下提供接近实时的性能。此外，AerialDB包括一个分散的、位置感知的分布式执行引擎，在处理大型时空数据时，它在边缘故障时以相对较低的延迟提供优雅的性能降级。AerialDB展示了相当的插入性能和100倍于最新基线的查询性能提升。此外，在云基准上，插入工作负载的性能提高了10倍，查询工作负载的性能提高了100倍。

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引用次数: 0

Minimizing communication-computing energy consumption for UAV assisted collaborative computing offloading 最小化无人机辅助协同计算卸载的通信计算能耗

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-08-23 DOI: 10.1016/j.pmcj.2025.102104

Zhiqi Li, Qing Wei, Wenle Bai

Unmanned aerial vehicles (UAVs) are viewed as a potential technology for handling user offloading duties as edge nodes. With their adaptable qualities, UAVs may be quickly deployed to useful locations and service consumers. However, the inability of UAVs to operate continuously for an extended time is a challenge for the current UAV-assisted mobile edge computing solutions. We put forth an optimization problem that involves the dynamic division of computational windows for UAVs, the optimization of user grouping and user transmission power, and the optimization of UAV deployment locations to save energy. We design a Communication-Computing Resource Scheduling with Dynamic computational Window allocation (CCRS-DW) algorithm to realize the problem decomposition and optimization. Specifically, the

K

-means clustering technique and the bisection search are used to tackle this problem. Simulation results show that the energy consumption of the proposed CCRS-DW scheme is significantly lower than that of other benchmark schemes.

无人驾驶飞行器（uav）被视为处理用户卸载任务作为边缘节点的潜在技术。凭借其适应性，无人机可以快速部署到有用的位置并为消费者服务。然而，无人机无法长时间连续运行是当前无人机辅助移动边缘计算解决方案面临的挑战。提出了无人机计算窗口的动态划分、用户分组和用户传输功率的优化、无人机部署位置的优化等优化问题。设计了一种基于动态计算窗口分配的通信-计算资源调度算法（CCRS-DW），实现了问题的分解和优化。具体来说，使用k均值聚类技术和二分搜索来解决这个问题。仿真结果表明，所提出的CCRS-DW方案能耗显著低于其他基准方案。

引用次数: 0