Pervasive and Mobile Computing最新文献

Mobility-aware Q-learning for workload offloading in vehicular edge–cloud environment 基于移动感知q学习的车辆边缘云环境下的工作负载卸载

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

Pervasive and Mobile Computing

Pub Date : 2026-01-13 DOI: 10.1016/j.pmcj.2026.102172

Afzal Badshah , Abdulrahman Ahmed Gharawi , Mona Eisa , Nada Alzaben , Saud Yonbawi , Ali Daud

The Intelligent Transportation System (ITS) continuously generates data that needs to be processed under strict latency and connectivity constraints across a heterogeneous computing architecture (e.g., Vehicular Edge Computing (VEC), Mobile Edge Computing (MEC), and Cloud Computing (CC)). In this context, efficient task offloading requires mobility and server-aware intelligence to optimize communication delay, cost, and resource utilization. In this paper, we propose a mobility-aware Q-learning offloading scheduler that learns optimal tier selection on real-time metrics (e.g., resource availability, signal strength, and Base Station (BS) handover dynamics). Unlike the previous investigation, this approach explicitly incorporates vehicle mobility patterns to the offloading decision using Q-learning. The scheduler favors VEC when underutilized, transitions to MEC when the VEC is overutilized, and falls back to the cloud only when VEC and MEC are infeasible. A structured reward model reinforces decisions that improve resource efficiency and penalizes excessive switching or skipping underutilized resources. The proposed framework is evaluated using DriveNetSim, a custom-developed vehicular simulator that models realistic mobility, signal degradation, and BS switching. Simulation results show a strong preference for VEC, with shifts to MEC only under VEC over-utilization and minimal reliance on the cloud. As a result, the system achieves up to 43% reduction in transmission delay and 38% reduction in processing cost, validating its effectiveness in dynamic vehicular environments.

智能交通系统（ITS）不断生成数据，这些数据需要在严格的延迟和连接约束下跨异构计算架构（例如，车辆边缘计算（VEC），移动边缘计算（MEC）和云计算（CC））进行处理。在这种情况下，高效的任务卸载需要机动性和服务器感知智能来优化通信延迟、成本和资源利用率。在本文中，我们提出了一个移动性感知的q -学习卸载调度程序，该调度程序根据实时指标（例如，资源可用性，信号强度和基站（BS）切换动态）学习最佳层选择。与之前的研究不同，该方法明确地将车辆移动模式结合到使用q学习的卸载决策中。调度程序在未充分利用时倾向于VEC，在VEC被过度利用时过渡到MEC，只有在VEC和MEC不可行的情况下才会回到云。结构化的奖励模式强化了提高资源效率的决策，并惩罚过度转换或跳过未充分利用的资源。所提出的框架使用DriveNetSim进行评估，DriveNetSim是一种定制开发的车辆模拟器，可以模拟现实的移动性、信号退化和BS切换。模拟结果显示，人们对VEC有强烈的偏好，只有在VEC过度利用和对云的依赖最小的情况下，才会转向MEC。结果表明，该系统的传输延迟降低了43%，处理成本降低了38%，验证了其在动态车辆环境中的有效性。

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引用次数: 0

SMOTE-Enhanced CNN-Bi-LSTM for wearable sensor-based human activity recognition 基于smote增强的CNN-Bi-LSTM可穿戴传感器的人体活动识别

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

Pervasive and Mobile Computing

Pub Date : 2026-01-08 DOI: 10.1016/j.pmcj.2026.102161

Ahmed Arafa , Hadeer Harfoush , Nawal El-Fishawy , Marwa Radad

Human Activity Recognition (HAR) refers to the automatic recognition of various human physical activities such as walking, sitting, and standing. HAR based on wearable sensors and smartphones has gained significant attention due to its wide range of applications in healthcare, sports, rehabilitation, and smart environments. Despite extensive research, challenges remain in modeling complex spatial–temporal dependencies within activity sequences and addressing class imbalance issues in sensor-based datasets. In this paper, we propose a hybrid deep learning model that integrates a Convolutional Neural Network (CNN) for spatial feature extraction followed by a Bidirectional Long Short-Term Memory (Bi-LSTM) for bi-directional sequential analysis and a fully connected layer for classifying the different types of activities. To address data imbalance and enhance the model robustness, three oversampling techniques — Random Oversampling (ROS), Adaptive Synthetic Sampling (ADASYN), and Synthetic Minority Over-sampling Technique (SMOTE) — were experimentally evaluated, with SMOTE demonstrating superior performance. The proposed model was trained and evaluated on six publicly available benchmark datasets: MHealth, PAMAP2, WISDM, UCI-HAR, USC-HAD and Opportunity datasets, achieving F1-score at 100%, 97.99%, 99.0%, 94.81%, 91.13% and 90.95% respectively. Comparative results demonstrate that the proposed framework outperforms several state-of-the-art methods across multiple datasets, confirming its robustness, reliability, and generalization capability for diverse human activity recognition scenarios.

人类活动识别（Human Activity Recognition， HAR）是指对人类行走、坐、站等各种身体活动的自动识别。基于可穿戴传感器和智能手机的HAR因其在医疗保健、运动、康复和智能环境中的广泛应用而备受关注。尽管进行了广泛的研究，但在模拟活动序列中复杂的时空依赖关系和解决基于传感器的数据集中的类不平衡问题方面仍然存在挑战。在本文中，我们提出了一种混合深度学习模型，该模型集成了用于空间特征提取的卷积神经网络（CNN）、用于双向序列分析的双向长短期记忆（Bi-LSTM）和用于分类不同类型活动的完全连接层。为了解决数据不平衡和增强模型鲁棒性，实验评估了三种过采样技术——随机过采样（ROS）、自适应合成采样（ADASYN）和合成少数过采样技术（SMOTE）， SMOTE表现出优异的性能。该模型在MHealth、PAMAP2、WISDM、UCI-HAR、USC-HAD和Opportunity 6个公开的基准数据集上进行了训练和评估，f1得分分别为100%、97.99%、99.0%、94.81%、91.13%和90.95%。对比结果表明，所提出的框架在多个数据集上优于几种最先进的方法，证实了其在不同人类活动识别场景中的鲁棒性、可靠性和泛化能力。

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引用次数: 0

MDWD-KAN: Multilevel discrete wavelet decomposition with Kolmogorov–Arnold network for fall detection and activity recognition using wearable sensors 基于Kolmogorov-Arnold网络的多电平离散小波分解可穿戴传感器跌倒检测和活动识别

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

Pervasive and Mobile Computing

Pub Date : 2026-01-06 DOI: 10.1016/j.pmcj.2026.102160

Zhiyuan Jiang , Sike Ni , Mohammed A.A. Al-qaness

Fall detection and Human Activity Recognition (HAR) are crucial applications in pervasive and mobile computing, enabling real-time monitoring of individuals – especially the elderly or patients – for enhanced safety and health management. Wearable devices have emerged as a critical tool for continuous activity monitoring, enabling real-time detection and intervention. However, the quality of data collected by wearable sensors faces several challenges, including noise interference, instability due to wearing, and individual differences. To address these challenges, this paper proposes a feature stepwise fusion detection system based on a multilevel discrete wavelet decomposition with Kolmogorov–Arnold Network, namely MDWD-KAN. This model utilizes multilevel wavelet decomposition to perform multiresolution analysis on sensor signals, extracting multilevel features and effectively enhancing feature stability and noise resistance. Additionally, through a heterogeneous model and a multilevel feature fusion strategy, MDWD-KAN achieves complementary low-frequency and high-frequency features, improving the recognition capability for complex motion patterns. Experiments were conducted on three public datasets: MobiAct, SisFall, and UniMiB-SHAR. The results show that MDWD-KAN achieves average recognition accuracies of 99.67%, 99.90%, and 99.65%, respectively, for binary classification (fall and non-fall), and 98.85%, 85.45%, and 96.86%, respectively, for multiclassification.

跌倒检测和人体活动识别（HAR）是普及和移动计算中的关键应用，能够实时监测个人，特别是老年人或患者，以加强安全和健康管理。可穿戴设备已经成为持续活动监测的关键工具，可以实现实时检测和干预。然而，可穿戴传感器收集的数据质量面临着一些挑战，包括噪声干扰、佩戴不稳定以及个体差异。为了解决这些问题，本文提出了一种基于Kolmogorov-Arnold网络的多层离散小波分解的特征逐步融合检测系统MDWD-KAN。该模型利用多级小波分解对传感器信号进行多分辨率分析，提取多级特征，有效增强特征稳定性和抗噪性。此外，MDWD-KAN通过异构模型和多层次特征融合策略，实现低频和高频特征的互补，提高了对复杂运动模式的识别能力。实验在三个公共数据集上进行：MobiAct、SisFall和unimib - share。结果表明，mddd - kan对二分类（跌倒和非跌倒）的平均识别准确率分别为99.67%、99.90%和99.65%，对多分类的平均识别准确率分别为98.85%、85.45%和96.86%。

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引用次数: 0

FedEMMD: Entropy and MMD-based data and aggregation selection for non-iid and long-tailed data in federated learning FedEMMD：联邦学习中基于熵和mmd的数据和非id和长尾数据的聚合选择

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

Pervasive and Mobile Computing

Pub Date : 2025-12-30 DOI: 10.1016/j.pmcj.2025.102159

Nafas Gul Saadat, Santhosh Kumar G.

The increasing need for privacy-preserving machine learning has rendered centralized data collection progressively unfeasible. To solve this, Federated Learning (FL) has emerged as a distributed learning paradigm in which multiple clients collectively train a shared global model while keeping all data locally, ensuring that no private data is sent over the network. However, FL is often hindered by statistical heterogeneity, where clients’ data are non-independent and identically distributed (non-iid), resulting in biased local updates and reduced global model performance. To overcome these key challenges, this study proposes FedEMMD, a novel method to enhance model performance under heterogeneous data. First, entropy-based data selection is used to identify and select high-quality data with a lower degree of non-iidness. Second, Maximum Mean Discrepancy (MMD) is used to calculate the divergence between local updates and the global model, guaranteeing that only stable and consistent updates are aggregated on the global model. Experiments have been conducted in two heterogeneous settings (non-iid and long-tailed distribution), using CIFAR-10 and CIFAR-10-LT. Additionally, we conduct experiments with centralized Machine Learning (ML) under the same settings to establish a baseline to evaluate the effect of data heterogeneity on centralized ML. The experimental results demonstrate that FedEMMD outperforms state-of-the-art algorithms such as FedAvg, FedProx, Scaffold, and FedOpt in terms of accuracy and convergence speed in both non-iid and long-tailed scenarios, thereby improving robustness and performance under heterogeneous settings.

对保护隐私的机器学习的需求日益增长，使得集中收集数据变得越来越不可行。为了解决这个问题，联邦学习（FL）作为一种分布式学习范例出现了，在这种范例中，多个客户端共同训练一个共享的全局模型，同时将所有数据保存在本地，确保没有私有数据通过网络发送。然而，FL经常受到统计异质性的阻碍，其中客户端的数据是非独立和同分布的（non-iid），导致局部更新有偏差，降低了全局模型的性能。为了克服这些关键挑战，本研究提出了一种新的方法FedEMMD来提高异构数据下的模型性能。首先，使用基于熵的数据选择来识别和选择非完整性程度较低的高质量数据。其次，利用最大平均差异（Maximum Mean difference， MMD）计算局部更新与全局模型之间的差异，保证在全局模型上只聚合稳定一致的更新。使用CIFAR-10和CIFAR-10- lt在两种异质环境下（非id分布和长尾分布）进行了实验。此外，我们在相同设置下对集中式机器学习（ML）进行了实验，以建立基线来评估数据异质性对集中式机器学习的影响。实验结果表明，FedEMMD在非id和长尾场景下的准确性和收敛速度都优于FedAvg、FedProx、Scaffold和FedOpt等最先进的算法，从而提高了异构设置下的鲁棒性和性能。

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引用次数: 0

Traffic analysis and resource adaptation in large-scale 5G multi-layer edge networks 大规模5G多层边缘网络的流量分析与资源适配

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

Pervasive and Mobile Computing

Pub Date : 2025-12-27 DOI: 10.1016/j.pmcj.2025.102158

Marcello Pietri , Natalia Selini Hadjidimitriou , Marco Mamei , Marco Picone , Enrico Rossini , Edoardo Maria Sanna , Jovanka Adzic , Andrea Buldorini

In this research, we propose automating network management through data-driven intelligence, with a particular focus on anomalies and network traffic during specific events or periods. We analyze a large dataset collected by Orange mobile network operator in France with the goal of forecasting mobile demand for different classes of services. To model the underlying network infrastructure, we introduce a model for the underlying network based on a hierarchy of virtualization layers and slices. Building on this model, we propose algorithms to optimize the resources allocated to network slices and traffic distribution within the operator’s network. Network performance is evaluated as the fraction of time the mobile traffic is within the capacity of the network. Our results demonstrate that dynamic reallocation of resources among slices, and dynamic load balancing (traffic shaping) between nodes notably improves network performance. These results provide insights into critical aspects related to future 5G network management.

在这项研究中，我们建议通过数据驱动的智能自动化网络管理，特别关注特定事件或时期的异常和网络流量。我们分析了法国Orange移动网络运营商收集的大型数据集，目的是预测不同类别服务的移动需求。为了对底层网络基础设施进行建模，我们引入了一个基于虚拟化层和切片层次结构的底层网络模型。在此模型的基础上，我们提出了优化分配给网络切片的资源和运营商网络内流量分配的算法。网络性能是用移动流量在网络容量范围内的时间比例来评估的。我们的结果表明，在片之间动态重新分配资源，以及节点之间的动态负载平衡（流量整形）显着提高了网络性能。这些结果为未来5G网络管理的关键方面提供了见解。

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引用次数: 0

Sybil-aware adaptive defence framework for robust federated learning 用于健壮联邦学习的sybil感知自适应防御框架

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

Pervasive and Mobile Computing

Pub Date : 2025-12-16 DOI: 10.1016/j.pmcj.2025.102157

Dnyanesh Khedekar , Tanmaya Mahapatra , Amitesh Singh Rajput

Federated Learning (FL) enables distributed learning of sensitive and multi-location data to enhance data privacy and avoid data leakages. In FL, participating clients only share the trained local model updates to the external server for global aggregation. However, FL is vulnerable to security threats like adaptive data poisoning attacks in non-IID data scenarios, especially coordinated attacks by colluding sybils. Existing defences struggle against sybils as their focus has been on either analysing model behaviour or strengthening aggregation mechanisms. Sybils can collude by sharing their model updates with each other in the external environment. They can then strategically manipulate and share poisoned model updates for global aggregation, specifically employing label-flipping attacks. This paper introduces a novel defence framework that shifts the focus from model analysis to sybil behaviour analysis utilizing historical pairwise cosine similarity of client updates during the training process. By establishing a dynamic threshold and analysing patterns of similarity change among participating clients, the proposed defence framework detects sybils exhibiting coordinated data poisoning and excludes them from the subsequent global aggregation process. This approach is adaptable to various underlying aggregation methods, providing a robust defence against collusive data poisoning attacks and improving model resilience and convergence even under challenging non-IID settings. Comprehensive evaluations demonstrate a significant reduction in the Attack Success Ratio, i.e. from over 40% to below 1%, showcasing its superior efficacy compared to state-of-the-art defences against targeted data poisoning attacks.

联邦学习（Federated Learning， FL）可以对敏感数据和多位置数据进行分布式学习，以增强数据的隐私性，避免数据泄露。在FL中，参与的客户端仅将经过训练的本地模型更新共享到外部服务器以进行全局聚合。然而，在非iid数据场景下，FL容易受到自适应数据中毒攻击等安全威胁，特别是由串通黑客发起的协同攻击。现有的防御与sysyls斗争，因为它们的重点要么是分析模型行为，要么是加强聚合机制。sybil可以通过在外部环境中相互共享他们的模型更新来串通起来。然后，他们可以策略性地操纵和共享有毒的模型更新，用于全局聚合，特别是使用标签翻转攻击。本文介绍了一种新的防御框架，该框架利用客户端在训练过程中更新的历史两两余弦相似性，将重点从模型分析转移到符号行为分析。通过建立动态阈值和分析参与客户端之间的相似性变化模式，所提出的防御框架检测出表现出协调数据中毒的sybils，并将其排除在随后的全局聚合过程之外。这种方法适用于各种底层聚合方法，提供了对串通数据中毒攻击的强大防御，并提高了模型的弹性和收敛性，即使在具有挑战性的非iid设置下也是如此。综合评估表明，攻击成功率显著降低，即从40%以上降至1%以下，与最先进的针对目标数据中毒攻击的防御相比，显示出其优越的功效。

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引用次数: 0

Gradient-driven exploration and pattern matching experience replay for efficient UAV path planning: Flying over or around? 高效无人机路径规划的梯度驱动探索和模式匹配经验回放：飞越还是绕飞？

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

Pervasive and Mobile Computing

Pub Date : 2025-12-14 DOI: 10.1016/j.pmcj.2025.102156

Zhengmiao Jin , Renxiang Chen , Ke Wu , Dong Liang , Li Yan

The flexibility of unmanned aerial vehicle (UAV) provides a promising solution for large-scale urban services. However, their limited energy remains one of the primary constraints affecting task efficiency. Trajectory optimization is required for energy management, as incorrect path decisions can result in lower task performance and potentially cause damage to the UAV or the urban environment. This paper investigates the path decision-making problem of UAV in dense, high-rise urban environments, characterized by optimal decisions for flying over or around obstacles to minimize energy consumption. Firstly, this study establishes a UAV energy consumption model based on the differences in energy usage across various flight states, and frames the UAV trajectory optimization problem as a Markov Decision Process (MDP), solved using the Deep Deterministic Policy Gradient (DDPG) framework. Secondly, within the Deep Reinforcement Learning (DRL) environment, when the UAV faces a choice between flying over or around obstacles, the exploration-exploitation dilemma arises due to the target-proximity-based dense reward function setup. This research proposes a three-stage learning framework, with a notable feature in the second stage, where exploration is driven by the gradient features of obstacle height to counteract excessive exploitation induced by the reward function. Finally, to address the issue of the algorithm’s experience sampling strategy neglecting the mismatch between the current state and past experiences, which arises due to the progression of the training process, this paper proposes a two-stage experience replay strategy. One notable feature of this strategy is the pattern-matching filtering method in the second stage, which selects experiences that closely match the current state for sampling, thereby accelerating the training process. Extensive simulation experiments demonstrate the necessity and effectiveness of the proposed exploration strategy and experience replay strategy.

无人机（UAV）的灵活性为大规模城市服务提供了一个很有前景的解决方案。然而，他们有限的精力仍然是影响任务效率的主要制约因素之一。轨迹优化是能源管理所必需的，因为不正确的路径决策可能导致较低的任务性能，并可能对无人机或城市环境造成损害。本文研究了密集高层城市环境中无人机的路径决策问题，该问题的特征是飞越或绕过障碍物以最小化能量消耗的最优决策。首先，基于不同飞行状态下的能量消耗差异，建立了无人机的能量消耗模型，并将无人机的轨迹优化问题框架为马尔可夫决策过程（MDP），利用深度确定性策略梯度（DDPG）框架进行求解。其次，在深度强化学习（DRL）环境中，当无人机面临飞越或绕过障碍物的选择时，由于基于目标接近度的密集奖励函数设置，导致了探索-利用困境。本研究提出了一个三阶段的学习框架，其中第二阶段的一个显著特征是，探索是由障碍物高度的梯度特征驱动的，以抵消奖励函数引起的过度开发。最后，为了解决算法的经验采样策略忽略当前状态和过去经验之间不匹配的问题，本文提出了一种两阶段的经验重播策略。该策略的一个显著特点是第二阶段的模式匹配滤波方法，该方法选择与当前状态密切匹配的经验进行采样，从而加快了训练过程。大量的仿真实验证明了所提出的勘探策略和经验回放策略的必要性和有效性。

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引用次数: 0

A self-adaptive framework for child healthcare in IoT environment using AI-based prediction 基于人工智能预测的物联网环境下儿童医疗自适应框架

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

Pervasive and Mobile Computing

Pub Date : 2025-12-04 DOI: 10.1016/j.pmcj.2025.102153

Euijong Lee , Jaemin Jeong , Gyuchan Jo , Taegyeom Lee , Gee-Myung Moon , Young-Duk Seo , Ji-Hoon Jeong

Childhood overweight and obesity have emerged as some of the most serious global public health challenges, as they can lead to a variety of health-related problems and the early development of chronic diseases. The healthcare domain has been transformed by the integration of the Internet of Things (IoT), leading to the development of digital healthcare solutions. This integration has led to an increase in the health data collected from a variety of IoT sources. Consequently, advanced technologies are required to analyze health data, and Artificial Intelligence (AI) has been employed to extract meaningful insights from the data. Moreover, these technologies can be effectively applied in healthcare to address childhood overweight and obesity. A self-adaptive framework is proposed to manage childhood weight using lifelog data from IoT environments. An ensemble-based learning model is applied to predict weight using the lifelog data. A smartphone application providing real-world services was developed, and lifelog data were collected from a cohort of 362 children aged 104 to 152 months. Reasonable results were obtained, indicating that the proposed ensemble model can predict childhood weight change using lifelog data with an accuracy of 0.9711 and an F1-score 0.9725. Also, the results indicated that appropriate rewards can encourage human involvement and positively influence data quality.The results demonstrated the efficiency of the proposed framework with human involvement in weight prediction. The experimental results demonstrate the efficiency of the proposed framework and its potential application in healthcare services to enhance children’s health.

儿童超重和肥胖已成为一些最严重的全球公共卫生挑战，因为它们可导致各种与健康有关的问题和慢性病的早期发展。物联网（IoT）的集成已经改变了医疗保健领域，从而导致了数字医疗保健解决方案的发展。这种整合导致了从各种物联网来源收集的健康数据的增加。因此，需要先进的技术来分析健康数据，并使用人工智能（AI）从数据中提取有意义的见解。此外，这些技术可以有效地应用于医疗保健，以解决儿童超重和肥胖问题。提出了一种自适应框架，利用物联网环境中的生活日志数据来管理儿童体重。采用基于集成的学习模型，利用生活日志数据预测体重。研究人员开发了一款提供真实世界服务的智能手机应用程序，并收集了362名年龄在104至152个月之间的儿童的生活日志数据。结果表明，本文提出的集成模型可以利用生活日志数据预测儿童体重变化，准确率为0.9711，f1得分为0.9725。此外，结果表明，适当的奖励可以鼓励人的参与，并积极影响数据质量。结果表明，在人类参与权重预测的情况下，所提出的框架是有效的。实验结果证明了该框架的有效性及其在医疗保健服务中提高儿童健康水平的潜在应用。

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引用次数: 0

The structure design of the smart sock prototype integrated with stretchable hybrid electronic temperature sensing yarn for real-time temperature monitoring 结合可拉伸混合电子感温纱进行实时温度监测的智能袜子原型的结构设计

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

Pervasive and Mobile Computing

Pub Date : 2025-11-26 DOI: 10.1016/j.pmcj.2025.102136

Sumonta Ghosh , Fenye Meng , Rony Shaha , Jiyong Hu

Rapid advances in electronic textiles have enabled the development of smart socks with temperature-sensing capabilities for real-time foot temperature monitoring, where stretchability, user comfort, durability, bending, washability, sensing location error during wear, and higher manufacturing cost are drawbacks. This study introduces a smart sock prototype that integrates highly stretchable hybrid electronic temperature-sensing (SHETS) yarns in key physiological foot regions—hallux, metatarsal, midfoot, and heel—to detect temperature variations and address the challenges of existing and commercial products. Structural design of SHETS yarns involved wrapping, novel interconnection, encapsulation, and braiding techniques to integrate miniature thermistor within the yarn structure, showing high stretchability, durability, washability, and sensing accuracy. Low-power microcontroller transmits analog data to a digital format, and the web-based interface enables users to monitor data through mobile phone applications in real-time. The prototype demonstrates high accuracy, durability, and reliability under various conditions, with average temperature error ranging from ±0.23 °C to ±0.27 °C. The prototype maintains durability and stability in hot, cold, and sweaty conditions. Physical activities like walking, running, and cycling demonstrate the durability and stability of foot temperature changes, while extended wear shows low power consumption and stability. The device withstands over 30 washing cycles with minimal accuracy loss (maximum ±0.28 °C error) and retains the mechanical and electrical properties of SHETS yarn under repeated stretching and bending. Additionally, the android-based intelligent foot alert system enhances usability by providing real-time monitoring and alerts on smartphones, offering a cost-effective, energy-efficient, and user-friendly solution for proactive foot health management. Available code: https://github.com/Sumonta-e-textile/Smart-Materials-and-Electronic-Textiles-Lab.

电子纺织品的快速发展使得具有温度传感功能的智能袜子能够进行实时足部温度监测，其中拉伸性，用户舒适性，耐用性，弯曲性，可洗涤性，磨损时的传感位置误差以及较高的制造成本是缺点。本研究介绍了一种智能袜子原型，该原型将高度可拉伸的混合电子温度传感（SHETS）纱线集成在脚的关键生理区域-拇趾，跖骨，足中部和脚跟-以检测温度变化并解决现有和商业产品的挑战。shts纱线的结构设计采用包绕、新型互连、封装和编织技术，将微型热敏电阻集成到纱线结构中，具有高拉伸性、耐久性、耐洗性和传感精度。低功耗微控制器将模拟数据传输为数字格式，基于web的界面使用户可以通过手机应用程序实时监控数据。该样机在各种条件下具有高精度、耐用性和可靠性，平均温度误差范围为±0.23°C至±0.27°C。原型在热、冷和出汗的条件下保持耐用性和稳定性。步行、跑步和骑自行车等体育活动表现出足部温度变化的耐久性和稳定性，而长时间穿着则表现出低功耗和稳定性。该设备可承受超过30次洗涤循环，精度损失最小（最大±0.28°C误差），并在重复拉伸和弯曲下保持SHETS纱线的机械和电气性能。此外，基于android的智能足部警报系统通过在智能手机上提供实时监控和警报，提高了可用性，为主动足部健康管理提供了经济高效、节能和用户友好的解决方案。可用代码：https://github.com/Sumonta-e-textile/Smart-Materials-and-Electronic-Textiles-Lab。

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引用次数: 0

Edge computing and 5G network integration for mobility-aware service deployments 边缘计算和5G网络集成，用于移动感知服务部署

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

Pervasive and Mobile Computing

Pub Date : 2025-11-24 DOI: 10.1016/j.pmcj.2025.102134

João Gameiro , Rodrigo Rosmaninho , Gonçalo Perna , Pedro Rito , Susana Sargento , Carlos Marques , Filipe Pinto

The growing scale of smart city sensing devices and infrastructure entails a wide variety of available sensing information that can provide valuable insights into user mobility and traffic congestion. This information can be used to optimize service delivery through the development of mobility-aware services. 5G systems and their associated technologies provide an ideal environment with capabilities to efficiently support edge computing and bring the processing and storage resources closer to the end users, which results in a latency and backhaul usage reduction.

This article proposes the integration of edge computing in 5G operator network and a mobility/road-side infrastructure with edge orchestration to provide mobility-aware services to the end-users on demand. With this approach, a service instantiation can be translated into resource allocation both on the 5G platform through multi-slicing and the edge infrastructure. Resource management is then optimized for the users on the move by continuously allocating the necessary virtual network slices, processing, and storage resources in the appropriate locations for the user to consume its services while maintaining the appropriate QoS levels and optimized resource distribution in the edge platform. This approach is evaluated in a real mobile 5G network with emulated Radio Access Network (RAN) resources through two use cases based on infotainment and emergency services. The results show that the approach is efficient in using mobility, service requirements, and platform’s resources information to enable a proactive resource reservation both in the 5G base stations and edge computing nodes throughout the path traversed by the users.

智能城市传感设备和基础设施的规模不断扩大，需要各种各样的可用传感信息，这些信息可以为用户移动性和交通拥堵提供有价值的见解。这些信息可用于通过开发移动感知服务来优化服务交付。5G系统及其相关技术提供了一个理想的环境，能够有效地支持边缘计算，并使处理和存储资源更接近最终用户，从而减少延迟和回程使用。本文提出在5G运营商网络中集成边缘计算和具有边缘编排的移动/道路侧基础设施，以按需为最终用户提供移动感知服务。通过这种方法，可以通过多切片和边缘基础设施将服务实例化转换为5G平台上的资源分配。然后，通过在适当的位置持续分配必要的虚拟网络切片、处理和存储资源，以便用户使用其服务，同时在边缘平台中保持适当的QoS级别和优化的资源分配，从而为移动中的用户优化资源管理。通过基于信息娱乐和应急服务的两个用例，在具有模拟无线接入网（RAN）资源的真实移动5G网络中对该方法进行了评估。结果表明，该方法能够有效地利用移动性、业务需求和平台的资源信息，在用户走过的整个路径上实现5G基站和边缘计算节点的主动资源预留。

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