Internet of Things最新文献_第5页

Snip-Cache: A code snippet caching system for LLM-based command-driven IoT systems snippet - cache：用于基于llm的命令驱动物联网系统的代码片段缓存系统

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-13 DOI: 10.1016/j.iot.2025.101852

Chiwon Song , Sooyong Kang

Large language models (LLMs) are widely used in real-time interface systems that process user commands. Despite their high output quality, the long response times and substantial operating costs undermine the practicality and sustainability of LLM-based services. Prompt caching is one of the optimization techniques introduced to mitigate the problem. It avoids redundant processing of repetitive prompts by caching and reusing the response for the same or similar prompts. However, such a static caching scheme has an intrinsic limitation, in terms of the reusability of results, due to the variety of expressions having the same semantics in real-world usage environments. In this paper, we introduce a new strategy for prompt caching, Snippet Caching, for LLM-based command-driven IoT systems to overcome the limitation. It perceives a command (prompt) as a function call with specific arguments. Instead of caching (input, output) pairs, it caches two simple code snippets that mimic LLM operations for each function. Based on the strategy, we design a novel prompt caching scheme, Snip-Cache, which generates code snippets with the help of LLMs. Experimental results show that Snip-Cache is significantly more beneficial to command-driven IoT systems than semantic caching schemes (GPTCache and vCache), in terms of response accuracy, response time, and token usage.

大型语言模型（llm）广泛应用于处理用户命令的实时接口系统中。尽管它们的输出质量很高，但响应时间长，运营成本高，破坏了llm服务的实用性和可持续性。提示缓存是为了缓解这个问题而引入的一种优化技术。它通过缓存和重用相同或类似提示的响应来避免重复提示的冗余处理。然而，就结果的可重用性而言，这种静态缓存方案具有内在的限制，因为在实际使用环境中，各种表达式具有相同的语义。在本文中，我们为基于llm的命令驱动物联网系统引入了一种新的提示缓存策略——Snippet caching，以克服这一限制。它将命令（提示符）视为带有特定参数的函数调用。它没有缓存（输入、输出）对，而是缓存两个简单的代码片段，模拟每个函数的LLM操作。基于该策略，我们设计了一种新的提示缓存方案——snippet - cache，该方案在llm的帮助下生成代码片段。实验结果表明，在响应精度、响应时间和令牌使用方面，snippet - cache明显比语义缓存方案（GPTCache和vCache）更有利于命令驱动的物联网系统。

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引用次数: 0

Machine learning on the edge for sustainable IoT networks: A systematic literature review 可持续物联网网络边缘的机器学习：系统文献综述

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-12 DOI: 10.1016/j.iot.2025.101846

Luisa Schuhmacher , Jimmy Fernandez Landivar , Ihsane Gryech , Hazem Sallouha , Michele Rossi , Sofie Pollin

The Internet of Things (IoT) has become integral to modern technology, enhancing daily life and industrial processes through seamless connectivity. However, the rapid expansion of IoT systems presents significant sustainability challenges, such as high energy consumption and inefficient resource management. Addressing these issues is critical for the long-term viability of IoT networks. Machine learning (ML), with its proven success across various domains, offers promising solutions for optimizing IoT operations. ML algorithms can learn directly from raw data, uncovering hidden patterns and optimizing processes in dynamic environments. Executing ML at the edge of IoT networks can further enhance sustainability by reducing bandwidth usage, enabling real-time decision-making, and improving data privacy. Additionally, testing ML models on actual hardware is essential to ensure satisfactory performance under real-world conditions, as it captures the complexities and constraints of real-world IoT deployments. Combining ML at the edge and actual hardware testing, therefore, increases the reliability of ML models to effectively improve the sustainability of IoT systems. The present systematic literature review explores how ML can be utilized to enhance the sustainability of IoT networks, examining current methodologies, benefits, challenges, and future opportunities. Through our analysis, we aim to provide insights that will drive future innovations in making IoT networks more sustainable.

物联网（IoT）已成为现代技术不可或缺的一部分，通过无缝连接改善了日常生活和工业流程。然而，物联网系统的快速扩展带来了重大的可持续性挑战，例如高能耗和低效的资源管理。解决这些问题对于物联网网络的长期可行性至关重要。机器学习（ML）在各个领域都取得了成功，为优化物联网运营提供了有前途的解决方案。机器学习算法可以直接从原始数据中学习，发现隐藏的模式，并在动态环境中优化过程。在物联网网络边缘执行机器学习可以通过减少带宽使用、实现实时决策和改善数据隐私来进一步增强可持续性。此外，在实际硬件上测试ML模型对于确保在现实条件下的令人满意的性能至关重要，因为它捕捉了现实世界物联网部署的复杂性和局限性。因此，将边缘机器学习与实际硬件测试相结合，可以提高机器学习模型的可靠性，从而有效提高物联网系统的可持续性。本系统的文献综述探讨了如何利用机器学习来增强物联网网络的可持续性，研究了当前的方法、好处、挑战和未来的机会。通过我们的分析，我们的目标是提供见解，推动未来的创新，使物联网网络更具可持续性。

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引用次数: 0

FedWKD: Federated learning weighted aggregation with knowledge distillation for IoT forecasting FedWKD：基于知识蒸馏的物联网预测的联邦学习加权聚合

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-11 DOI: 10.1016/j.iot.2025.101849

Bouchra Fakher , Mohamed El Amine Brahmia , Ismail Bennis , Abdelhafid Abouaissa

Federated Learning (FL) has emerged as a promising solution for decentralized Machine Learning (ML) that does not have direct access to datasets in a centralized manner. However, the traditional FL methods are prone to overfitting and model drift at the client level and server divergence during classic aggregation in case of heterogeneous, non-independent and identically distributed (non-IID) time-series sensor data. In this paper, we propose a novel approach that integrates bidirectional Knowledge Distillation (KD) by using distilled soft predictions of each client model, called logits, as well as server model distilled logits. Specifically, clients use KD regularization techniques using the received server logits during model training, while the server uses received client logits to build a score for weighted global aggregation each round. Thus, we avoid local training overhead for clients, while also improving global aggregation using weighting on the server-side for each training round for non-IID data. Experimental results highlight its ability to improve forecasting metrics compared to other methods such as CADIS and FEDGKD, using loss, error, and execution time metrics, hence bettering generalization and minimizing client drift and bias.

联邦学习（FL）已经成为去中心化机器学习（ML）的一个有前途的解决方案，它不能以集中的方式直接访问数据集。然而，对于异构、非独立、同分布（non-IID）的时间序列传感器数据，传统的FL方法在经典聚合过程中容易出现客户端的过拟合和模型漂移，而服务器端的发散。在本文中，我们提出了一种新的方法，通过使用每个客户端模型（称为logits）的蒸馏软预测以及服务器模型蒸馏logits来集成双向知识蒸馏（KD）。具体来说，客户端在模型训练期间使用接收到的服务器logit使用KD正则化技术，而服务器则使用接收到的客户端logit为每轮加权全局聚合构建分数。因此，我们避免了客户端的本地训练开销，同时还在服务器端使用非iid数据的每个训练轮的加权来改进全局聚合。实验结果表明，与其他方法（如CADIS和FEDGKD）相比，它能够使用损失、误差和执行时间指标来改进预测指标，从而更好地泛化并最大限度地减少客户端漂移和偏差。

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引用次数: 0

Fault detection of industrial air separation stations based on metaheuristic optimization and bidirectional long short-term memory technique 基于元启发式优化和双向长短期记忆技术的工业空分站故障检测

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-09 DOI: 10.1016/j.iot.2025.101850

Thanh-Phuong Nguyen , Chung-Chieh Lin , Ming-Yuan Cho

Industrial air separation stations play a critical role in numerous sectors, necessitating robust fault detection frameworks to ensure reliability and operational safety. This study presents a novel approach combining Bidirectional Long Short-Term Memory (BLSTM) networks with Enhanced Particle Swarm Optimization (EPSO) for fault detection in industrial air separation stations. The BLSTM model, renowned for its ability to capture temporal dependencies in sequential data, is optimized using EPSO to fine-tune its hyperparameters, enhancing its fault detection performance. The proposed EPSO-BLSTM framework is rigorously evaluated against conventional techniques, including Recurrent Neural Networks (RNN), Bidirectional RNN, Gated Recurrent Units (GRU), LSTM, Convolutional Neural Networks (CNN), and standard BLSTM model with the most notable improvements of error-based 58.32 % Loss, 63.37 % Val Loss, 1.24 % CP, 0.6 % Val CP, 60.11 % MAE, 63.37 % Val MAE, 63.46 % MSE, and 81.75 % Val MSE, and with accurate-based 4.68 % Pre, 5.55 % Val Pre, 5.72 % Rec, and 4.39 % Val Rec. Comparative analysis highlights the superior fault classification accuracy and generalization capability of the EPSO-BLSTM model under diverse operational scenarios. This research underscores the potential of integrating metaheuristic optimization with advanced deep learning architectures to address complex fault detection challenges, offering a scalable and efficient solution for industrial air separation stations.

工业空分站在许多领域发挥着关键作用，需要强大的故障检测框架来确保可靠性和运行安全性。提出了一种将双向长短期记忆（BLSTM）网络与增强粒子群优化（EPSO）相结合的工业空分站故障检测新方法。BLSTM模型以其捕获时序数据中的时间依赖性的能力而闻名，该模型使用EPSO对其超参数进行了优化，从而提高了其故障检测性能。提出的EPSO-BLSTM框架与常规技术（包括循环神经网络（RNN）、双向RNN、门控循环单元（GRU）、LSTM、卷积神经网络（CNN）和标准BLSTM模型）进行了严格的评估，其中最显著的改进是基于误差的58.32% Loss、63.37% Val Loss、1.24% CP、0.6% Val CP、60.11% MAE、63.37% Val MAE、63.46% MSE和81.75% Val MSE，以及基于精度的4.68% Pre、5.55% Val Pre、5.72% Rec，对比分析表明，EPSO-BLSTM模型在不同运行场景下具有较好的故障分类精度和泛化能力。这项研究强调了将元启发式优化与先进的深度学习架构相结合的潜力，以解决复杂的故障检测挑战，为工业空分站提供可扩展和高效的解决方案。

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引用次数: 0

Reformed multi-source sharing (RMSS) for efficient resource distribution across edge servers 改进了多源共享（RMSS），以实现跨边缘服务器的有效资源分配

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-08 DOI: 10.1016/j.iot.2025.101848

Kannan Srinivasan , Guman Singh Chauhan , Rahul Jadon , Rajababu Budda , Venkata Surya Teja Gollapalli , Joseph Bamidele Awotunde

Mobile Edge Computing plays a critical role in enabling real-time services across heterogeneous applications by distributing computational resources across multiple servers. However, managing service efficacy in multi-resource environments remains a challenge, particularly concerning scalability and service deficiencies. This study aims to introduce a novel method, Reformed Multi-Source Sharing (RMSS), to enhance resource distribution and improve service efficiency in MEC environments. The propose RMSS, which periodically updates resource allocations based on service density and source sharing to optimize the sharing rate. The method employs federated learning to validate previous resource allocations, ensuring optimal service distribution and minimizing deficiencies. The system was evaluated using multi-server environments and edge devices. The proposed RMSS method effectively mitigates resource allocation deficiencies, leading to significant improvements in service response times and user support. RMSS demonstrated up to 9.77 % higher service response rates, 13.88 % lower latency, and reduced service deficiency by 11.89 %, compared to existing approaches. RMSS improves scalability and resource distribution in MEC, edge devices in dense user environments. Future research will focus on incorporating virtualization-based edge slicing to further reduce latency and optimize resource distribution in increasingly complex edge networks.

移动边缘计算通过在多个服务器上分配计算资源，在跨异构应用程序实现实时服务方面发挥着关键作用。然而，在多资源环境中管理服务效率仍然是一个挑战，特别是在可伸缩性和服务缺陷方面。本研究旨在引入一种新的方法，即改进型多源共享（RMSS），以加强MEC环境下的资源分配，提高服务效率。提出了基于服务密度和资源共享的RMSS，定期更新资源分配，优化资源共享率。该方法采用联邦学习来验证以前的资源分配，确保最优的服务分配和最小化缺陷。该系统使用多服务器环境和边缘设备进行了评估。提出的RMSS方法有效地缓解了资源分配不足的问题，显著改善了服务响应时间和用户支持。与现有方法相比，RMSS的服务响应率提高了9.77%，延迟降低了13.88%，服务不足减少了11.89%。RMSS提高了MEC和密集用户环境中的边缘设备的可伸缩性和资源分配。未来的研究将集中于结合基于虚拟化的边缘切片，以进一步减少延迟并优化日益复杂的边缘网络中的资源分配。

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引用次数: 0

A TinyML device for risk identification for people with hearing loss 一种TinyML设备，用于听力损失人群的风险识别

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-08 DOI: 10.1016/j.iot.2025.101840

Cristian Bautista-Villalpando , Victor Lomas-Barrie

This paper presents an innovative approach to enhancing the quality of life for people with hearing impairment by implementing a portable and discreet TinyML device. The Support System for Identifying Emergency Sounds (SSIES) is designed to recognize four characteristic emergency sounds: car horn, scream, ambulance sirens, and crying babies. Through unique vibration patterns for each sound, the device provides a haptic response that allows the user to be aware of their surroundings and react if necessary. In addition, the device provides information on the direction of arrival (DOA) of the sound. In the state of the art, various supervised machine learning techniques have been explored to achieve this behavior. In this work, we focus primarily on artificial neural network algorithms (ANN) and their optimization for execution on devices with limited computational resources, a trend known as Machine Learning at the Edge.

The methodology used in this project is based on a combination of the HW/SW co-design and development lifecycle model for embedded systems and the lifecycle of ML-based solutions.

The results obtained indicate that the proposed TinyML device is feasible and has the potential to significantly improve environmental awareness for people with hearing impairment.

本文提出了一种创新的方法，通过实施便携式和谨慎的TinyML设备来提高听力障碍患者的生活质量。识别紧急声音支持系统（SSIES）旨在识别四种典型的紧急声音：汽车喇叭声、尖叫声、救护车警报声和婴儿哭声。通过对每种声音的独特振动模式，该设备提供触觉响应，使用户能够意识到周围环境并在必要时做出反应。此外，该设备还提供声音到达方向（DOA）的信息。在目前的技术状态下，各种监督机器学习技术已经被探索来实现这种行为。在这项工作中，我们主要关注人工神经网络算法（ANN）及其在计算资源有限的设备上执行的优化，这一趋势被称为边缘机器学习。该项目中使用的方法是基于嵌入式系统的硬件/软件协同设计和开发生命周期模型以及基于ml的解决方案的生命周期的组合。结果表明，所提出的TinyML装置是可行的，具有显著提高听障人士环保意识的潜力。

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引用次数: 0

Human-centered and context-aware smart ML-based IoT framework for online fatigue detection: A real-world study of football training 用于在线疲劳检测的以人为中心和上下文感知的基于ml的智能物联网框架：足球训练的现实世界研究

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-08 DOI: 10.1016/j.iot.2025.101847

Abdelkarim Mamen , Elisabetta De Giovanni , Teodoro Montanaro , Ilaria Sergi , Luigi Patrono

Fatigue is one of the factors that most influences competitive athletes’ performance, leading to injuries and overtraining. To effectively monitor and predict fatigue levels during real-world training, it is necessary to integrate Internet of Things (IoT) technology with machine learning (ML). In this context, the paper presents three main contributions : a) a smart IoT framework that integrates edge and cloud-based modules to collect physiological parameters, monitor fatigue during real-world sessions, and assist coaches in optimizing exercise strategies ; b) a dataset collected through the proposed framework in a real pilot study with eight futsal players over five training sessions, each lasting between 35 and 50 m depending on performed exercises, using ECG and PPG-based sensors ; c) an online ML-based fatigue detection module and on-cloud analysis of various ML models, traditional and deep learning, including CNN+GRU, XGBoost, and Transformer architectures, and context-aware feature sets. We evaluated the accuracy of our fatigue detection method using standard metrics, achieving an F1-score of up to 95 % with pilot study data. Our framework incorporates a context-aware design, where contextual information (exercise type) and sensing modality (ECG- or PPG-based) are explicitly integrated with physiological features (HRV and HR) in the fatigue prediction model to adapt it to different settings, improving robustness and interpretability. Finally, we evaluated the framework’s efficacy and the value of user and expert input, highlighting the benefits of integrating IoT and ML within a human-centered, context-aware approach to balance sensor accuracy, comfort, and efficiency in competitive sports training.

疲劳是影响竞技运动员表现的主要因素之一，它会导致受伤和过度训练。为了有效地监测和预测现实训练中的疲劳水平，有必要将物联网（IoT）技术与机器学习（ML）相结合。在此背景下，本文提出了三个主要贡献：a)集成边缘和基于云的模块的智能物联网框架，用于收集生理参数，监测真实会话中的疲劳，并协助教练优化运动策略；b)使用ECG和基于ppg的传感器，通过建议的框架在一项真实的试点研究中收集的数据集，该研究包括八名五人制足球运动员在五次训练中进行的训练，每次训练时长在35至50米之间，具体取决于所进行的练习；c)基于ML的在线疲劳检测模块和各种ML模型的云上分析，传统和深度学习，包括CNN+GRU， XGBoost和Transformer架构，以及上下文感知功能集。我们使用标准指标评估了疲劳检测方法的准确性，在初步研究数据中获得了高达95%的f1分。我们的框架采用了上下文感知设计，其中上下文信息（运动类型）和传感模式（基于ECG或ppg）与疲劳预测模型中的生理特征（HRV和HR）明确集成，以使其适应不同的设置，提高鲁棒性和可解释性。最后，我们评估了框架的有效性以及用户和专家输入的价值，强调了将物联网和机器学习集成在以人为中心的情境感知方法中的好处，以平衡竞技体育训练中传感器的准确性、舒适性和效率。

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引用次数: 0

Blockchain-assisted attribute-based multi-keyword search for dynamic encrypted data in cloud-edge-IoT 云边缘物联网中基于属性的区块链辅助多关键字动态加密数据搜索

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-06 DOI: 10.1016/j.iot.2025.101838

Hanlei Cheng , Sio-Long Lo , Jing Lu

Keyword search is a fundamental technique for retrieving data outsourced to the cloud. Although encryption preserves data confidentiality, existing searchable encryption schemes often fail to efficiently support dynamic authorization and flexible retrieval. To address these limitations, we propose

BAMKS

, a blockchain-assisted attribute-based multi-keyword search scheme that supports secure and efficient search over version-aware encrypted data. In

BAMKS

, multiple data owners collaboratively generate version-bound access tokens that grant authorized users decryption privileges over evolving data. The scheme further enables conjunctive keyword search with updatable indexes. To ensure the integrity of search results, users can verify their correctness using an aggregated Schnorr-based non-interactive zero-knowledge proof, which is validated by smart contracts. In addition,

BAMKS

provides efficient attribute and user revocation without re-encrypting the stored ciphertexts, and supports user traceability for identifying malicious users from leaked keys. We formally prove that

BAMKS

achieves security against chosen-plaintext attacks (IND-CPA) and chosen-keyword attacks (IND-CKA) under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. Performance evaluations show that the scheme achieves lightweight decryption and efficient multi-keyword search, thereby reducing client-side computation and making it suitable for resource-constrained IoT environments. These features demonstrate the practicality of

BAMKS

for distributed cloud-edge-IoT storage applications.

关键字搜索是检索外包给云的数据的基本技术。虽然加密保护了数据的机密性，但现有的可搜索加密方案往往不能有效地支持动态授权和灵活的检索。为了解决这些限制，我们提出了BAMKS，这是一种区块链辅助的基于属性的多关键字搜索方案，支持对版本感知加密数据的安全高效搜索。在BAMKS中，多个数据所有者协作生成版本绑定的访问令牌，授予授权用户对演进数据的解密权限。该方案进一步支持使用可更新索引的联合关键字搜索。为了确保搜索结果的完整性，用户可以使用基于聚合schnorr的非交互式零知识证明来验证其正确性，该证明由智能合约验证。此外，BAMKS提供了有效的属性和用户撤销，而无需重新加密存储的密文，并支持用户可追溯性，以便从泄露的密钥中识别恶意用户。我们正式证明了BAMKS在决策双线性Diffie-Hellman （DBDH）假设下实现了对选择明文攻击（IND-CPA）和选择关键字攻击（IND-CKA）的安全性。性能评估表明，该方案实现了轻量级解密和高效的多关键字搜索，减少了客户端计算量，适用于资源受限的物联网环境。这些特性证明了BAMKS在分布式云边缘物联网存储应用中的实用性。

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引用次数: 0

Decentralized proximity-aware clustering for collective self-federated learning 用于集体自联合学习的分散式邻近感知聚类

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-05 DOI: 10.1016/j.iot.2025.101841

Davide Domini , Nicolas Farabegoli , Gianluca Aguzzi , Mirko Viroli , Lukas Esterle

In recent years, Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative model training in IoT systems, enabling clients to learn a global model for tasks like classification, prediction, or anomaly detection in IoT environments without sharing raw data. However, traditional centralized FL architectures face bottlenecks, single points of failure, and struggle with non-IID data. These limitations hinder effective Collective Intelligence in large-scale IoT systems where numerous devices operate across diverse and dynamic environments. Existing clustered FL approaches often retain centralization or overlook how the spatial distribution inherent in IoT deployments directly influences data heterogeneity, challenging both the integration of spatially correlated devices and the establishment of intelligence distributed across the entire system. Creating such intelligence demands both decentralized architectures for scalability and effective integration of devices with similar data distributions. For these reasons, this article introduces Proximity-Aware Self-Federated Learning (PSFL), a novel decentralized approach embodying collective intelligence principles. PSFL leverages field-based coordination to enable IoT devices to form self-federations, dynamically clustered groups that train specialized models based on both spatial proximity and local model characteristics. These self-federations reflect underlying data distributions, creating a distributed ecosystem of specialized models across the network. This approach overcomes global model limitations in non-IID settings through specialized federations based on local data distributions, enhancing performance while maintaining decentralization. We evaluate our approach using the Extended MNIST and CIFAR-100 datasets against state-of-the-art baselines, demonstrating its effectiveness in forming coherent, localized models under non-IID conditions.

近年来，联邦学习（FL）已成为物联网系统中协作模型训练的隐私保护范例，使客户能够在不共享原始数据的情况下学习物联网环境中分类、预测或异常检测等任务的全局模型。然而，传统的集中式FL架构面临瓶颈、单点故障以及与非iid数据的斗争。这些限制阻碍了大规模物联网系统中有效的集体智能，其中许多设备在不同和动态的环境中运行。现有的集群FL方法通常保留集中化或忽略物联网部署中固有的空间分布如何直接影响数据异构性，这既挑战了空间相关设备的集成，也挑战了分布在整个系统中的智能的建立。创建这样的智能既需要分散的可伸缩性架构，也需要具有类似数据分布的设备的有效集成。基于这些原因，本文介绍了一种体现集体智能原则的新颖分散方法——邻近感知自联邦学习（PSFL）。PSFL利用基于现场的协调，使物联网设备能够形成自联盟，动态聚类组，根据空间接近性和局部模型特征训练专门的模型。这些自联合反映了底层数据分布，在网络上创建了一个专门模型的分布式生态系统。这种方法通过基于本地数据分布的专门联合克服了非iid设置中的全局模型限制，在保持分散性的同时提高了性能。我们使用扩展的MNIST和CIFAR-100数据集对最先进的基线进行了评估，证明了其在非iid条件下形成连贯的局部模型的有效性。

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引用次数: 0

Acheron: a market-based multi-relay architecture for adaptive and secure cross-chain communication Acheron：基于市场的多中继架构，用于自适应和安全的跨链通信

IF 7.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Internet of Things

Pub Date : 2025-12-04 DOI: 10.1016/j.iot.2025.101836

Tuan-Dung Tran , Quang Vu , Bao Huynh , Van-Hau Pham

The growing fragmentation of the blockchain ecosystem has intensified the demand for secure and scalable interoperability protocols. Existing cross-chain solutions face an ‘interoperability trilemma’, struggling to simultaneously achieve decentralization, security, and scalability amidst a fragmented blockchain ecosystem. This paper introduces Acheron, a market-based multi-relay architecture designed to navigate this trilemma by decoupling security from a single monolithic entity and parallelizing message transport across independent, sovereign Relay Chains. Each Relay Chain operates as a self-contained Proof-of-Stake (PoS) network with slashing-based cryptoeconomic security, eliminating monolithic bottlenecks. Routing decisions are governed by a Multi-Attribute Utility Theory (MAUT) model that dynamically optimizes for security, latency, and cost, while the Acheron DAO and Watchtower Network ensure verifiable governance and continuous relay telemetry. Experimental validation using the Hardhat framework and the Base Sepolia testnet demonstrates that throughput scales linearly from 0.13 to 1.27 transactions per second as relays increase from one to ten, while latency variance decreases by over 90 % and average transaction costs remain stable. Compared with established baselines, Acheron achieves a 7 % reduction in mean latency and over 2.2 ×  higher throughput under identical workloads. These results demonstrate that Acheron’s market-based paradigm presents a viable and quantitatively superior path toward achieving secure, scalable, and decentralized interoperability for both financial and IoT-driven ecosystems.

区块链生态系统的日益分散加剧了对安全和可扩展互操作性协议的需求。现有的跨链解决方案面临着“互操作性三难困境”，在分散的区块链生态系统中努力同时实现去中心化、安全性和可扩展性。本文介绍了Acheron，这是一种基于市场的多中继架构，旨在通过将安全性与单个整体实体解耦并在独立的主权中继链上并行传输消息来解决这一三难困境。每个中继链作为一个独立的权益证明（PoS）网络运行，具有基于削减的加密经济安全性，消除了单一的瓶颈。路由决策由多属性效用理论（Multi-Attribute Utility Theory， MAUT）模型治理，该模型动态优化了安全性、延迟和成本，而Acheron DAO和Watchtower Network确保了可验证的治理和连续的中继遥测。使用Hardhat框架和Base Sepolia测试网进行的实验验证表明，随着中继从1个增加到10个，吞吐量从每秒0.13到1.27个事务呈线性增长，而延迟差异减少了90%以上，平均交易成本保持稳定。与已建立的基线相比，在相同的工作负载下，Acheron的平均延迟降低了7%，吞吐量提高了2.2 × 以上。这些结果表明，Acheron基于市场的范例为金融和物联网驱动的生态系统实现安全、可扩展和分散的互操作性提供了一条可行的、数量上的优越途径。

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引用次数: 0