Computer Communications最新文献_第2页

Performance analysis and optimisation of wireless sensor networks with startup times and (V,N)-policy sleep scheduling 具有启动时间和（V，N）策略睡眠调度的无线传感器网络性能分析与优化

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-20 DOI: 10.1016/j.comcom.2026.108427

Yongcong Mou , Yinghui Tang , Miaomiao Yu

To effectively conserve energy in wireless sensor networks (WSNs) and reduce packet delay, we propose a (

V, N

)-policy sleep scheme for each sensor node, functioning in four distinct states. We model the sensor node, which incorporates the sleep mechanism, as a discrete-time

G e o / G / 1

vacation queueing system that accounts for startup times and an activation threshold. We first employ a probabilistic analysis technique to conduct a transient analysis of the system, aiming to derive recursive formulas for the steady-state distribution of the number of packets. We further obtain explicit expressions for several essential system performance metrics, including the expected number of packets, mean delay, and average energy cost of the node. The simulation experiments on models with various service time distributions confirm the analytical results, and extensive numerical experiments evaluate the sensitivity of system performance to several parameters. A weighted-sum cost function integrating mean delay and average energy consumption is formulated, and optimal sleep-wake strategies that minimise the weighted sum cost are evaluated across diverse sleep time distributions, service time distributions, weight coefficients, and delay constraints. The results demonstrate the advantages of the

(V, N)

-policy in achieving an ideal equilibrium between energy efficiency and mean delay in WSNs.

为了有效地节省无线传感器网络（WSNs）的能量并降低数据包延迟，我们提出了一种（V，N）策略休眠方案，每个传感器节点在四种不同的状态下工作。我们将包含睡眠机制的传感器节点建模为考虑启动时间和激活阈值的离散时间Geo/G/1假期排队系统。我们首先采用概率分析技术对系统进行暂态分析，旨在推导出包数稳态分布的递归公式。我们进一步得到了几个基本系统性能指标的显式表达式，包括期望的数据包数量、平均延迟和节点的平均能量成本。对不同服役时间分布的模型进行了仿真实验，验证了分析结果，并进行了大量的数值实验，评估了系统性能对多个参数的敏感性。建立了一个积分平均延迟和平均能量消耗的加权和成本函数，并在不同的睡眠时间分布、服务时间分布、权重系数和延迟约束下评估了最小化加权和成本的最佳睡眠-觉醒策略。结果表明，（V，N）策略在WSNs中实现能量效率和平均延迟之间的理想平衡方面具有优势。

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引用次数: 0

An efficient master head selection for multi-EEG to multi-fog IoT network using 6G-driven FaaS 基于6g驱动FaaS的多eeg到多雾物联网的高效主头选择

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-15 DOI: 10.1016/j.comcom.2026.108429

Rupalin Nanda , Sakthivel P. , Rama Krushna Rath , Abhishek Hazra

An Electroencephalogram (EEG) signal plays a vital role in a healthcare communication system for recording the electrical activities of the human brain from the scalp. In recent times, the conventional IoT-based healthcare system uses the cloud computing paradigm to manage time-critical healthcare data. Moreover, switching to the fog computing, the fog-assisted EEG systems are for single EEG applications. However, the use of a fog computing paradigm for a single EEG system is not an efficient solution in terms of resource management and time consumption. Therefore, we introduce a Fog-enabled EEG architecture where multiple fog devices collaboratively process the data in a single integrated IoT platform. As the proposed architecture is new, we focus on developing the mathematical model of the architecture and discuss the crucial aspects. Additionally, we devise a dynamic optimal fog head selection within the network using a weighted multi-criteria decision-making approach. From the simulation, we observe that the average propagation delay is reduced by approximately 95% using 6G-enabled fog computing as compared to the cloud. Further, our method reduces the total delay by 83.87% compared to the existing baseline KCHE technique, showing the effectiveness of this work.

脑电图（EEG）信号在医疗保健通信系统中起着至关重要的作用，用于记录来自头皮的人脑电活动。最近，传统的基于物联网的医疗保健系统使用云计算范式来管理时间关键型医疗保健数据。此外，转向雾计算，雾辅助脑电图系统适用于单脑电图应用。然而，就资源管理和时间消耗而言，对单个EEG系统使用雾计算范式并不是一种有效的解决方案。因此，我们引入了一种支持雾的EEG架构，其中多个雾设备在单个集成物联网平台中协同处理数据。由于所建议的体系结构是新的，我们将重点放在开发体系结构的数学模型并讨论关键方面。此外，我们使用加权多准则决策方法设计了网络内动态最优雾头选择。从模拟中，我们观察到与云计算相比，使用支持6g的雾计算，平均传播延迟减少了约95%。此外，与现有的基线KCHE技术相比，我们的方法减少了83.87%的总延迟，表明了这项工作的有效性。

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引用次数: 0

MoCS: Modular configuration synthesis via large language models and graph neural network-augmented recommendation MoCS：通过大型语言模型和图形神经网络增强推荐的模块化配置合成

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-14 DOI: 10.1016/j.comcom.2026.108428

Yuqi Dai , Hua Zhang , Jingyu Wang , Jianxin Liao

Network configuration synthesis is essential for automated configuration management in large and complex networks. However, existing synthesizers face challenges in practical applications, including limited scalability, slow synthesis speed, insufficient support for various routing protocols, and difficulty in handling mixed vendor configurations.

To address these issues, this paper proposes MoCS, a modular configuration synthesizer that integrates multiple Large Language Models (LLMs) with Graph Neural Network (GNN)-enhanced recommendations to enable protocol-agnostic and vendor-compliant configuration synthesis. MoCS decomposes the synthesis pipeline into three LLM-based modules, each following a unified prompt engineering framework with task-specific adaptations. Specifically, the Intent Translation Module (IT-Module) translates natural language intents into structured configuration tasks, while the Configuration Graph Generation Module (CG-Module) constructs a Configuration Knowledge Graph (CKG) by incorporating semantic information from network topologies, structured tasks, and vendor-specific configuration templates. These two modules collaborate to support various protocols and mixed vendor configurations via a unified graph representation. The Configuration Recommendation Module (CR-Module) utilizes a heterogeneous GNN-based model (HGAT-CR) to perform type-aware reasoning over the CKG and generate top-

k

candidate parameters. These candidates provide prior knowledge that narrows the search space and improves recommendation accuracy. Finally, they are refined through an LLM-guided optimization mechanism that combines formal verification feedback to produce the final configuration, ensuring maximal intent satisfaction while minimizing side effects.

Our evaluation demonstrates that MoCS outperforms existing synthesizers, including NetComplete, INCS, and ConfigReco. In large networks with complex intents, MoCS achieves a high coverage rate (88.23 ± 1.12%), low redundancy rate (7.89 ± 1.59%), perfect intent satisfaction rate (1.00 ± 0.00), and reasonable runtime (143.83 ± 21.89s). Furthermore, MoCS can synthesize mixed vendor configurations, which current synthesizers cannot handle.

网络配置综合是实现大型复杂网络中自动化配置管理的关键。然而，现有的合成器在实际应用中面临着挑战，包括有限的可扩展性、缓慢的合成速度、对各种路由协议的支持不足以及处理混合供应商配置的困难。为了解决这些问题，本文提出了MoCS，一种模块化配置合成器，它将多个大型语言模型（llm）与图神经网络（GNN）增强的建议集成在一起，以实现协议无关和供应商兼容的配置合成。MoCS将合成管道分解为三个基于llm的模块，每个模块都遵循统一的提示工程框架，并具有特定于任务的适应性。具体来说，意图翻译模块（it模块）将自然语言意图转换为结构化配置任务，而配置图生成模块（cg模块）通过整合来自网络拓扑、结构化任务和供应商特定配置模板的语义信息来构建配置知识图（CKG）。这两个模块通过统一的图形表示来协作支持各种协议和混合供应商配置。配置推荐模块（CR-Module）利用基于异构gnn的模型（HGAT-CR）在CKG上执行类型感知推理并生成top-k候选参数。这些候选提供了缩小搜索空间和提高推荐准确性的先验知识。最后，它们通过llm指导的优化机制进行细化，该机制结合正式验证反馈来产生最终配置，确保最大程度地满足意图，同时最小化副作用。我们的评估表明MoCS优于现有的合成器，包括NetComplete， INCS和ConfigReco。在复杂意图的大型网络中，MoCS实现了高覆盖率（88.23±1.12%）、低冗余率（7.89±1.59%）、完美意图满意率（1.00±0.00）和合理运行时间（143.83±21.89s）。此外，MoCS可以合成混合的供应商配置，这是目前的合成器无法处理的。

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引用次数: 0

Design, Implementation, Performance evaluation of a Sub-7 GHz 5G NR-U system Sub-7 GHz 5G NR-U系统的设计、实现和性能评估

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-12 DOI: 10.1016/j.comcom.2026.108425

Mahamadou Diawara , Andre Faye

With the introduction of the fifth generation of mobile networks (5G) in 3GPP Release 15, driven by the exponential growth in the number of mobile users, the emergence of new multimedia services and the proliferation of private networks, spectrum management has become a key challenge in the field of telecommunications. In light of the high costs associated with the acquisition and use of licensed frequency bands, the NR-U (New Radio-Unlicensed) standard has emerged as a strategic solution. It enables the extension of 5G services to unlicensed spectrum, thereby addressing the increasing demand for capacity and flexibility. Unlicensed bands, particularly those below 7 GHz, exhibit promising characteristics for supporting real-time critical applications. They offer a cost-effective, flexible communication infrastructure capable of dynamically adapting to network capacity demands. This paper presents an experimental study of 5G NR-U operation over sub-7 GHz unlicensed bands using the open-source OpenAirInterface (OAI) platform and USRP B210 software-defined radio. We integrated these bands into a 5G system and provided a reference framework for future research on communications over unlicensed spectrum with OAI. The implementation accounts for hardware constraints and the stringent requirements of real-time processing to emulate a realistic deployment environment. Performance, and power consumption analysis results confirm the relevance of using sub-7 GHz unlicensed bands for critical applications in private network scenarios or connectivity extensions in remote areas. The proposed implementation is validated through a drone-based application scenario.

随着3GPP第15版第五代移动网络（5G）的引入，随着移动用户数量的指数级增长、新型多媒体业务的出现和专网的激增，频谱管理已成为电信领域的关键挑战。鉴于与获得和使用许可频段相关的高成本，NR-U（新无线电-无许可）标准已成为一种战略解决方案。它可以将5G业务扩展到未经许可的频谱，从而满足日益增长的容量和灵活性需求。未经许可的频段，特别是低于7 GHz的频段，在支持实时关键应用方面表现出很好的特性。它们提供了一种经济、灵活的通信基础设施，能够动态适应网络容量需求。本文利用开源的OpenAirInterface （OAI）平台和USRP B210软件定义无线电，对低于7 GHz的免许可频段上的5G NR-U操作进行了实验研究。我们将这些频段集成到5G系统中，并为未来使用OAI进行未经许可频谱通信的研究提供了参考框架。实现考虑了硬件约束和实时处理的严格要求，以模拟真实的部署环境。性能和功耗分析结果证实了在专用网络场景或偏远地区连接扩展的关键应用中使用低于7 GHz的非授权频段的相关性。建议的实现通过基于无人机的应用场景进行验证。

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引用次数: 0

Applying NovaGenesis: A service-oriented, self-organizing, and programmable IoT architecture for LoRa and Wi-Fi-based environmental monitoring 应用NovaGenesis：面向服务、自组织、可编程的物联网架构，用于基于LoRa和wi - fi的环境监测

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-10 DOI: 10.1016/j.comcom.2026.108423

Antonio M. Alberti , Epper Bonomo , Rodrigo H. Santos , Victor A. de J. Alberti , Marcelo E. Pellenz , Rodrigo da Rosa Righi

This work integrates NovaGenesis (NG), a clean-slate IoT architecture, with LoRa technology within low-power wide-area networks (LPWAN), extending previous efforts on NG connectivity with Wi-Fi. The research aims to update the embedded version of NG and develop devices for seamless LoRa and Wi-Fi IoT operation. It evaluates NG’s performance on LoRa and Wi-Fi, focusing on throughput, delay, and packet loss. Despite LPWAN limitations, the results show that the NG deployment is feasible, validating its self-organizing IoT life cycle to maintain service continuity between an ESP-32 and a data client. Performance meets the needs of IoT applications in agribusiness, logistics, and smart monitoring. In addition, a 24-hour environmental monitoring experiment was conducted in Santa Rita do Sapucaí(SRS), Minas Gerais, Brazil, where a commercial weather station was modified to integrate NG, allowing accurate collection of temperature, humidity, atmospheric pressure, wind conditions, solar radiation and UV index. The results met expected diurnal patterns in SRS, proving the reliability and precision of the sensors and communication infrastructure. This solution overcomes common IETF IoT stack limitations in devices naming, information provenance, entities identification, programmability via digital twins, programmability, services and devices self-organization, and trust formation, offering a robust platform for varied IoT scenarios in LPWAN environments. These are the key benefits of applying NovaGenesis for LoRa and Wi-Fi-based environmental monitoring.

这项工作将NovaGenesis （NG）这一全新的物联网架构与低功耗广域网（LPWAN）中的LoRa技术集成在一起，扩展了之前在NG连接Wi-Fi方面的努力。该研究旨在更新NG的嵌入式版本，并开发无缝LoRa和Wi-Fi物联网操作的设备。它评估了NG在LoRa和Wi-Fi上的性能，重点关注吞吐量、延迟和数据包丢失。尽管有LPWAN的限制，但结果表明，NG部署是可行的，验证了其自组织物联网生命周期，以保持ESP-32和数据客户端之间的服务连续性。性能满足物联网在农业综合企业、物流和智能监控领域的应用需求。此外，在巴西米纳斯吉拉斯州Santa Rita do Sapucaí（SRS）进行了一项24小时环境监测实验，在那里对一个商业气象站进行了改造，以整合NG，从而能够准确收集温度、湿度、大气压、风况、太阳辐射和紫外线指数。结果符合SRS的预期日模式，证明了传感器和通信基础设施的可靠性和精度。该解决方案克服了常见的IETF物联网堆栈在设备命名、信息来源、实体识别、通过数字双胞胎可编程性、可编程性、服务和设备自组织以及信任形成方面的限制，为LPWAN环境中的各种物联网场景提供了一个强大的平台。这些是将NovaGenesis应用于LoRa和基于wi - fi的环境监测的主要好处。

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引用次数: 0

FaaSBid: An auction-based model for Function as a Service in edge-fog environments using unallocated resources FaaSBid：在边缘雾环境中使用未分配资源的基于拍卖的功能即服务模型

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-08 DOI: 10.1016/j.comcom.2026.108413

Abdulrahman K. Al-Qadhi , Rukshan Athauda , Rohaya Latip , Masnida Hussin

The exponential growth of IoT devices has resulted in a need to process IoT workloads. Processing such workloads near the edge instead of the cloud has a number of advantages including lower latency, improved security and ability to meet many other Quality of Service attributes. Function as a Service (FaaS) is becoming a popular method to process such IoT workloads. In this paper, we propose a novel model, termed FaaSBid, that incentivise users to utilise serverless functions near the edge using unallocated resources. The service provider offers a discount range based on resource utilisation, where users offer bids to execute their functions near the edge resulting in cost savings while the service providers have a new revenue stream and higher resource utilisation near the edge. In this paper, a number of algorithms are proposed and evaluated for FaaSBid model. To initialise function placement, Fitness-Based Swap (FBSW) algorithm is proposed which places functions based on pre-defined information such as function size, function maximum execution time, and storage cost. Next, the Dynamic Demand Replacement Algorithm (DDRA) algorithm is used to place in-demand functions near the edge nodes periodically, while the proposed task scheduling algorithm - Maximum Revenue Bid (MRB) is used to give priority to tasks to maximise revenue near the edge. We have evaluated the FaaSBid model and the proposed algorithms and pricing model by comparing with a number of existing models and algorithms using real-world datasets. The results show that FaaSBid model provides higher resource utilisation, a new revenue stream for service providers while reducing costs for users. On average, in FaasBid, the proposed pricing model saved 12.9% and 6.5% compared to AWS fixed pricing and AuctionWhisk pricing respectively per function execution. Also, the results show that the proposed function placement and scheduling algorithms outperform many well-known function placement and scheduling algorithms in terms of revenue generated, resource utilisation, throughput, and latency with significant improvements near the edge. The results also demonstrated that dynamically placing functions based on demand has a significant impact. Overall, this paper outlines a new paradigm that uses unutilised resources near the edge, improving many QoS attributes from both service providers' and users’ perspectives.

物联网设备的指数级增长导致需要处理物联网工作负载。在边缘而不是云上处理此类工作负载具有许多优势，包括更低的延迟、更高的安全性以及满足许多其他服务质量属性的能力。功能即服务（FaaS）正在成为处理此类物联网工作负载的流行方法。在本文中，我们提出了一种称为FaaSBid的新模型，该模型激励用户使用未分配的资源利用边缘附近的无服务器功能。服务提供商提供基于资源利用率的折扣范围，用户在边缘附近出价执行其功能，从而节省成本，而服务提供商在边缘附近有新的收入流和更高的资源利用率。本文针对FaaSBid模型提出并评估了多种算法。为了初始化函数的位置，提出了基于适应度的交换（FBSW）算法，该算法根据预定义的信息（如函数大小、函数最大执行时间和存储成本）来放置函数。其次，采用动态需求替换算法（Dynamic Demand Replacement Algorithm， DDRA）周期性地将需求函数放置在边缘节点附近，同时采用提出的任务调度算法——最大收益出价（Maximum Revenue Bid， MRB）对边缘节点附近的任务给予优先级，以最大化收益。我们通过使用真实世界的数据集与许多现有的模型和算法进行比较，对FaaSBid模型和提出的算法和定价模型进行了评估。结果表明，FaaSBid模型提供了更高的资源利用率，为服务提供商提供了新的收入来源，同时降低了用户的成本。在FaasBid中，与AWS固定定价和AuctionWhisk定价相比，建议的定价模型在每次功能执行中分别节省了12.9%和6.5%的成本。此外，结果表明，所提出的功能放置和调度算法在产生的收入、资源利用率、吞吐量和延迟方面优于许多知名的功能放置和调度算法，并且在边缘附近有显着改进。结果还表明，基于需求动态配置功能具有显著的影响。总之，本文概述了一种利用边缘附近未利用资源的新范例，从服务提供商和用户的角度改进了许多QoS属性。

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引用次数: 0

Large and reliable data transfer service for LoRa mesh network applications 为LoRa网状网络应用提供大规模、可靠的数据传输服务

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-08 DOI: 10.1016/j.comcom.2025.108404

Joan Miquel Solé, Roger Pueyo Centelles, Felix Freitag, Roc Meseguer, Roger Baig Viñas

Recently, LoRa mesh networks have appeared as a communication technology for Internet of Things (IoT) devices. Through node-to-node communication, novel distributed IoT applications that extend beyond the capabilities of the LoRaWAN architecture can be enabled. However, current technologies for LoRa networks do not provide mechanisms for large and reliable data transfers between IoT nodes. This paper presents a service for such data transfers in LoRa mesh network applications, along with the protocol and formats used for inter-node communication. We explain the design choices and detail the implementation decisions to ensure that this service is practically usable. To this end, the service was integrated into the LoRaMesher library and is available as an open-source operational implementation. In experiments with ten real nodes and two network topologies, we observe that the service effectively achieves a large and reliable message delivery in an environment of concurrent transmissions and packet losses. In contrast, the cost of reliability for large data transfers is an increased number of messages and a higher delivery time. With the integration of the service into the LoRaMesher technology, developers now have a library that provides a reliable and large payload service for LoRa mesh network applications, eliminating the need to develop such capacity as a specific application-level solution.

近年来，LoRa mesh网络作为物联网（IoT）设备的通信技术出现。通过节点对节点通信，可以启用超越LoRaWAN架构功能的新型分布式物联网应用。然而，目前的LoRa网络技术并没有提供在物联网节点之间传输大量可靠数据的机制。本文提出了一种在LoRa网状网络应用中用于此类数据传输的服务，以及用于节点间通信的协议和格式。我们解释了设计选择并详细说明了实现决策，以确保该服务实际可用。为此，该服务被集成到LoRaMesher库中，并作为开源操作实现提供。在10个真实节点和两种网络拓扑的实验中，我们观察到该服务在并发传输和丢包的环境中有效地实现了大量可靠的消息传递。相比之下，大数据传输的可靠性成本是消息数量的增加和交付时间的延长。通过将服务集成到LoRaMesher技术中，开发人员现在拥有了一个库，可以为LoRa网状网络应用程序提供可靠的大负载服务，从而消除了开发特定应用程序级解决方案的需求。

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引用次数: 0

Sparse QoS prediction for cloud services via inductive subgraph pattern aware graph neural network 基于感应子图模式感知的云服务稀疏QoS预测

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-08 DOI: 10.1016/j.comcom.2026.108415

Jianlong Xu , Caiyi Chen , Qingcao Dai , Guanchen Du , Ruiqi Wang , Mingtong Li , Quanqing Guo , Yuxiang Zeng

Accurately predicting the Quality of Service (QoS) is a crucial issue for selecting suitable cloud services for each user. Collaborative prediction models have been successful in selecting suitable cloud services for users. However, they often struggle with extreme sparsity, where only a limited number of interactions are available for collaborative filtering. Some models excel at handling extreme sparsity but struggle with generalization at the same time. To overcome these challenges, we propose a sparse QoS prediction framework for cloud services via an inductive subgraph pattern-aware graph neural network (ISPA-GNN). Our framework employs a novel graph-based collaborative filtering approach combined with a subgraph sampling strategy to extract semantic information about user-service interactions more effectively. To optimize memory usage and enhance the generalization of unseen users or services, we utilize decoupled ID-based embeddings that maximize contextual information. Extensive experiments on a large-scale, real-world QoS dataset demonstrate that ISPA-GNN outperforms most current collaborative QoS prediction techniques in terms of mean absolute error (MAE) and root mean squared error (RMSE), while also achieving significant gains in memory efficiency.

准确预测服务质量（QoS）是为每个用户选择合适的云服务的关键问题。协作预测模型在为用户选择合适的云服务方面取得了成功。然而，它们经常与极端稀疏性作斗争，其中只有有限数量的交互可用于协同过滤。有些模型擅长处理极端稀疏性，但同时也在泛化方面挣扎。为了克服这些挑战，我们提出了一种基于归纳子图模式感知图神经网络（ISPA-GNN）的云服务稀疏QoS预测框架。我们的框架采用了一种新颖的基于图的协同过滤方法，结合子图采样策略，更有效地提取有关用户服务交互的语义信息。为了优化内存使用并增强未见用户或服务的泛化，我们利用解耦的基于id的嵌入来最大化上下文信息。在大规模、真实的QoS数据集上进行的大量实验表明，ISPA-GNN在平均绝对误差（MAE）和均方根误差（RMSE）方面优于大多数当前的协作QoS预测技术，同时在内存效率方面也取得了显著的进步。

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引用次数: 0

AgriSmart: An IoT-enabled framework for agricultural resource optimization AgriSmart：农业资源优化的物联网框架

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-07 DOI: 10.1016/j.comcom.2026.108416

Xu Tao , Jackson Butcher , Christian Cumini , Mounica Talasila , Salmeron Cortasa Montserrat , Alessio Sacco , Michael Popp , Guido Marchetto , Simone Silvestri

Efficient use of farming resources (e.g., nitrogen, water, pesticides) is key to maximizing productivity and promoting sustainable agriculture. Traditional methods, such as fixed-rate applications or soil sampling, often fail to adapt to changing in-season conditions and specific nutrient demands, leading to inefficiencies and environmental harm. In this work, we propose AgriSmart, an IoT-enabled framework that optimizes resource application strategies to maximize crop yield while minimizing resource usage within a given budget. AgriSmart formulates an optimization problem solved periodically using an enhanced Differential Evolution (DE) algorithm that balances exploration and exploitation, following a Model Predictive Control (MPC) approach. Crop yield responses to varying application timings and rates are estimated using the process-based crop simulation model DSSAT (Decision Support System for Agrotechnology Transfer). To improve flexibility and reduce computational complexity, we introduce adjustable receding horizon that allows multiple actions to be applied before re-optimization, enabling adaptation to resources with different application frequencies (e.g., water vs. nitrogen). As the time horizon advances, AgriSmart dynamically adjusts the resource applications to better match crop needs at each growth stage, responding to evolving weather and field conditions. We evaluate AgriSmart in two use cases: irrigation scheduling for soybean and nitrogen management for maize. Results show that AgriSmart outperforms existing methods, achieving up to 21.4% water savings for soybean without yield loss, and increasing maize yield by 20% while reducing nitrogen use by up to 32%.

有效利用农业资源（如氮、水、农药）是最大限度提高生产力和促进可持续农业的关键。传统的方法，如固定速率施用或土壤取样，往往不能适应季节条件的变化和特定的养分需求，导致效率低下和环境危害。在这项工作中，我们提出了AgriSmart，这是一个基于物联网的框架，可优化资源应用策略，以最大限度地提高作物产量，同时在给定预算内最大限度地减少资源使用。AgriSmart根据模型预测控制（MPC）方法，利用增强型差分进化（DE）算法平衡勘探和开发，制定了一个定期解决的优化问题。利用基于过程的作物模拟模型DSSAT（农业技术转移决策支持系统）估计作物产量对不同施用时间和施用量的响应。为了提高灵活性和降低计算复杂性，我们引入了可调节的后退地平线，允许在重新优化之前应用多个动作，从而适应不同应用频率的资源（例如，水与氮）。随着时间的推移，AgriSmart动态调整资源应用，以更好地匹配作物在每个生长阶段的需求，响应不断变化的天气和田间条件。我们在两个用例中评估AgriSmart：大豆的灌溉调度和玉米的氮管理。结果表明，AgriSmart优于现有方法，在不损失产量的情况下，大豆节水21.4%，玉米增产20%，氮肥用量减少32%。

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引用次数: 0

Context-aware anomaly detection by community detection in the Internet of Things 基于社区检测的物联网环境感知异常检测

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2026-01-06 DOI: 10.1016/j.comcom.2026.108414

Fatemeh Stodt , Christoph Reich , Fabrice Theoleyre

This paper introduces a novel context-aware anomaly detection framework for the Internet of Things, leveraging community detection in multi-edge graphs with a heterogeneous Graph Neural Network (HeteroGNN) architecture to enhance network security. The proposed framework detects anomalies such as unexpected communication patterns among devices that rarely interact, unusual traffic spikes during off-hours, or deviations in the contextual and knowledge-based interactions of devices. For example, in an industrial IoT environment, unauthorized access or malicious activity can be inferred from unexpected communication within a device community after working hours. Our detection approach uses multi-edge graphs to model diverse interactions (network communication, context, knowledge) and applies community detection to capture stable graph structures. By incorporating these insights into a HeteroGNN, the framework effectively distinguishes anomalous edges while maintaining scalability and adaptability to dynamic network conditions. Experimental evaluation on the CIC-ToN-IoT and CIC-IDS2017 dataset demonstrates the framework’s superior accuracy, precision, and robustness, establishing it as a practical and effective solution for securing IoT networks against both known and emerging threats.

本文介绍了一种新的物联网上下文感知异常检测框架，利用异构图神经网络（HeteroGNN）架构利用多边缘图中的社区检测来增强网络安全性。所提出的框架检测异常，例如很少交互的设备之间的意外通信模式，非工作时间的异常流量峰值，或设备上下文和基于知识的交互中的偏差。例如，在工业物联网环境中，可以从工作时间后设备社区内的意外通信中推断出未经授权的访问或恶意活动。我们的检测方法使用多边图来模拟各种交互（网络通信、上下文、知识），并应用社区检测来捕获稳定的图结构。通过将这些见解整合到一个HeteroGNN中，该框架有效地区分了异常边缘，同时保持了对动态网络条件的可扩展性和适应性。在CIC-ToN-IoT和CIC-IDS2017数据集上的实验评估表明，该框架具有卓越的准确性、精度和鲁棒性，使其成为保护物联网网络免受已知和新出现威胁的实用有效解决方案。

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引用次数: 0