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Unmanned aerial vehicle-enabled mobile edge computing for semantic communications 支持无人机的语义通信移动边缘计算

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

Computer Communications

Pub Date : 2026-02-15 Epub Date: 2026-01-05 DOI: 10.1016/j.comcom.2026.108411

Liyuan Xie , Wancheng Xie , Huabing Lu , Helin Yang

With the rapid evolution of wireless networks and the increasing demand for flexible communication and computing services, unmanned aerial vehicles (UAVs) have emerged as a promising solution to enhance the performance of these networks. This paper investigates a UAV-assisted mobile edge computing (MEC) system with semantic communication (SemCom) to improve the efficiency of wireless networks by transmitting only meaningful information, thereby reducing bandwidth and computational resource requirements. We propose a resource scheduling approach to minimize the weighted sum of overall latency for task processing and energy consumption under malicious jamming attacks. The approach jointly optimizes device scheduling, UAV trajectory, task offloading ratio, bandwidth allocation, and the number of transmitted SemCom symbols under different constraints. The optimization problem is complex and non-convex, involving ongoing decision-making due to constantly changing parameters. To address this challenge, we present a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm for real-time resource management. The proposed PPO-based resource scheduling approach effectively schedules both communication and computing resources to minimize the cost of the UAV-enabled wireless network against jamming attacks. Simulation-based performance analysis indicates that the PPO-based SemCom scheme reduces task execution latency and energy consumption compared to baseline approaches across various network scenarios. The proposed framework provides valuable insights into the design and optimization of UAV-assisted MEC systems with SemCom for enhanced wireless network performance in the presence of adversarial jamming.

随着无线网络的快速发展以及对灵活通信和计算服务的需求不断增加，无人机（uav）已经成为提高这些网络性能的一种有前途的解决方案。本文研究了一种具有语义通信（SemCom）的无人机辅助移动边缘计算（MEC）系统，通过仅传输有意义的信息来提高无线网络的效率，从而减少带宽和计算资源需求。我们提出了一种资源调度方法，以最小化在恶意干扰攻击下任务处理的总延迟和能量消耗的加权总和。该方法对不同约束条件下的设备调度、无人机轨迹、任务卸载比、带宽分配和发送SemCom符号数进行了联合优化。优化问题是一个复杂的非凸问题，涉及由于参数不断变化而导致的持续决策。为了解决这一挑战，我们提出了一种基于近端策略优化（PPO）的深度强化学习（DRL）算法，用于实时资源管理。所提出的基于ppo的资源调度方法有效地调度通信和计算资源，使无人机无线网络抵御干扰攻击的成本最小化。基于仿真的性能分析表明，与各种网络场景的基线方法相比，基于ppo的SemCom方案减少了任务执行延迟和能耗。所提出的框架为无人机辅助MEC系统的设计和优化提供了有价值的见解，该系统具有SemCom，可以在对抗性干扰存在的情况下增强无线网络性能。

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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 : 2026-02-15 Epub 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

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 : 2026-02-15 Epub 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 : 2026-02-15 Epub 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

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 : 2026-02-15 Epub 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

Deep reinforcement learning based energy management in full-duplex ultra dense networks with cell switching and radio resource allocation 基于深度强化学习的全双工超密集小区交换和无线资源分配网络能量管理

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

Computer Communications

Pub Date : 2026-02-15 Epub Date: 2026-01-21 DOI: 10.1016/j.comcom.2026.108430

Tahere Rahmati, Behrouz Shahgholi Ghahfarokhi

The exponential growth in traffic load and increasing number of connected devices have driven cellular networks to offer high capacity and to support massive access. Full-Duplex Ultra-Dense Networks (FD-UDNs) represent a promising technology to meet this demand in cellular networks. However, these networks encounter serious challenges concerning energy consumption and high levels of interference, which, if not properly managed, can adversely affect overall network performance. This paper presents a deep reinforcement learning-based solution for the problem of joint small base station (SBS) on/off switching and resource allocation, with the objective of maximizing energy efficiency and meeting quality of service (QoS) requirements. To reduce complexity, we decompose the problem into two sub-problems: 1) BS sleep management and 2) power and radio resource allocation. For BS sleep management, two approaches are proposed: centralized and distributed. In the centralized approach, the network decides about the sleep state of the SBSs. In the distributed approach, each SBS independently decides on its sleep state. Subsequently, by assigning users to the active stations, each BS allocates transmit power and radio resources to its users. The simulation results highlight performance of the proposed methods compared to the previous method in terms of both energy efficiency and user satisfaction rate. Additionally, the results show that our distributed sleep management method outperforms the centralized one.

流量负载的指数级增长和连接设备数量的增加推动了蜂窝网络提供高容量并支持大规模访问。全双工超密集网络（fd - udn）是一种很有前途的技术，可以满足蜂窝网络的这种需求。然而，这些网络遇到了严重的挑战，涉及能源消耗和高水平的干扰，如果管理不当，可能会对整体网络性能产生不利影响。本文提出了一种基于深度强化学习的联合小基站（SBS）开/关切换和资源分配问题的解决方案，以最大限度地提高能效和满足服务质量（QoS）要求为目标。为了降低复杂性，我们将问题分解为两个子问题：1)BS睡眠管理和2)功率和无线电资源分配。对于BS睡眠管理，提出了集中式和分布式两种方法。在集中式方法中，网络决定SBSs的休眠状态。在分布式方法中，每个SBS独立地决定其休眠状态。随后，通过将用户分配给活动电台，每个基站为其用户分配发射功率和无线电资源。仿真结果表明，与之前的方法相比，所提出的方法在能源效率和用户满意度方面的性能都有所提高。此外，结果表明，分布式睡眠管理方法优于集中式睡眠管理方法。

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引用次数: 0

L4D: An outlier-based learning framework for detecting event patterns in vehicular networks L4D：基于离群值的车辆网络事件模式检测学习框架

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

Computer Communications

Pub Date : 2026-02-15 Epub Date: 2026-01-29 DOI: 10.1016/j.comcom.2026.108436

Kawthar Zaraket , Hassan Harb , Ismail Bennis , Ali Jaber , Abdelhafid Abouaissa

This paper presents Learning4Detecting (L4D), an efficient learning framework designed for identifying unusual traffic incidents in Vehicular Ad hoc Networks (VANETs). L4D addresses the challenges of traffic outlier detection by combining advanced feature extraction techniques (LBP, GLCM, HOG) with a two-tier hybrid binary classification system, including event pattern recognition followed by a second verification classifier. This is complemented by a multi-class classification layer for event categorization. Unlike existing methods, L4D optimizes both preprocessing and classification to enhance detection accuracy, effectively handle unseen events, and capture spatio-temporal patterns, all while reducing computational overhead. Experimental results demonstrate that L4D outperforms existing techniques in both accuracy and efficiency when applied to real-world VANET datasets.

本文介绍了learning4detection (L4D)，这是一个高效的学习框架，旨在识别车辆自组织网络（VANETs）中的异常交通事件。L4D通过将先进的特征提取技术（LBP、GLCM、HOG）与两层混合二元分类系统（包括事件模式识别，然后是第二个验证分类器）相结合，解决了交通异常点检测的挑战。这是一个用于事件分类的多类分类层的补充。与现有方法不同，L4D优化了预处理和分类，以提高检测精度，有效处理未见事件，并捕获时空模式，同时减少了计算开销。实验结果表明，L4D在实际VANET数据集上的精度和效率都优于现有技术。

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 : 2026-02-15 Epub 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

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 : 2026-02-15 Epub 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

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 : 2026-02-15 Epub 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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