Journal of Network and Computer Applications最新文献_第9页

Joint container orchestrating and request routing for serverless edge computing-based simulation applications 基于无服务器边缘计算的模拟应用的联合容器编排和请求路由

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-08-08 DOI: 10.1016/j.jnca.2025.104284

Yong Peng , Miao Zhang , Zhi Zhou , Hao Huang

Serverless edge computing dynamically invokes functions based on events, enabling on-demand code execution at the network edge and minimizing infrastructure management overhead. This computing paradigm is naturally suitable for event-driven distributed simulation applications, which involves frequent event interactions and stringent latency constraints. When running on top of geographically dispersed edge clouds, container orchestration and request routing have a significant impact on the performance of serverless edge computing-based simulations. In this paper, we propose an online orchestration framework for cross-edge serverless computing-based-simulations, which aims to minimize the resource cost and carbon emission under performance (i.e., latency) constraint, via jointly optimizing the container retention and requesting routing on-the-fly. This long-term cost minimization problem is difficult since it is NP-hard and involves future uncertain information. To simultaneously address these dual challenges, we carefully combine an online optimization technique with an approximate optimization method in a joint optimization framework. This framework first temporally decomposes the long-term time-coupling problem into a series of one-shot fractional problem via Lyapunov optimization, and then applies randomized dependent scheme to round the fractional solution to a near-optimal integral solution. The resulting online algorithm achieves an outstanding performance, as verified by extensive trace-driven simulations.

无服务器边缘计算基于事件动态调用函数，支持在网络边缘按需执行代码，并最大限度地减少基础设施管理开销。这种计算范式自然适用于事件驱动的分布式仿真应用程序，它涉及频繁的事件交互和严格的延迟约束。当在地理上分散的边缘云上运行时，容器编排和请求路由对基于无服务器边缘计算的模拟的性能有重大影响。在本文中，我们提出了一个基于跨边缘无服务器计算模拟的在线编排框架，旨在通过联合优化容器保留和动态请求路由，在性能（即延迟）约束下最大限度地降低资源成本和碳排放。这个长期成本最小化问题是困难的，因为它是np困难的，并且涉及到未来的不确定信息。为了同时解决这些双重挑战，我们在联合优化框架中仔细地将在线优化技术与近似优化方法相结合。该框架首先通过Lyapunov优化将长期时间耦合问题暂时分解为一系列单次分数阶问题，然后采用随机依赖格式将分数阶解舍入到一个近最优积分解。由此产生的在线算法取得了出色的性能，并通过大量的跟踪驱动仿真得到了验证。

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引用次数: 0

OpenDriver: An open-road driver state detection benchmark openriver：开放道路驾驶员状态检测基准

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-07-28 DOI: 10.1016/j.jnca.2025.104279

Delong Liu , Shichao Li , Tianyi Shi , Zhu Meng , Guanyu Chen , Zhicheng Zhao

Wearable physiological measurements offer a convenient and feasible method for real-time driver states monitoring. However, there are currently few driver physiological datasets in open-road scenarios, and the existing datasets suffer from issues such as poor signal quality, small sample sizes, and short data collection periods. In this paper, a large-scale multi-modal driving benchmark namely OpenDriver is elaborately constructed for driver state detection. Firstly, the OpenDriver encompasses 3278 driving trips, with a signal duration of approximately 4600 h. Two modalities of driving signals are collected: electrocardiogram (ECG) signals and six-axis motion data of the steering wheel from a motion measurement unit (IMU), which are recorded from 81 bus drivers and their vehicles. Secondly, three challenging tasks are carefully designed, and they are ECG signal quality assessment, individual biometric identification based on ECG signals, and physiological signal analysis in complex driving environments, respectively. Moreover, the corresponding baseline models and evaluation metrics are proposed to demonstrate the rationality and completeness of the dataset and tasks. First, in the quality assessment task, a noisy augmentation strategy is introduced to achieve realistic noise simulation, and then a larger-scale ECG signal dataset is generated. Second, an end-to-end contrastive learning framework is employed to effectively identify individual biometric. Finally, a comprehensive analysis of drivers’ Heart Rate Variability (HRV) features under different driving conditions gives multiple heuristic analytical conclusions. The OpenDriver benchmark and dataset will be publicly available at https://github.com/bdne/OpenDriver.

可穿戴式生理测量为实时监测驾驶员状态提供了一种方便可行的方法。然而，目前开放道路场景下驾驶员生理数据集较少，且存在信号质量差、样本量小、数据采集周期短等问题。本文精心构建了一个大规模的多模态驾驶基准OpenDriver，用于驾驶员状态检测。首先，OpenDriver包含3278次驾驶行程，信号持续时间约为4600小时。收集了两种模式的驾驶信号：心电图（ECG）信号和来自运动测量单元（IMU）的方向盘六轴运动数据，这些数据来自81名公交车司机和他们的车辆。其次，精心设计了心电信号质量评估、基于心电信号的个体生物特征识别和复杂驾驶环境下的生理信号分析三个具有挑战性的任务。在此基础上，提出了相应的基线模型和评价指标，以证明数据集和任务的合理性和完整性。首先，在质量评估任务中引入噪声增强策略，实现真实的噪声模拟，然后生成更大规模的心电信号数据集。其次，采用端到端对比学习框架有效识别个体生物特征。最后，综合分析驾驶员在不同驾驶条件下的心率变异性（HRV）特征，得出多个启发式分析结论。openriver基准和数据集将在https://github.com/bdne/OpenDriver上公开。

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引用次数: 0

Delay-aware partial task offloading using multicriteria decision model in IoT–fog–cloud networks 基于多准则决策模型的物联网雾云网络延迟感知部分任务卸载

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-07-26 DOI: 10.1016/j.jnca.2025.104278

Sushma S.A. , Madhunisha E. , Sourav Kanti Addya , Saifur Rahman , Shantanu Pal , Chandan Karmakar

Fog computing plays a prominent role in offloading computational tasks in heterogeneous environments since it provides less service delay than traditional cloud computing. The Internet of Things (IoT) devices cannot handle complex tasks due to less battery power, storage and computational capability. Full offloading has issues in providing efficient computation delay due to more response time and transmission cost. A suitable solution to overcome this problem is to partition the tasks into splittable subtasks. Considering multi-criteria decision parameters like processing efficiency and deadline helps to achieve efficient resource allocation and task assignment. The matching theory is applied to map task nodes to heterogeneous fog nodes and VMs for stability. Compared to baseline algorithms, proposed algorithms like Resource Allocation based on Processing Efficiency (RABP) and Task Assignment Based on Completion Time (TAC) are efficient enough to provide reasonable service delay and discard the non-beneficial tasks, i.e., tasks that do not execute within the deadline.

雾计算比传统云计算提供更少的服务延迟，在异构环境中卸载计算任务方面发挥着重要作用。由于电池电量、存储和计算能力不足，物联网（IoT）设备无法处理复杂的任务。由于更多的响应时间和传输成本，完全卸载在提供有效的计算延迟方面存在问题。克服这个问题的一个合适的解决方案是将任务划分为可拆分的子任务。考虑处理效率、截止日期等多准则决策参数有助于实现高效的资源分配和任务分配。将匹配理论应用于任务节点映射到异构雾节点和虚拟机中，以保证稳定性。与基线算法相比，我们提出的RABP （Resource Allocation based Processing Efficiency）和TAC （Task Assignment based Completion Time）等算法能够提供合理的服务延迟，并丢弃那些没有在截止日期内执行的非有益任务。

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引用次数: 0

NTP-INT: Network traffic prediction-driven in-band network telemetry for high-load switches 用于高负载交换机的网络流量预测驱动的带内网络遥测

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-07-22 DOI: 10.1016/j.jnca.2025.104265

Penghui Zhang , Hua Zhang , Yuqi Dai , Cheng Zeng , Jingyu Wang , Jianxin Liao

Due to its real-time visibility, In-band network telemetry (INT) is of great significance for network management. Nevertheless, with the rapid growth of network devices and services, targeted access to detailed network information in dynamic environments has become increasingly essential. This paper proposes an intelligent network telemetry system called NTP-INT to obtain more fine-grained network information on high-load switches. Specifically, NTP-INT consists of three modules: the network traffic prediction module, the topology pruning module, and the probe path planning module. Firstly, the network traffic prediction module adopts a Multi-Temporal Graph Neural Network (MTGNN) to predict future network traffic and identify high-load switches. Then, we design the topology pruning algorithm to generate a subnetwork covering all high-load switches to reduce the complexity of probe path planning. Finally, the probe path planning module uses an attention-mechanism-based Deep Reinforcement Learning (DRL) model to plan efficient probe paths in the subnetwork. Experimental results demonstrate that NTP-INT achieves more accurate telemetry on high-load switches while reducing control overhead by 50%. Additionally, the topology pruning strategy shortens training time by over 40%.

带内网络遥测（INT）由于其实时可见性，对网络管理具有重要意义。然而，随着网络设备和服务的快速增长，在动态环境中有针对性地获取详细的网络信息变得越来越重要。本文提出了一种名为NTP-INT的智能网络遥测系统，以获取高负载交换机上更细粒度的网络信息。具体来说，NTP-INT包括三个模块：网络流量预测模块、拓扑修剪模块和探针路径规划模块。首先，网络流量预测模块采用多时图神经网络（Multi-Temporal Graph Neural network， MTGNN）预测未来网络流量，识别高负载交换机。然后，设计拓扑剪枝算法，生成覆盖所有高负载交换机的子网，以降低探针路径规划的复杂性。最后，探测路径规划模块使用基于注意机制的深度强化学习（DRL）模型来规划子网中有效的探测路径。实验结果表明，NTP-INT在高负载开关上实现了更精确的遥测，同时减少了50%的控制开销。此外，拓扑修剪策略将训练时间缩短了40%以上。

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引用次数: 0

Intelligent congestion control in 5G URLLC Software-Defined Networks using adaptive resource management via Reinforced Dueling Deep Q-Networks 基于增强Dueling Deep Q-Networks自适应资源管理的5G URLLC软件定义网络智能拥塞控制

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-07-22 DOI: 10.1016/j.jnca.2025.104276

Vitawat Sittakul , Iacovos Ioannou , Prabagarane Nagaradjane , Vasos Vassiliou

Centralized control of Software Defined Networking (SDN) yields efficient management of network resources and offers a global perspective. However, centralized controllers have many performance and scalability issues, particularly given the rapid expansion of 5G connectivity. The latest demands on the transport network come from areas such as increasing RAN and mobile broadband service capacity, new 5G-enabled services and the dynamic deployment flexibility of the 5G Radio Access Network (RAN) split architecture, with its tight transport characteristics. These characteristics are particularly evident in the fronthaul segment of RAN, where latency and synchronization requirements pose significant challenges. Enhanced automation capabilities in the operations and management domain represent a key requirement to meet these challenges. Traditional machine learning (ML) techniques, which concentrate the training data and carry out sequential model learning over a sizable data set, are the main emphasis of current wireless network learning approaches. However, using a huge dataset for training is inefficient since it takes a lot of time and does not use resources or energy efficiently. Hence, this work focuses on Reinforced Dueling Deep Q-Network (RDDQN), a revolutionary approach to network slicing design for load prediction and resource management in data-driven workflows. Moreover, it can reduce congestion by adopting an Ultimatum queuing game theory-based scheduling mechanism in the controller. The proposed RDDQN achieves an average throughput of 579.34 kbps, an execution time of 12.57 s, goodput fairness of 94.56%, and delay fairness of 10.37 s across various parameters.

软件定义网络（SDN）的集中控制可以有效地管理网络资源，并提供全局视角。然而，集中式控制器存在许多性能和可扩展性问题，特别是考虑到5G连接的快速扩展。对传输网络的最新需求来自于不断增加的RAN和移动宽带业务容量、新的5G支持业务以及具有紧密传输特性的5G无线接入网（RAN）拆分架构的动态部署灵活性等领域。这些特征在无线局域网的前传部分尤其明显，在那里延迟和同步需求构成了重大挑战。操作和管理领域中增强的自动化能力是满足这些挑战的关键要求。传统的机器学习（ML）技术是当前无线网络学习方法的重点，它将训练数据集中起来，并在一个相当大的数据集上进行顺序模型学习。然而，使用庞大的数据集进行训练是低效的，因为它需要大量的时间，并且不能有效地使用资源或能量。因此，这项工作的重点是增强Dueling深度Q-Network (RDDQN)，这是一种革命性的网络切片设计方法，用于数据驱动工作流中的负载预测和资源管理。此外，在控制器中采用基于最后通牒排队博弈的调度机制，可以减少拥塞。提出的RDDQN实现了579.34 kbps的平均吞吐量、12.57 s的执行时间、94.56%的goodput公平性和10.37 s的延迟公平性。

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引用次数: 0

LASeR: Lightweight and secure remote user authentication protocol for Internet of Drones LASeR：用于无人机互联网的轻量级安全远程用户认证协议

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-07-21 DOI: 10.1016/j.jnca.2025.104275

Ilyes Ahmim , Feriel Bouakkaz , Abderrezak Rachedi , Nassira Ghoualmi-Zine

The Internet of Drones has emerged as a new paradigm in academia and industry due to its clear advantages in multiple domains, including the military, smart cities, smart agriculture, and more recently, during the COVID-19 pandemic. Remote users are increasingly eager to access real-time information collected by drones in specific areas. However, the wireless communication used for information exchange between remote users and drones is vulnerable to various security challenges due to its open nature. Moreover, drones are constrained by limited energy and resources, which hinders the effective use of traditional cryptographic methods that involve high computational and communication costs. To address these challenges in IoD (Internet of Drones) environments, we propose a novel lightweight authenticated key agreement protocol, named LASeR, which ensures secure exchanges between users and drones. LASeR relies exclusively on Elliptic Curve Cryptography (ECC), bit-wise XOR operation, and one-way hash functions, thereby achieving a lightweight design. Our protocol not only verifies the authenticity of the user but also establishes a session key between the user and drone for encrypted communications. Security evaluations demonstrate that LASeR is resilient against various security attacks and meets essential security requirements, notably forward and backward secrecy. Furthermore, we show that LASeR imposes significantly lower computational and communication costs compared to other relevant protocols.

无人机互联网因其在军事、智慧城市、智慧农业等多个领域的明显优势，以及最近在2019冠状病毒病大流行期间的优势，已成为学术界和工业界的新范式。远程用户越来越渴望获得无人机在特定区域收集的实时信息。然而，用于远程用户和无人机之间信息交换的无线通信由于其开放性，容易受到各种安全挑战。此外，无人机受限于有限的能源和资源，这阻碍了传统加密方法的有效使用，这些方法涉及高计算和通信成本。为了解决IoD（无人机互联网）环境中的这些挑战，我们提出了一种新的轻量级认证密钥协议，名为LASeR，它确保用户和无人机之间的安全交换。LASeR完全依赖于椭圆曲线加密（ECC）、逐位异或运算和单向哈希函数，从而实现了轻量级设计。我们的协议不仅验证了用户的真实性，而且在用户和无人机之间建立了一个会话密钥，用于加密通信。安全评估表明，LASeR具有抗各种安全攻击的弹性，满足基本的安全要求，特别是前向和后向保密。此外，我们表明，与其他相关协议相比，激光的计算和通信成本显着降低。

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引用次数: 0

Network traffic feature representation with contrastive learning for traffic engineering in hybrid software defined networks 混合软件定义网络中流量工程的对比学习网络流量特征表示

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-07-19 DOI: 10.1016/j.jnca.2025.104270

Weihong Zhou , Ruiyu Yang , Yingya Guo , Huan Luo

Traffic Engineering (TE) promotes the performance of hybrid Software Defined Networks (hybrid SDN) through optimizing traffic route selection. To handle dynamic network traffic, existing machine learning-based TE methods in hybrid SDNs focus on leveraging Reinforcement Learning (RL) to learn the mapping between the dynamic traffic demands and the traffic splitting ratios. However, with the huge network state space incurred by the dynamic network traffic and increasing network scale, it is hard for the RL-agent to learn and converge to the optimal mapping between traffic demands and traffic splitting ratios, thus the network performance suffers a degradation in dynamic network environment. To tackle this issue, we innovatively propose a TE approach that combines Contrastive learning (CL) and RL. Specifically, to reduce huge state space, we design to learn the mapping between network traffic features and routing policy rather than learning the mapping between traffic demand and routing policy. To well capture the features of traffic demands, we leverage CL to train a feature encoder for representing network traffic. We conduct extensive experiments on real network topologies datasets and the experimental results demonstrate that our proposed algorithm provides significant network performance improvements over state-of-arts.

TE （Traffic Engineering）是一种通过优化流量路由选择来提升混合SDN网络性能的技术。为了处理动态网络流量，混合sdn中现有的基于机器学习的TE方法侧重于利用强化学习（RL）来学习动态流量需求与流量分割比之间的映射关系。然而，随着动态网络流量带来的巨大网络状态空间和网络规模的不断扩大，RL-agent很难学习和收敛到流量需求与流量分割比之间的最优映射，从而导致动态网络环境下网络性能下降。为了解决这个问题，我们创新地提出了一种结合对比学习（CL）和强化学习（RL）的TE方法。具体来说，为了减少巨大的状态空间，我们设计学习网络流量特征和路由策略之间的映射关系，而不是学习流量需求和路由策略之间的映射关系。为了很好地捕获流量需求的特征，我们利用CL来训练用于表示网络流量的特征编码器。我们在真实的网络拓扑数据集上进行了大量的实验，实验结果表明，我们提出的算法比现有的算法提供了显着的网络性能改进。

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引用次数: 0

MTEAL: Network routing optimization of SD-WAN traffic engineering integrating multi-dimensional QoS metrics metal：集成多维QoS指标的SD-WAN流量工程的网络路由优化

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-07-19 DOI: 10.1016/j.jnca.2025.104272

Yuyu Chen , Anyang Gu , Lin Cui , Longxin Lin

In the field of network routing optimization, the metrics associated with quality of service (QoS) play a fundamental role in ensuring the reliable execution of high-priority applications and traffic. Recent advancements in software-defined wide area networks (SD-WAN) , have made it feasible to optimize complex objectives in network efficiency. Traditional methods, such as linear programming (LP), face challenges in modeling complex QoS metrics. Meanwhile, commonly used machine learning methods lack sufficient awareness of network topology. They also show insufficient robustness to dynamic traffic changes. To overcome these challenges, we propose MTEAL, a framework that integrates graph neural network (GNN) with multi-agent reinforcement learning (MARL). By leveraging its unique edge-edge GNN architecture, MTEAL effectively captures inter-link correlations and flow-level traffic dynamics, enabling robust optimization of diverse QoS metrics. We have developed a two-stage optimized reward function as well as a novel deep GNN module to enhance the model’s perception and optimization capability for various QoS metrics. Our experimental findings substantiate that MTEAL outperforms other models on three small-scale wide area networks (WAN) topologies, achieving improvements over TEAL and DRL-GNN models across multiple indicators and configurations. Optimally, the proposed approach exhibited improvements of 8.7% in flow arrival rate and 9.5% in average delay. In the event of link failures, it exhibited superior and more stable performance compared to the other two models. In situations involving diverse traffic patterns and unfamiliar topologies, our method shows greater robustness and generalization capability than TEAL.

在网络路由优化领域，与服务质量（QoS）相关的度量在确保高优先级应用和流量的可靠执行方面发挥着重要作用。软件定义广域网（SD-WAN）的最新进展使得优化网络效率中的复杂目标成为可能。传统的方法，如线性规划（LP），在建模复杂的QoS指标时面临挑战。同时，常用的机器学习方法对网络拓扑缺乏足够的认识。对动态流量变化的鲁棒性不足。为了克服这些挑战，我们提出了MTEAL，这是一个将图神经网络（GNN）与多智能体强化学习（MARL）相结合的框架。通过利用其独特的边缘GNN架构，MTEAL有效地捕获了链路间的相关性和流级流量动态，从而实现了各种QoS指标的鲁棒优化。我们开发了一个两阶段优化的奖励函数以及一个新的深度GNN模块，以增强模型对各种QoS指标的感知和优化能力。我们的实验结果证实，MTEAL在三种小规模广域网（WAN）拓扑结构上优于其他模型，在多个指标和配置上优于TEAL和DRL-GNN模型。优化后，该方法的流量到达率提高了8.7%，平均延迟率提高了9.5%。在链路故障的情况下，与其他两种模型相比，它表现出更优越、更稳定的性能。在涉及多种流量模式和不熟悉拓扑的情况下，我们的方法显示出比TEAL更强的鲁棒性和泛化能力。

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引用次数: 0

Machine learning-based IoT: Developing an energy-efficient and balanced clustering routing protocol (EEB-CR) for WSNs 基于机器学习的物联网：为wsn开发节能和平衡的集群路由协议（EEB-CR）

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-07-19 DOI: 10.1016/j.jnca.2025.104269

Nguyen Duy Tan , Thi-Thu-Huong Le

Wireless sensor networks (WSNs) have become integral to the Internet of Things (IoT), supporting diverse applications such as healthcare, environmental monitoring, intrusion detection, military surveillance, and industrial automation. However, sensor nodes (SNs) in WSNs are constrained by limited computational capabilities and finite energy reserves, making energy efficiency a critical concern for IoT applications deployed over WSN infrastructure. This study proposes an Energy-Efficient and Balanced Cluster-based Routing protocol (EEB-CR) to improve the operational longevity and energy distribution of WSNs. The EEB-CR protocol operates in three systematic phases: balanced cluster formation, cluster head (CH) selection, and energy-aware route discovery. Initially, balanced clusters are formed using an enhanced fuzzy

c

-means algorithm integrated with a mechanism to reduce uneven energy usage among SNs. Subsequently, CHs are optimally selected based on local node density, residual energy, and Euclidean distance to the base station (or gateway), and the CH role is periodically rotated among cluster members to promote fairness in energy consumption. In the final phase, the Ford–Fulkerson algorithm is employed to establish both intra- and inter-cluster data transmission paths with the objective of minimizing communication overhead from SNs to the base station (BS). Performance evaluation conducted through NS2 simulations demonstrates that EEB-CR achieves superior energy distribution balance and improved network stability compared to benchmark protocols such as LEACH-C, TEZEM, PECR, and FC-GWO.

无线传感器网络（wsn）已成为物联网（IoT）不可或缺的一部分，支持各种应用，如医疗保健、环境监测、入侵检测、军事监视和工业自动化。然而，WSN中的传感器节点（SNs）受到有限的计算能力和有限的能量储备的限制，使得能源效率成为部署在WSN基础设施上的物联网应用的关键问题。为了改善无线传感器网络的运行寿命和能量分配，提出了一种节能、均衡的基于集群的路由协议（EEB-CR）。EEB-CR协议在三个系统阶段运行：平衡簇形成，簇头（CH）选择和能量感知路由发现。首先，使用增强的模糊c均值算法形成平衡簇，该算法集成了一种机制，以减少SNs之间的能量使用不均匀。随后，基于本地节点密度、剩余能量和到基站（或网关）的欧氏距离来优选CHs，并在集群成员之间周期性地轮换CH角色，以促进能源消耗的公平性。在最后阶段，采用Ford-Fulkerson算法建立集群内和集群间的数据传输路径，目标是最小化从SNs到基站（BS）的通信开销。通过NS2仿真进行的性能评估表明，与LEACH-C、TEZEM、PECR和FC-GWO等基准协议相比，EEB-CR实现了更好的能量分布平衡，提高了网络稳定性。

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引用次数: 0

Fine-grained access control with decentralized delegation for collaborative healthcare systems 具有分散委托的细粒度访问控制，用于协作医疗保健系统

IF 7.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-07-18 DOI: 10.1016/j.jnca.2025.104273

Minghui Li , Jingfeng Xue , Yong Wang , Tianwei Lei , Zixiao Kong

Collaborative treatment has emerged as a crucial approach for improving the quality and efficiency of medical services in modern healthcare systems. To support this paradigm, secure access control over electronic health records (EHR) and flexible delegation of patient permissions are essential for enabling efficient, privacy-preserving data sharing. This paper proposes a fine-grained access control scheme with decentralized permission delegation tailored for medical collaboration scenarios. To ensure fine-grained access control, we adopt a hybrid encryption scheme that combines a dual-key regression tree with Identity-Based Encryption with Wildcard Key Derivation (WKD-IBE) for efficient and scalable key management. This integration enables access control based on data attributes rather than user identities and adheres to the principle of minimal data disclosure. To support decentralized permission delegation, we extend the WKD-IBE scheme to enable patients to authorize multiple doctors to grant access permissions collaboratively. This extension ensures controlled delegation by enforcing a predefined threshold of doctors and requiring consensus on the requested access scope. Additionally, we provide both theoretical and practical security analyses, along with an implementation to demonstrate the scheme’s real-world applicability. Experimental results demonstrate that our scheme achieves lower authorization latency and better scalability in collaborative healthcare scenarios while maintaining comparable encryption efficiency.

在现代医疗体系中，协作治疗已成为提高医疗服务质量和效率的重要途径。为了支持这种范例，对电子健康记录（EHR）的安全访问控制和灵活的患者权限授权对于实现高效、保护隐私的数据共享至关重要。本文提出了一种针对医疗协作场景的细粒度访问控制方案，该方案具有分散的权限授权。为了确保细粒度的访问控制，我们采用了一种混合加密方案，该方案将双密钥回归树与基于身份的加密和通配符密钥派生（WKD-IBE）相结合，以实现高效和可扩展的密钥管理。这种集成支持基于数据属性而不是用户身份的访问控制，并遵循最小化数据泄露的原则。为了支持分散的权限委托，我们扩展了WKD-IBE方案，使患者能够授权多个医生协作授予访问权限。这个扩展通过强制医生的预定义阈值和要求对请求的访问范围达成共识来确保受控委托。此外，我们还提供了理论和实践安全性分析，以及演示该方案在现实世界中的适用性的实现。实验结果表明，我们的方案在保持相当的加密效率的同时，在协作医疗场景中实现了更低的授权延迟和更好的可扩展性。

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引用次数: 0