Ad Hoc Networks最新文献_第8页

TOP: A forward and reverse offloading strategy in MEC-enabled Cooperative Vehicle–Infrastructure System TOP：基于mec的协同车辆基础设施系统中的正向和反向卸载策略

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-11-01 DOI: 10.1016/j.adhoc.2025.104058

Dun Cao , Weijia Xiao , Dan Cai , Yifan Yang , Fayez Alqahtani , Jin Wang

Enabled by Mobile Edge Computing (MEC) equipped on Base Station (BS), Collaborative Vehicle–Infrastructure Systems (CVIS) can provide efficient and reliable computing services for mobile vehicles. Vehicles can achieve intelligent applications such as autonomous driving by forward offloading tasks to base stations. However, most existing studies focus on the BS merely as a task receiver and integrator in CVIS, and neglecting its role as a task generator for information processing. When the BS is overloaded, CVIS will deteriorate drastically. Reverse offloading from BS to idle vehicles can relieve the pressure. Nonetheless, with the huge volume of tasks generated by both some task vehicles and the BS, how to select appropriate offloading and resource allocation strategies is a challenge. The situation will become more complex when facing heterogeneous nodes, i.e., task vehicles, the BS, and the idle vehicles in the range of the task vehicle or in the range of the BS but out of the task vehicle in dynamic scenarios. Thus, we propose an optimization problem joint multiple task offloading and resource partitioning to maximize the average task satisfaction of the system. To address the above proposed optimization problem, we propose Two-way Offloading & Partitioning (TOP) strategy, where a Two-way Collaborative Edge Node Dividing and Offloading Algorithm determines the cooperative edge nodes for different tasks and obtains the offloading strategy for each task set. Furthermore, we optimize the resource partitioning using the Genetic Algorithm to avoid resource wastage while enhancing the overall satisfaction of the system. Extensive experimental results show that our proposed TOP strategy improves average system satisfaction by up to 33% compared to other baseline strategies.

通过基站（BS）上的移动边缘计算（MEC），协同车辆基础设施系统（CVIS）可以为移动车辆提供高效可靠的计算服务。车辆可以通过将任务转发给基站来实现自动驾驶等智能应用。然而，现有的研究大多只关注脑电信号在CVIS中的任务接收和集成商作用，而忽视了脑电信号在信息加工中的任务生成作用。当BS过载时，CVIS将急剧恶化。倒车卸至怠速车辆可减轻压力。然而，由于某些任务车和BS都产生了大量的任务，如何选择合适的卸载和资源分配策略是一个挑战。当面对异构节点，即任务车辆、BS、在任务车辆范围内的空闲车辆或在BS范围内但不在任务车辆的动态场景时，情况会变得更加复杂。因此，我们提出了一个多任务卸载和资源分配相结合的优化问题，以最大限度地提高系统的平均任务满意度。为了解决上述优化问题，我们提出了双向卸载和卸载（TOP）策略，其中双向协作边缘节点划分和卸载算法确定不同任务的合作边缘节点，并获得每个任务集的卸载策略。此外，我们利用遗传算法优化资源分配，避免资源浪费，同时提高系统的整体满意度。广泛的实验结果表明，与其他基线策略相比，我们提出的TOP策略将平均系统满意度提高了33%。

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引用次数: 0

Decentralized opportunistic crowdsensing task allocation with global and local communication 分散的机会主义众感任务分配与全球和本地通信

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-11-01 DOI: 10.1016/j.adhoc.2025.104069

Chunyu Tu , Yanghui Chen , Zhiyong Yu , Fangwan Huang , Yuezhong Wu , Xianwei Guo , Chao Yang , Runhe Huang

Traditional opportunistic crowdsensing is usually managed by a central platform that assigns tasks, which imposes significant demands on the platform’s performance and increases the risk of privacy breaches for participants. To address these issues, this paper proposes a decentralized opportunistic sensing-based solution to achieve task allocation under a global budget constraint and maximize task coverage. Unlike traditional approaches, this solution allows participants to decide whether to join sensing tasks autonomously, forming a collaborative multi-agent system. In this decentralized environment, ensuring efficient task allocation while adhering to global constraints poses the main challenge. Since the communication conditions between participants directly affect the execution and coordination efficiency of global constraints, this paper designs corresponding global and local communication algorithms to address large-scale decentralized task allocation. Under global communication conditions, we adopt the concept of a cellular model and propose a decentralized genetic algorithm (D-GA). This approach utilizes global communication to evaluate the fitness of individuals, enabling coordinated problem-solving among agents. In addition, we consider local communication and design a decentralized soft-constrained probabilistic decision algorithm (DS-PD) to address scenarios where global communication is impractical. D-GA achieves 59.1%–81.2% task coverage, performing within 4.5%–5.8% of centralized integer linear programming while reducing execution time by over 96%. DS-PD significantly outperforms all methods under node failures, maintaining robust performance with only 2.1%–3.4% degradation.

传统的机会主义众感通常由一个分配任务的中央平台管理，这对平台的性能提出了很高的要求，并增加了参与者隐私泄露的风险。为了解决这些问题，本文提出了一种基于分散式机会感知的解决方案，以实现全局预算约束下的任务分配，并最大化任务覆盖率。与传统方法不同，该解决方案允许参与者自主决定是否加入感知任务，形成一个协作的多智能体系统。在这种分散的环境中，在遵守全局约束的同时确保有效的任务分配是主要的挑战。由于参与者之间的通信条件直接影响全局约束的执行和协调效率，本文设计了相应的全局和局部通信算法来解决大规模分散的任务分配问题。在全局通信条件下，采用细胞模型的概念，提出了一种分散的遗传算法（D-GA）。这种方法利用全局通信来评估个体的适应度，使代理之间能够协调解决问题。此外，我们考虑了本地通信，并设计了一个分散的软约束概率决策算法（DS-PD）来解决全球通信不切实际的情况。D-GA实现了59.1%-81.2%的任务覆盖率，在集中式整数线性规划的4.5%-5.8%范围内执行，同时将执行时间减少了96%以上。在节点故障情况下，DS-PD显著优于所有方法，仅以2.1%-3.4%的性能下降保持了稳健的性能。

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引用次数: 0

An attention-enhanced LSTM model for efficient network slicing in beyond 5G networks 一种用于超5G网络中高效网络切片的注意力增强LSTM模型

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-11-01 DOI: 10.1016/j.adhoc.2025.104070

Anjali Rajak, Rakesh Tripathi

Beyond 5G (B5G) networks are designed to significantly enhance network capacity and reduce latency by utilizing higher frequency spectrum bands. These networks rely on effective network slicing—the partitioning of physical infrastructure into independent logical networks, each optimized for specific service requirements to support a wide range of emerging application. However, B5G networks face substantial challenges due to massive data generation and stringent Service-Level Agreements (SLAs). This study introduces a Network Slice Framework (NSFrame) that addresses these challenges through a novel approach combining feature selection techniques with deep learning for efficient slice classification. NSFrame integrates Mutual Information, Shapley values from cooperative game theory, and Borda Count rank aggregation to overcome high dimensionality by selecting the most relevant features. These features are then processed by a multi-head attention enhanced long short-term memory model. Evaluated on the Unicauca IP Flow Version2 and 5G-SliciNdd datasets, NSFrame achieved classification accuracies of 99.79% and 98.67%, respectively, with strong generalization confirmed through 10-fold cross-validation. The proposed approach significantly outperforms existing methods, enhancing both quality of service and quality of experience, while enabling service providers to meet strict SLAs in B5G environments where softwarization and virtualization are essential for customized service delivery.

超5G （B5G）网络旨在通过利用更高的频谱带来显着增强网络容量并减少延迟。这些网络依赖于有效的网络切片——将物理基础设施划分为独立的逻辑网络，每个网络都针对特定的服务需求进行了优化，以支持广泛的新兴应用程序。然而，由于大量数据生成和严格的服务水平协议（sla）， B5G网络面临着巨大的挑战。本研究引入了一种网络切片框架（NSFrame），通过一种将特征选择技术与深度学习相结合的新方法来解决这些挑战，从而实现高效的切片分类。NSFrame结合了互信息、合作博弈论中的Shapley值和Borda Count秩聚合，通过选择最相关的特征来克服高维。这些特征随后通过多头注意增强长短期记忆模型进行处理。在Unicauca IP Flow Version2和5g - slicind数据集上进行评估，NSFrame的分类准确率分别达到99.79%和98.67%，通过10倍交叉验证证实了较强的泛化能力。提出的方法显著优于现有方法，提高了服务质量和体验质量，同时使服务提供商能够在B5G环境中满足严格的sla，其中软件化和虚拟化对于定制服务交付至关重要。

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引用次数: 0

RELTO: A reliability-oriented DRL approach with context-aware adaptive reward weighting for multi-objective task offloading in MEC 基于上下文感知自适应奖励加权的MEC多目标任务卸载可靠性导向DRL方法

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-10-30 DOI: 10.1016/j.adhoc.2025.104065

Anam Nasir, Xiang He, Teng Wang, Haomai Shi, Zhongjie Wang

Task offloading in Mobile Edge Computing (MEC) enables resource-constrained IoT devices to reduce latency and energy consumption while enhancing computational performance. However, designing effective offloading strategies presents a multi-objective optimization challenge, particularly in ensuring task reliability while optimizing energy efficiency and latency under dynamic conditions with unpredictable task failures caused by fluctuating computation demands and unstable communication links that severely degrade Quality of Service (QoS). Existing Deep Reinforcement Learning (DRL) approaches struggle to address these reliability-centered challenges, primarily due to their limited adaptability to dynamic reliability requirements, inadequate hybrid action space management, and insufficient handling of complex system state representations. To address these limitations, this work formulates a reliability-aware task offloading problem that explicitly integrates communication and computation reliability with latency and energy consumption into a multi-objective optimization formulation. To solve this optimization problem, the proposed Reliability Energy Latency Task Offloading (RELTO) algorithm employs Proximal Policy Optimization (PPO) within hybrid action spaces and incorporates a context-aware adaptive reward weighting mechanism driven by dual-attention architecture. The mechanism dynamically adjusts objective prioritization particularly emphasizing reliability based on real-time conditions, while attention-based state representation enables proactive decision-making through temporal pattern recognition. Extensive experiments in simulated MEC environments demonstrate that RELTO achieves optimal trade-offs across the key performance metrics, providing a more adaptive and robust solution for multi-objective task offloading.

移动边缘计算（MEC）中的任务卸载使资源受限的物联网设备能够减少延迟和能耗，同时提高计算性能。然而，设计有效的卸载策略是一个多目标优化挑战，特别是在动态条件下，由于计算需求波动和通信链路不稳定导致的不可预测的任务失败严重降低了服务质量（QoS），在保证任务可靠性的同时优化能效和延迟。现有的深度强化学习（DRL）方法难以解决这些以可靠性为中心的挑战，主要是因为它们对动态可靠性要求的适应性有限，混合动作空间管理不足，以及对复杂系统状态表示的处理不足。为了解决这些限制，本工作制定了一个可靠性感知任务卸载问题，该问题明确地将通信和计算可靠性与延迟和能耗集成到一个多目标优化公式中。为了解决这一优化问题，本文提出的可靠性能量延迟任务卸载（RELTO）算法在混合动作空间中采用了近端策略优化（PPO），并结合了由双注意力架构驱动的上下文感知自适应奖励加权机制。该机制动态调整客观优先级，特别强调基于实时条件的可靠性，而基于注意力的状态表示通过时间模式识别实现主动决策。在模拟MEC环境中进行的大量实验表明，RELTO在关键性能指标之间实现了最佳权衡，为多目标任务卸载提供了更具适应性和鲁棒性的解决方案。

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引用次数: 0

MobiAuth: Blockchain-driven decentralized authentication for enhanced security and privacy in mobile networks MobiAuth：区块链驱动的分散身份验证，用于增强移动网络的安全性和隐私性

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-10-30 DOI: 10.1016/j.adhoc.2025.104064

Narendra K. Dewangan , Gauri Shankar

Decentralized authentication in dynamic mobile networks faces significant challenges due to high node mobility, resource constraints, and vulnerabilities to side-channel attacks. In this work, we present MobiAuth, a blockchain-driven framework based on Hyperledger Iroha and OMNET++ that enables secure, peer-to-peer authentication using compact Ed25519 signatures and ephemeral session keys. Our protocol eliminates single points of failure by distributing trust across a permissioned ledger and employs constant-time cryptographic operations to thwart timing and power-analysis attacks. We validate MobiAuth through co-simulation in OMNET++ integrated with Iroha via a Python gRPC bridge and benchmark its performance with Hyperledger Caliper.

Simulation yields 95% packet delivery with an authentication latency ranging from 12 ms in the only OMNeT++ and baseline to 20–150ms in the full ledger-integrated system, and a ledger write throughput of 250tps. Comparative experiments demonstrate a 33% reduction in communication overhead and robust operation under random Control Point failures and Byzantine Access Node behavior. Analysis of on-device ledger synchronization further highlights practical storage growth and bandwidth requirements for long-term deployment. These results indicate that MobiAuth achieves strong security and privacy with modest energy impact, scalable performance, and compatibility with mobile devices in real-world network environments.

动态移动网络中的去中心化认证由于节点的高移动性、资源的有限性以及易受侧信道攻击而面临着巨大的挑战。在这项工作中，我们提出了MobiAuth，这是一个基于Hyperledger Iroha和omnet++的区块链驱动框架，它使用紧凑的Ed25519签名和临时会话密钥实现安全的点对点身份验证。我们的协议通过在许可的分类账上分配信任来消除单点故障，并采用恒定时间加密操作来阻止定时和功率分析攻击。我们通过Python gRPC桥在与Iroha集成的omnet++中进行联合仿真验证了MobiAuth，并使用Hyperledger Caliper对其性能进行了基准测试。仿真结果显示95%的数据包交付，身份验证延迟范围从仅有的omnet++和基线中的12 ms到完整的分类账集成系统中的20-150ms，分类账写吞吐量为250tps。对比实验表明，在随机控制点故障和拜占庭访问节点行为下，通信开销降低了33%，并且具有鲁棒性。对设备上分类账同步的分析进一步强调了长期部署的实际存储增长和带宽需求。这些结果表明，MobiAuth实现了强大的安全性和隐私性，具有适度的能源影响、可扩展的性能以及与现实网络环境中的移动设备的兼容性。

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引用次数: 0

Optimization of ship engine room communication based on ray tracing method and ZigBee technology 基于光线追踪法和ZigBee技术的船舶机舱通信优化

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-10-28 DOI: 10.1016/j.adhoc.2025.104067

Lige Yuan , Yangzhou Hao , Yinghua Li

The communication quality in the engine room of the ship is not ideal because of the complicated internal environment of the ship during the voyage at sea. The communication quality of the existing communication methods is not high, which is affected by the environment of the engine room, and the signal is unstable and the transmission fails. In view of this situation, a communication optimization method for ship engine room based on ZigBee technology and ray tracing method is proposed. Firstly, ZigBee technology was used to design the networking structure, and simulation software and ray final method were used to simulate the ship scene and radio wave transmission. In addition, the state of the communication signal is predicted by the short-time memory network and the communication scheme is adjusted in time. Genetic algorithm is used to optimize the antenna parameters. Finally, according to the simulation model, the design network structure and node layout parameters are optimized, and the communication optimization of the ship engine room is finally realized. The experimental results show that the transmission rate, data packet transmission delay time and transmission error rate indicators of the communication system after using the communication optimization scheme designed in the research are 214.05kbps, 20.14ms and 0.52 % respectively. And its node coverage exceeds 90 %. It can be seen that the communication optimization method designed in this study can effectively improve the quality of communication transmission data, improve the transmission efficiency, and provide reliable communication technology support for ship navigation. The research method provides a new idea for the design of wireless communication system in similar complex environment.

船舶在海上航行时，由于船舶内部环境复杂，导致机舱通信质量不理想。现有通信方式的通信质量不高，受机舱环境影响，信号不稳定，传输失败。针对这种情况，提出了一种基于ZigBee技术和光线追踪方法的船舶机舱通信优化方法。首先，采用ZigBee技术进行组网结构设计，利用仿真软件和ray final方法对船舶场景和无线电波传输进行仿真。此外，利用短时记忆网络预测通信信号的状态，及时调整通信方案。采用遗传算法对天线参数进行优化。最后，根据仿真模型对设计网络结构和节点布局参数进行优化，最终实现船舶机舱通信优化。实验结果表明，采用本研究设计的通信优化方案后，通信系统的传输速率、数据包传输延迟时间和传输错误率指标分别为214.05kbps、20.14ms和0.52%。节点覆盖率超过90%。可见，本研究设计的通信优化方法可以有效提高通信传输数据的质量，提高传输效率，为船舶导航提供可靠的通信技术支持。该研究方法为类似复杂环境下的无线通信系统设计提供了一种新的思路。

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引用次数: 0

Authentication protocol for the Internet of Drones with fog computing based on aggregate signatures for forest inventory 基于集合签名的无人机互联网雾计算森林清查认证协议

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-10-27 DOI: 10.1016/j.adhoc.2025.104034

Manuela de Jesus Sousa , Paulo Roberto L. Gondim , Sandra Sendra , Jaime Lloret

The Internet of Drones (IoD) has become increasingly important in applications such as forest inventory, leveraging advanced sensors and internet connectivity to enable efficient data collection. Compared to traditional methods, IoD offers superior cost-effectiveness. However, its reliance on public channels, unreliable connectivity, and dynamic environments poses significant security and privacy challenges. Safeguarding forest inventory data is essential to maintaining accuracy, preventing unauthorized access, and mitigating the risk of data manipulation, which can lead to suboptimal management decisions. To address these concerns, it is essential to design a lightweight authentication protocol that secures IoD communication, accounts for network bandwidth limitations and scalability, and supports integration with emerging technologies. This manuscript introduces a new Authentication and Key Agreement (AKA) protocol specifically designed for the Internet of Drones (IoD), leveraging asymmetric cryptography and aggregate signatures to enhance security and privacy in forest inventories with fog computing. Its robustness was confirmed through informal and formal security analyses by the AVISPA tool and the ROR model, demonstrating resistance to known attacks and superior communication, computational, and energy performance compared to existing protocols.

无人机互联网（IoD）在森林清查等应用中变得越来越重要，利用先进的传感器和互联网连接来实现高效的数据收集。与传统方法相比，IoD具有更高的成本效益。然而，它对公共通道的依赖、不可靠的连接和动态环境构成了重大的安全和隐私挑战。保护森林清查数据对于保持准确性、防止未经授权的访问和减轻可能导致次优管理决策的数据操纵风险至关重要。为了解决这些问题，必须设计一个轻量级的身份验证协议，以保护IoD通信，考虑网络带宽限制和可扩展性，并支持与新兴技术的集成。本文介绍了一种专门为无人机互联网（IoD）设计的新的身份验证和密钥协议（AKA）协议，利用非对称加密和聚合签名来增强雾计算森林清单的安全性和隐私性。AVISPA工具和ROR模型通过非正式和正式的安全分析证实了它的鲁棒性，与现有协议相比，它展示了对已知攻击的抵抗力，以及更好的通信、计算和能源性能。

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引用次数: 0

Cross-Technology Interference awareness for multi-user OFDMA scheduling in IEEE 802.11ax IEEE 802.11ax中多用户OFDMA调度的跨技术干扰感知

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-10-27 DOI: 10.1016/j.adhoc.2025.104057

Thijs Havinga, Xianjun Jiao, Wei Liu, Baiheng Chen, Robbe Gaeremynck, Ingrid Moerman

Cross-Technology Interference (CTI) significantly degrades the performance of heterogeneous wireless communication systems operating within a shared spectrum. Traditional mitigation techniques, such as Clear Channel Assessment (CCA), often fail due to, amongst others, varying bandwidth and detection threshold. Orthogonal Frequency Division Multiple Access (OFDMA), introduced to Wi-Fi in IEEE 802.11ax (Wi-Fi 6), allows multiple users to be served simultaneously using distinct subcarrier sets, known as Resource Units (RUs), providing enhanced flexibility in the frequency domain. This paper explores and evaluates several Wi-Fi 6 compliant methods for multi-user OFDMA scheduling with CTI awareness. Through simulations, we assess the benefits of different techniques in various scenarios, in terms of either total throughput or average latency. To effectively apply the mitigation techniques, we propose a methodology that incorporates CTI feedback from stations and real-time CCA per RU. Given that commercial Wi-Fi 6 access points lack control over low-level OFDMA features, we use openwifi, a full-stack Wi-Fi transceiver running on software-defined radio, to implement the CTI-aware OFDMA scheduler. Real-life experiments validate the effectiveness of the scheduler and confirm its real-time performance capabilities.

跨技术干扰（CTI）会显著降低在共享频谱内运行的异构无线通信系统的性能。传统的缓解技术，如清晰通道评估（CCA），往往由于带宽和检测阈值的变化等原因而失败。在IEEE 802.11ax （Wi-Fi 6）中引入的正交频分多址（OFDMA）允许使用不同的子载波集（称为资源单元（ru））同时为多个用户提供服务，从而在频域提供增强的灵活性。本文探索并评估了几种符合Wi-Fi 6的具有CTI感知的多用户OFDMA调度方法。通过模拟，我们从总吞吐量或平均延迟方面评估了不同技术在各种场景中的优势。为了有效地应用缓解技术，我们提出了一种方法，该方法结合了来自站点的CTI反馈和每个RU的实时CCA。鉴于商用Wi-Fi 6接入点缺乏对低级OFDMA功能的控制，我们使用openwifi，一种运行在软件定义无线电上的全栈Wi-Fi收发器，来实现cti感知的OFDMA调度器。实际实验验证了调度程序的有效性，并证实了其实时性能。

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引用次数: 0

MQTTEEB-D: A high-fidelity benchmark for real-time MQTT anomaly detection using machine learning techniques MQTTEEB-D：使用机器学习技术进行实时MQTT异常检测的高保真基准

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-10-27 DOI: 10.1016/j.adhoc.2025.104062

Hamza Allaga , Mohamed Biniz , Abderrazak Farchane

Message Queuing Telemetry Transport (MQTT) is essential for resource-constrained Internet of Things (IoT) environments; however, its widespread adoption has introduced significant security vulnerabilities. Although machine learning (ML) offers a promising solution for anomaly detection, existing models are often hindered by unrealistic data, severe class imbalances, and high computational costs. To address these limitations, we present a comprehensive ML framework for MQTT anomaly detection benchmarked on MQTTEEB-D, a high-fidelity dataset from a physical IoT testbed. Our framework evaluates a diverse suite of algorithms, including tree ensembles and boosting methods, on both original imbalanced and balanced data. We assessed performance using standard metrics, imbalance-stable metrics such as the Matthews Correlation Coefficient (MCC), and a Performance–Efficiency Score (PES) to quantify the trade-off between predictive power and computational cost. Our results establish a new state-of-the-art, with the top models achieving over 98.8% accuracy and F1-score. These models also yielded dramatic efficiency gains, including a 43-fold reduction in training time and a 299-fold speedup in inference latency over previous benchmarks. Critically, we found that a model’s resilience to class imbalance is more vital for real-world deployment than its peak performance on artificially balanced data. Simpler tree-based models remained robust under imbalanced conditions, where more complex algorithms failed. These findings provide a new benchmark and reorient model selection towards efficient, reliable, and deployable IoT security systems.

消息队列遥测传输（MQTT）对于资源受限的物联网（IoT）环境至关重要；然而，它的广泛采用带来了重大的安全漏洞。尽管机器学习（ML）为异常检测提供了一个很有前途的解决方案，但现有模型经常受到不现实数据、严重的类不平衡和高计算成本的阻碍。为了解决这些限制，我们提出了一个全面的MQTT异常检测ML框架，该框架以MQTTEEB-D为基准，MQTTEEB-D是来自物理物联网测试平台的高保真数据集。我们的框架评估了一套不同的算法，包括树集成和增强方法，对原始不平衡和平衡数据。我们使用标准指标、不平衡稳定指标（如马修斯相关系数（MCC））和性能效率评分（PES）来评估性能，以量化预测能力和计算成本之间的权衡。我们的研究结果建立了一种新的技术水平，顶级模型的准确率超过98.8%，得分为f1。这些模型还产生了显著的效率提升，包括与以前的基准测试相比，训练时间减少了43倍，推理延迟加快了299倍。关键的是，我们发现模型对类不平衡的弹性对于实际部署来说比它在人为平衡数据上的峰值性能更为重要。在不平衡条件下，更简单的基于树的模型仍然是健壮的，而更复杂的算法则失败了。这些发现提供了一个新的基准，并将模型选择重新定位为高效、可靠和可部署的物联网安全系统。

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引用次数: 0

Optimizing energy-efficient routing in Mobile Internet of Things (MIoT) networks using Grey Wolf Optimization and Recurrent Neural Networks 利用灰狼优化和递归神经网络优化移动物联网（MIoT）网络中的节能路由

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-10-25 DOI: 10.1016/j.adhoc.2025.104047

Seyed Salar Sefati , Sanda Osiceanu Maiduc , Bahman Arasteh , Winfred Ofoe Larkotey , Asgarali Bouyer , Wali Ullah Khan

The Mobile Internet of Things (MIoT) represents a significant evolution of traditional IoT by enabling seamless connectivity for mobile devices and sensors in dynamic environments. Given the resource constraints and mobility challenges in MIoT networks, developing adaptive and energy-efficient routing strategies is important. This paper proposes a novel routing protocol that integrates Grey Wolf Optimization (GWO) and Recurrent Neural Networks (RNNs) to enhance energy efficiency, reliability, and responsiveness in MIoT systems. The protocol features dynamic clustering, predictive traffic load balancing, and multi-objective optimization for Cluster Head (CH) selection, where RNNs forecast traffic trends and GWO optimizes routing paths. Simulation results demonstrate that the proposed method reduces energy consumption, lowers end-to-end delays, and improves packet delivery ratio (PDR) and network reliability under both static and mobile conditions. Compared to existing methods such as the Krill Herd (KH) algorithm, Dynamic Multi-Sink Routing Protocol (DMS-RP), and Evolutionary Fuzzy Rule-based (EFR) models, the proposed solution exhibits superior performance, validating its scalability and effectiveness for real-world MIoT applications.

移动物联网（MIoT）通过在动态环境中实现移动设备和传感器的无缝连接，代表了传统物联网的重大演变。考虑到物联网网络中的资源限制和移动性挑战，开发自适应和节能的路由策略非常重要。本文提出了一种集成灰狼优化（GWO）和循环神经网络（rnn）的新型路由协议，以提高物联网系统的能源效率、可靠性和响应性。该协议具有动态聚类、预测流量负载均衡和多目标簇头选择的特点，其中rnn预测流量趋势，GWO优化路由路径。仿真结果表明，该方法在静态和移动两种情况下都能降低能耗，降低端到端时延，提高分组分发率（PDR）和网络可靠性。与Krill Herd （KH）算法、动态多汇路由协议（DMS-RP）和进化模糊规则（EFR）模型等现有方法相比，该解决方案表现出卓越的性能，验证了其在实际工业物联网应用中的可扩展性和有效性。

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