Ad Hoc Networks最新文献_第5页

Analysis of the computational costs of an evolutionary fuzzy rule-based internet-of-things energy management approach 基于进化模糊规则的物联网能源管理方法的计算成本分析

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2024-11-22 DOI: 10.1016/j.adhoc.2024.103715

M. Mikus , Ja. Konecny , P. Krömer , K. Bancik , Ji. Konecny , J. Choutka , M. Prauzek

This study presents an in-depth analysis of the computational costs associated with the application of an Evolutionary Fuzzy Rule-based (EFR) energy management system for Internet of Things (IoT) devices. In energy-harvesting IoT nodes, energy management is critical for sustaining long-term operation. The proposed EFR approach integrates fuzzy logic and genetic programming to autonomously control energy consumption based on available resources. The study evaluates the system’s computational performance, particularly focusing on processing time, RAM and flash memory usage across various hardware configurations. Different compiler optimization levels and floating-point unit (FPU) settings were also explored, comparing standard and pre-compiled algorithms. The results reveal computational times ranging from 2.43 to 5.23 ms, RAM usage peaking at 6.23 kB, and flash memory consumption between 19 kB and 32 kB. A significant reduction in computational overhead is achieved with optimized compiler settings and hardware FPU, highlighting the feasibility of deploying EFR-based energy management systems in low-power, resource-constrained IoT environments. The findings demonstrate the trade-offs between computational efficiency and energy management, with particular benefits observed in scenarios requiring real-time control in remote and energy-limited environments.

本研究深入分析了与物联网（IoT）设备应用基于进化模糊规则（EFR）的能源管理系统相关的计算成本。在能量收集型物联网节点中，能量管理对于维持长期运行至关重要。所提出的 EFR 方法集成了模糊逻辑和遗传编程，可根据可用资源自主控制能源消耗。本研究评估了系统的计算性能，尤其关注不同硬件配置下的处理时间、RAM 和闪存使用情况。研究还探讨了不同的编译器优化级别和浮点运算单元（FPU）设置，并对标准算法和预编译算法进行了比较。结果显示，计算时间在 2.43 至 5.23 ms 之间，RAM 使用量峰值为 6.23 kB，闪存消耗量在 19 kB 至 32 kB 之间。通过优化编译器设置和硬件 FPU，计算开销大幅减少，这凸显了在低功耗、资源受限的物联网环境中部署基于 EFR 的能源管理系统的可行性。研究结果表明了计算效率和能源管理之间的权衡，在需要在远程和能源受限环境中进行实时控制的场景中尤其明显。

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引用次数: 0

Efficient slicing scheme and cache optimization strategy for structured dependent tasks in intelligent transportation scenarios 智能交通场景中结构化依赖任务的高效分片方案和缓存优化策略

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2024-11-20 DOI: 10.1016/j.adhoc.2024.103699

Zhu Sifeng , Song Zhaowei , Zhu Hai , Qiao Rui

The challenges posed by structured large-scale tasks to resource-sensitive intelligent transportation systems have been acknowledged, particularly regarding the need to reduce delay and energy consumption during the caching and offloading processes. To address these challenges and improve the quality of service for vehicular users, a cloud–edge-end collaboration caching strategy (CACCSC) based on structured task content awareness was proposed in this paper. The dependencies among task fragments were modeled through fuzzy judgment criteria. In addition, a system delay model, an energy consumption model, and an edge server load balancing model were developed, along with a multi-objective optimization model that integrates system delay, energy consumption, and edge server load balancing variance. To solve this multi-objective optimization problem, an adaptive multi-objective optimization algorithm (MDE-NSGA-III) was developed, which combines an enhanced version of the Differential Evolution algorithm with improvements to the NSGA-III algorithm. Finally, it has been demonstrated through simulation experiments that when the number of users in the system reaches 35, the system delay, energy consumption, and load balancing variance of the MDE-NSGA-III optimization scheme proposed in this paper are 6.1%, 6.6%, and 25% lower than those of the NSGA-III scheme, 15.8%, 10%, and 41.7% lower than those of the NSGA-II scheme, and 62.7%, 20.7%, and 8.3% lower than those of the PeEA scheme.

结构化大规模任务给资源敏感型智能交通系统带来的挑战已得到认可，尤其是在缓存和卸载过程中减少延迟和能耗的需求。为了应对这些挑战并提高车辆用户的服务质量，本文提出了一种基于结构化任务内容感知的云边端协作缓存策略（CACCSC）。通过模糊判断标准对任务片段之间的依赖关系进行建模。此外，还建立了系统延迟模型、能耗模型和边缘服务器负载平衡模型，以及综合系统延迟、能耗和边缘服务器负载平衡方差的多目标优化模型。为了解决这个多目标优化问题，开发了一种自适应多目标优化算法（MDE-NSGA-III），它结合了差分进化算法的增强版和对 NSGA-III 算法的改进。最后，通过仿真实验证明，当系统用户数量达到 35 个时，本文提出的 MDE-NSGA-III 优化方案的系统延迟、能耗和负载平衡方差分别比 NSGA-III 方案低 6.1%、6.6% 和 25%，比 NSGA-II 方案低 15.8%、10% 和 41.7%，比 PeEA 方案低 62.7%、20.7% 和 8.3%。

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引用次数: 0

Impact of urban environments on FANET communication: A comparative study of propagation models 城市环境对FANET传播的影响：传播模型的比较研究

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2024-11-19 DOI: 10.1016/j.adhoc.2024.103695

Henok Gashaw , Jamie Wubben , Carlos T. Calafate , Fabrizio Granelli

The steady rise in the use of unmanned aerial vehicles (UAVs) is leading to the development of an ever-growing number of applications. In urban settings, efforts like the U-Space initiative in Europe are striving to standardize and regulate the operations of UAVs. To support these applications and further UAV research, it is essential to thoroughly understand UAV communication, both among and between UAVs. Nonetheless, we have identified a lack of studies on communication models, especially in urban areas where obstacles like tall buildings can disrupt communication. This study offers a comprehensive review of current measurement campaigns on channel models for aerial communication. In addition, we conducted experiments on (i) the separation distance between two UAVs, (ii) Multi-UAV communication and (iii) Multi-UAV to ground communication using three different city profiles in Spain (Valencia, Barcelona, and Madrid). To accomplish this, we utilized an advanced co-simulation framework that accurately models both UAV mobility (Ardusim) and communication (OMNeT++). Our results regarding UAV-to-UAV communication in a city environment indicate that: (i) the communication range, in our specific experiments, is limited to around 400 meters. Afterward, the Packet Delivery Ratio (PDR) declines significantly. (ii) Different communication models yield similar results. (iii) UAV-to-UAV communication becomes feasible at higher altitudes (e.g., 120 m), particularly in the presence of tall buildings. With respect to the Multi-UAV to ground communications, we can conclude that again, the altitude of the UAVs is paramount. Furthermore, increasing the number of UAVs providing service to the ground does increase the PDR, but only ever so slightly.

无人驾驶飞行器（uav）使用的稳步增长正在导致越来越多的应用程序的发展。在城市环境中，欧洲的U-Space倡议等努力正在努力使无人机的操作标准化和规范化。为了支持这些应用和进一步的无人机研究，必须彻底了解无人机之间和无人机之间的通信。尽管如此，我们发现缺乏关于交流模式的研究，特别是在城市地区，像高层建筑这样的障碍会干扰交流。本研究对当前航空通信信道模型的测量活动进行了全面的回顾。此外，我们在(i)两架无人机之间的分离距离，（ii）多无人机通信和（iii）多无人机对地面通信使用西班牙的三个不同的城市轮廓（瓦伦西亚，巴塞罗那和马德里）进行了实验。为了实现这一目标，我们利用了一个先进的联合仿真框架，精确地模拟了无人机的机动性（Ardusim）和通信（omnet++）。我们关于城市环境中无人机对无人机通信的结果表明：(i)在我们的具体实验中，通信范围被限制在400米左右。之后，PDR （Packet Delivery Ratio）显著下降。不同的传播模式产生相似的结果。（iii）无人机对无人机通信在更高高度（例如，120米）变得可行，特别是在高层建筑存在的情况下。关于多无人机对地面通信，我们可以再次得出结论，无人机的高度是至关重要的。此外，增加为地面提供服务的无人机数量确实会增加PDR，但只是一点点。

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引用次数: 0

A survey on massive IoT for water distribution systems: Challenges, simulation tools, and guidelines for large-scale deployment 配水系统大规模物联网调查：大规模部署的挑战、模拟工具和指南

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2024-11-16 DOI: 10.1016/j.adhoc.2024.103714

Antonino Pagano , Domenico Garlisi , Ilenia Tinnirello , Fabrizio Giuliano , Giovanni Garbo , Mariana Falco , Francesca Cuomo

This survey explores the convergence of Internet of Things (IoT) technologies with Water Distribution Systems (WDSs), focusing on large-scale deployments and the role of edge computing (EC). Effective water management increasingly relies on IoT monitoring, resulting in massive deployments and the generation of Big Data. While previous research has examined these topics individually, this work integrates them into a comprehensive analysis. We systematically reviewed 255 studies on IoT in WDS, identifying key challenges such as interoperability, scalability, energy efficiency, network coverage, and reliability. We also examined technologies like LPWAN and the growing use of EC for real-time data processing. In large-scale WDS scenarios, where vast amounts of data are generated, we highlighted the importance of technologies like NB-IoT, SigFox, and LoRaWAN due to their low power consumption and wide coverage. Based on our findings, we provide guidelines for sustainable, large-scale IoT deployment in WDS, emphasizing the need for edge data processing to reduce cloud dependency, improve scalability, and enable smarter cities and digital twins.

本调查探讨了物联网（IoT）技术与配水系统（WDS）的融合，重点关注大规模部署和边缘计算（EC）的作用。有效的水管理越来越依赖于物联网监控，这导致了大规模部署和大数据的产生。以往的研究对这些主题进行了单独研究，而本研究则将其整合为一项综合分析。我们系统回顾了 255 项有关 WDS 物联网的研究，确定了互操作性、可扩展性、能效、网络覆盖和可靠性等关键挑战。我们还研究了 LPWAN 等技术，以及在实时数据处理中越来越多地使用 EC 的情况。在产生大量数据的大规模 WDS 场景中，我们强调了 NB-IoT、SigFox 和 LoRaWAN 等技术的重要性，因为这些技术功耗低、覆盖范围广。基于我们的研究结果，我们为 WDS 中可持续的大规模物联网部署提供了指导，强调了边缘数据处理的必要性，以减少对云的依赖、提高可扩展性，并实现更智能的城市和数字双胞胎。

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引用次数: 0

RIS-assisted D2D communication in the presence of interference: Outage performance analysis and DNN-based prediction 存在干扰时的 RIS 辅助 D2D 通信：中断性能分析和基于 DNN 的预测

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2024-11-13 DOI: 10.1016/j.adhoc.2024.103703

Hamid Amiriara , Farid Ashtiani , Mahtab Mirmohseni , Masoumeh Nasiri-Kenari , Behrouz Maham

This paper analyzes the performance of reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication systems, focusing on addressing co-channel interference, a prevalent issue due to the frequency reuse of sidelink in the underlay in-band D2D communications. In contrast to previous studies that either neglect interference or consider it only at the user, our research investigates a performance analysis in terms of outage probability (OP) for RIS-assisted D2D communication systems considering the presence of interference at both the user and the RIS. More specifically, we introduce a novel integral-form expression for an exact analysis of OP. Additionally, we present a new accurate approximation expression for OP, using the gamma distributions to approximate the fading of both desired and interference links, thereby yielding a closed-form expression. Nevertheless, both derived expressions, i.e., the exact integral-form and the approximate closed-form, contain special functions, such as Meijer’s G-function and the parabolic cylinder function, which complicate real-time OP analysis. To circumvent this, we employ a deep neural network (DNN) for real-time OP prediction, trained with data generated by the exact expression. Moreover, we present a tight upper bound that quantifies the impact of interference on achievable diversity order and coding gain. We validate the derived expressions through Monte Carlo simulations. Our analysis reveals that while interference does not affect the system’s diversity order, it significantly degrades the performance by reducing the coding gain. The results further demonstrate that increasing the number of RIS’s reflecting elements is an effective strategy to mitigate the adverse effects of the interference on the system performance.

本文分析了可重构智能表面（RIS）辅助设备对设备（D2D）通信系统的性能，重点是解决同信道干扰问题，这是由于带内 D2D 底层通信中侧向链路的频率重用而普遍存在的问题。以往的研究要么忽略了干扰，要么只考虑了用户的干扰，与此不同的是，我们的研究从中断概率 (OP) 的角度对 RIS 辅助 D2D 通信系统进行了性能分析，同时考虑了用户和 RIS 存在的干扰。更具体地说，我们引入了一种新的积分形式表达式，用于对 OP 进行精确分析。此外，我们还提出了一种新的 OP 精确近似表达式，利用伽马分布来近似期望链路和干扰链路的衰减，从而得出闭式表达式。然而，这两种推导表达式，即精确积分形式和近似闭合形式，都包含一些特殊函数，如 Meijer 的 G 函数和抛物线圆柱体函数，这使得实时 OP 分析变得复杂。为了避免这种情况，我们采用了一种深度神经网络（DNN）来进行实时 OP 预测，该网络由精确表达式生成的数据训练而成。此外，我们还提出了一个严密的上限，可量化干扰对可实现的分集阶和编码增益的影响。我们通过蒙特卡罗模拟验证了推导出的表达式。我们的分析表明，虽然干扰不会影响系统的分集顺序，但会通过降低编码增益而显著降低性能。结果进一步证明，增加 RIS 反射元件的数量是减轻干扰对系统性能不利影响的有效策略。

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引用次数: 0

Age and energy aware data collection scheme for urban flood monitoring in UAV-assisted Wireless Sensor Networks 无人机辅助无线传感器网络中城市洪水监测的年龄和能量感知数据收集方案

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2024-11-12 DOI: 10.1016/j.adhoc.2024.103704

Mekala Ratna Raju , Sai Krishna Mothku , Manoj Kumar Somesula , Srilatha Chebrolu

Wireless Sensor Networks (WSNs) have become pivotal in numerous applications, including environmental monitoring, precision agriculture, and disaster response. In the context of urban flood monitoring, utilizing unmanned aerial vehicles (UAVs) presents unique challenges due to the dynamic and unpredictable nature of the environment. The primary challenges involve designing strategies that maximize data collection while minimizing the Age of Information (AoI) to ensure timely and accurate decision-making. Efficient data collection is crucial to capturing all relevant information and providing a comprehensive understanding of flood dynamics. Simultaneously, reducing AoI is essential, as outdated data can lead to delayed or incorrect responses, potentially worsening the situation. Addressing these challenges is critical for the effective use of WSNs in urban flood monitoring. Initially, we formulate the problem as a mixed integer non-linear programming (MINLP) problem. Further, it is solved using a Lagrangian-based branch and bound technique by converting it into an unconstrained problem. Then, for large-scale WSN, we propose a hybrid optimization technique which combines a genetic algorithm with a particle swarm optimization technique to simultaneously maximize the data collection and reduce the AoI of the collected data with the constraint of energy consumption of the UAVs. Simulation results demonstrate that our proposed algorithm outperforms existing approaches in terms of both data collection and AoI.

无线传感器网络（WSN）在环境监测、精准农业和灾难响应等众多应用中已变得举足轻重。在城市洪水监测方面，由于环境的动态性和不可预测性，使用无人飞行器（UAV）面临着独特的挑战。主要挑战包括设计既能最大限度地收集数据，又能最小化信息时代（AoI）的策略，以确保及时、准确地做出决策。高效的数据收集对于获取所有相关信息和全面了解洪水动态至关重要。同时，降低信息年龄也至关重要，因为过时的数据会导致延迟或错误的响应，从而可能使情况恶化。应对这些挑战对于在城市洪水监测中有效利用 WSN 至关重要。最初，我们将问题表述为混合整数非线性编程（MINLP）问题。然后，使用基于拉格朗日的分支和约束技术，将其转换为无约束问题，从而解决该问题。然后，针对大规模 WSN，我们提出了一种混合优化技术，该技术结合了遗传算法和粒子群优化技术，在无人机能耗的约束下，同时最大化数据采集和降低采集数据的 AoI。仿真结果表明，我们提出的算法在数据收集和 AoI 方面都优于现有方法。

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引用次数: 0

Cross-layer UAV network routing protocol for spectrum denial environments 针对频谱拒绝环境的跨层无人机网络路由协议

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2024-11-09 DOI: 10.1016/j.adhoc.2024.103702

Siyue Zheng , Xiaojun Zhu , Zhengrui Qin , Chao Dong

Unmanned Aerial Vehicles (UAVs), which connect to one another over wireless networks, are being used in warfare more frequently. Nevertheless, adversarial interference has the potential to disrupt wireless communication, and the UAV routing methods in use today struggle to handle interference. In this paper, we propose a Cross-Layer UAV Link State Routing protocol, CLUN-LSR, to combat against jamming attacks. CLUN-LSR features three designs. First, it obtains real-time spectrum status from the link layer. Such capabilities are provided by many existing radios, especially the ones in military applications, but are ignored by traditional routing protocols. Second, based on the cross-layer information, CLUN-LSR adds efficient routing functions during routing, including the use of the number of two-hop neighbor nodes as a metric for route selection. Third, CLUN-LSR selects nodes that are not in the interference area, thereby reducing network interruptions and improving data transmission efficiency. All table-driven routing protocols can apply CLUN-LSR for better performance. We apply CLUN-LSR to the existing routing protocol MP-OLSR and simulate it using a commercial network simulator. Simulation results show that our innovative routing protocol demonstrates superior performance compared to existing table-driven routing methods, particularly in terms of packet transmission rate and overall throughput.

通过无线网络相互连接的无人飞行器（UAV）在战争中的使用越来越频繁。然而，对抗性干扰有可能破坏无线通信，而目前使用的无人飞行器路由选择方法很难处理干扰。在本文中，我们提出了一种跨层无人机链路状态路由协议 CLUN-LSR，以对抗干扰攻击。CLUN-LSR 有三个特点。首先，它能从链路层获取实时频谱状态。现有的许多无线电设备，尤其是军事应用中的无线电设备都具备这种能力，但传统路由协议却忽略了这一点。其次，基于跨层信息，CLUN-LSR 在路由过程中增加了高效路由功能，包括使用两跳邻居节点数作为路由选择的度量。第三，CLUN-LSR 选择不在干扰区域内的节点，从而减少网络中断，提高数据传输效率。所有表驱动路由协议都可以应用 CLUN-LSR 以获得更好的性能。我们将 CLUN-LSR 应用于现有的路由协议 MP-OLSR，并使用商业网络模拟器进行了仿真。仿真结果表明，与现有的表驱动路由方法相比，我们的创新路由协议表现出更优越的性能，尤其是在数据包传输速率和总体吞吐量方面。

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引用次数: 0

JamBIT: RL-based framework for disrupting adversarial information in battlefields JamBIT：基于 RL 的战场对抗信息干扰框架

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2024-11-06 DOI: 10.1016/j.adhoc.2024.103697

Muhammad Salman , Taehong Lee , Ali Hassan , Muhammad Yasin , Kiran Khurshid , Youngtae Noh

During battlefield operations, military radios (hereafter nodes) exchange information among various units using a mobile ad-hoc network (MANET) due to its infrastructure-less and self-healing capabilities. Adversarial cyberwarfare plays a crucial role in modern combat by disrupting communication between critical nodes (i.e., nodes mainly responsible for propagating important information) to gain dominance over the opposing side. However, determining critical nodes within a complex network is an NP-hard problem. This paper formulates a mathematical model to identify important links and their connected nodes, and presents JamBIT, a reinforcement learning-based framework with an encoder–decoder architecture, for efficiently detecting and jamming critical nodes. The encoder transforms network structures into embedding vectors, while the decoder assigns a score to the embedding vector with the highest reward. Our framework is trained and tested on custom-built MANET topologies using the Named Data Networking (NDN) protocol. JamBIT has been evaluated across various scales and weighting methods for both connected node and network dismantling problems. Our proposed method outperformed existing RL-based baselines, with a 24% performance gain for smaller topologies (50–100 nodes) and 8% for larger ones (400–500 nodes) in connected node problems, and a 7% gain for smaller topologies and 15% for larger ones in network dismantling problems.

在战场行动中，军用无线电（以下简称节点）利用移动特设网络（MANET）在不同单位之间交换信息，因为该网络不需要基础设施，而且具有自愈能力。对抗性网络战通过破坏关键节点（即主要负责传播重要信息的节点）之间的通信，在现代作战中发挥着至关重要的作用，从而获得对对方的主导权。然而，在复杂的网络中确定关键节点是一个 NP 难度很高的问题。本文提出了一个数学模型来识别重要链接及其连接的节点，并介绍了基于强化学习、采用编码器-解码器架构的框架 JamBIT，用于高效地检测和干扰关键节点。编码器将网络结构转换为嵌入向量，而解码器则为奖励最高的嵌入向量分配分数。我们的框架使用命名数据网络（NDN）协议在定制的城域网拓扑上进行了训练和测试。JamBIT 针对连接节点和网络解体问题的不同规模和加权方法进行了评估。我们提出的方法优于现有的基于 RL 的基线方法，在连接节点问题中，较小拓扑（50-100 个节点）的性能提高了 24%，较大拓扑（400-500 个节点）的性能提高了 8%；在网络拆除问题中，较小拓扑的性能提高了 7%，较大拓扑的性能提高了 15%。

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引用次数: 0

Wireless sensor networks and machine learning centric resource management schemes: A survey 无线传感器网络和以机器学习为中心的资源管理方案：调查

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2024-11-05 DOI: 10.1016/j.adhoc.2024.103698

Gururaj S. Kori , Mahabaleshwar S. Kakkasageri , Poornima M. Chanal , Rajani S. Pujar , Vinayak A. Telsang

Wireless Sensor Network (WSN) is a heterogeneous, distributed network composed of tiny cognitive, autonomous sensor nodes integrated with processor, sensors, transceivers, and software. WSNs offer much to the sensing world and are deployed in predefined geographical areas that are out of human interventions to perform multiple applications. Sensing, computing, and communication are the main functions of the sensor node. However, WSNs are mainly constrained by limited resources such as power, computational speed, memory, sensing capability, communication range, and bandwidth. WSNs when shared for multiple tasks and applications, resource management becomes a challenging task. Hence, effective utilization of available resources is a critical issue to prolong the life span of sensor network. Current research has explored various methods for resources management in WSNs, but most of these approaches are traditional and often fall short in addressing the resource management issues during real-time applications. Resource management schemes involves in resource identification, resource scheduling, resource allocation, resource utilization and monitoring, etc. This paper aims to fill the gap by reviewing and analysing the latest Computational Intelligence (CI) techniques, particularly Machine Learning (ML) and Artificial Intelligence (AI). AIML has been applied to countless humdrum and complex problems arising in WSN operation and resource management. AIML algorithms increase the efficiency of the network and speed up the computational time with optimized utilization of the available resources. Therefore, this is a timely perspective on the ramifications of machine learning algorithms for autonomous WSN establishment, operation, and resource management.

无线传感器网络（WSN）是一种异构分布式网络，由集成了处理器、传感器、收发器和软件的微型认知自主传感器节点组成。WSN 为传感世界提供了很多便利，它被部署在预定义的地理区域，不受人为干预，可执行多种应用。传感、计算和通信是传感器节点的主要功能。然而，WSN 主要受限于有限的资源，如功率、计算速度、内存、传感能力、通信范围和带宽。当 WSN 被多个任务和应用共享时，资源管理就成为一项具有挑战性的任务。因此，有效利用可用资源是延长传感器网络寿命的关键问题。目前的研究探索了 WSN 中资源管理的各种方法，但这些方法大多比较传统，往往无法解决实时应用中的资源管理问题。资源管理方案涉及资源识别、资源调度、资源分配、资源利用和监控等方面。本文旨在通过回顾和分析最新的计算智能（CI）技术，特别是机器学习（ML）和人工智能（AI），填补这一空白。AIML 已被应用于 WSN 运行和资源管理中出现的无数琐碎和复杂问题。AIML 算法通过优化利用可用资源，提高了网络效率，加快了计算时间。因此，本文从机器学习算法对自主 WSN 建立、运行和资源管理的影响的角度进行了及时的探讨。

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引用次数: 0

V2X application server and vehicle centric distribution of commitments for V2V message authentication V2X 应用服务器和以车辆为中心的 V2V 信息验证承诺分配

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2024-11-04 DOI: 10.1016/j.adhoc.2024.103701

Mujahid Muhammad , Ghazanfar Ali Safdar

Safety applications, such as intersection collision warnings and emergency brake warnings, enhance road safety and traffic efficiency through periodic broadcast messages by vehicles and roadside infrastructure. While the Elliptic Curve Digital Signature Algorithm (ECDSA) is a widely used security approach, its performance limitations make it unsuitable for time-critical safety applications. As such, a symmetric cryptography-based technique called Timed Efficient Stream Loss-tolerant Authentication (TESLA) offers a viable alternative. However, applying standard TESLA in the context of vehicle-to-vehicle (V2V) communications has its own challenges. One challenge is the difficulty of distributing authentication information called commitments in the highly dynamic V2V environment. In this paper, we propose two novel solutions to this problem, namely, V2X Application Server (VAS)-centric and vehicle-centric. The former is an application-level solution that involves selective unicasting of commitments to vehicles by a central server, the VAS, and the latter is a reactive scheme that involves the periodic broadcast of commitments by the vehicles themselves. Extensive simulations are conducted using representatives of the real V2V environment to evaluate the performance of these approaches under different traffic situations; as well as performance comparison with a state-of-the-art distribution solution. The simulation results indicate that the VAS-centric solution is preferable for use in a TESLA-like V2V security scheme. It demonstrates desirable features, including timely delivery of commitments and high distribution efficiency, with over 95 % of commitments sent by the VAS are associated with relevant safety messages when compared with the vehicle-centric and state-of-the-art solutions. Formal security analysis, conducted using the Random Oracle Model (ROM), proves the correctness of our proposed distribution schemes. Additionally, an informal security analysis shows the resilience of the proposed schemes against various attacks, including impersonation, replay, and bogus commitment messages.

交叉路口碰撞警告和紧急制动警告等安全应用通过车辆和路边基础设施的定期广播信息来提高道路安全和交通效率。虽然椭圆曲线数字签名算法（ECDSA）是一种广泛使用的安全方法，但其性能限制使其不适合时间紧迫的安全应用。因此，一种名为 "定时高效流损容限验证"（TESLA）的对称加密技术提供了一种可行的替代方案。然而，在车对车 (V2V) 通信中应用标准 TESLA 有其自身的挑战。挑战之一是在高度动态的 V2V 环境中难以分发称为承诺的验证信息。本文针对这一问题提出了两种新颖的解决方案，即以 V2X 应用服务器 (VAS) 为中心和以车辆为中心。前者是一种应用级解决方案，包括由中央服务器（VAS）有选择地向车辆单播承诺；后者是一种反应式方案，包括由车辆本身定期广播承诺。我们使用真实 V2V 环境的代表进行了大量模拟，以评估这些方法在不同交通状况下的性能，并与最先进的分配解决方案进行性能比较。模拟结果表明，以 VAS 为中心的解决方案更适合用于类似 TESLA 的 V2V 安全方案。与以车辆为中心的解决方案和最先进的解决方案相比，VAS 发送的承诺中有 95% 以上与相关的安全信息有关，因此它具有及时交付承诺和高分配效率等理想特性。使用随机甲骨文模型（ROM）进行的正式安全分析证明了我们提出的分配方案的正确性。此外，非正式的安全分析表明，所提出的方案能够抵御各种攻击，包括冒名顶替、重放和伪造承诺信息。

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