Pub Date : 2025-12-26DOI: 10.1016/j.adhoc.2025.104126
Xuejian Chi , Xiaoya Jin , Dapeng Jiao
Mobile virtual reality (MVR), leveraging edge computing’s proximity to end devices, enables real-time responsiveness and improves users’ Quality of Experience (QoE), and has therefore attracted growing attention. However, the mobility and instability of edge environments render edge servers vulnerable to network attacks or hardware failures, undermining service continuity. Prior studies have devoted limited attention to the systemic impact of such failures. In this work, we study the problem of robust service deployment and scheduling, focusing on how to deploy service components to mitigate the degradation in user experience caused by server failures. This problem presents two challenges: (i) seamless takeover of users served by failed servers; and (ii) balancing robustness gains against the total system cost. To overcome these challenges, we design a two-stage service placement strategy based on deep reinforcement learning (TSP-DRL). In the first stage, an iterative search groups neighboring edge servers to identify those that can take over users from failed servers. In the second stage, a deep reinforcement learning agent models the complex relationship between robustness gains and total cost, enabling efficient service-component placement in dynamic environments. Finally, real trace-based data simulations indicate that, compared with state-of-the-art methods, TSP-DRL improves robustness gains by 12%–23% while reducing total system cost by 11%–14%.
{"title":"TSP-DRL: High-robustness service deployment for mobile virtual reality","authors":"Xuejian Chi , Xiaoya Jin , Dapeng Jiao","doi":"10.1016/j.adhoc.2025.104126","DOIUrl":"10.1016/j.adhoc.2025.104126","url":null,"abstract":"<div><div>Mobile virtual reality (MVR), leveraging edge computing’s proximity to end devices, enables real-time responsiveness and improves users’ Quality of Experience (QoE), and has therefore attracted growing attention. However, the mobility and instability of edge environments render edge servers vulnerable to network attacks or hardware failures, undermining service continuity. Prior studies have devoted limited attention to the systemic impact of such failures. In this work, we study the problem of robust service deployment and scheduling, focusing on how to deploy service components to mitigate the degradation in user experience caused by server failures. This problem presents two challenges: (i) seamless takeover of users served by failed servers; and (ii) balancing robustness gains against the total system cost. To overcome these challenges, we design a two-stage service placement strategy based on deep reinforcement learning (TSP-DRL). In the first stage, an iterative search groups neighboring edge servers to identify those that can take over users from failed servers. In the second stage, a deep reinforcement learning agent models the complex relationship between robustness gains and total cost, enabling efficient service-component placement in dynamic environments. Finally, real trace-based data simulations indicate that, compared with state-of-the-art methods, TSP-DRL improves robustness gains by 12%–23% while reducing total system cost by 11%–14%.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104126"},"PeriodicalIF":4.8,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1016/j.adhoc.2025.104131
Zijiang Yang, Qi Tao
With the rapid growth of the automotive industry, the Internet of Vehicles (IoV) has become a vital platform for information exchange among vehicles and between vehicles and roadside infrastructure. However, due to complex network structures and high vehicle mobility, such exchanges are not always reliable. Trust management in IoV is therefore critical to ensuring safety and reliability. This paper reviews research on IoV trust management mechanisms. It first introduces the basic IoV models, and then identifies an evolutionary trajectory based on the existing literature: from early trust management models, to growing attention to privacy protection, and more recently to the adoption of emerging technologies such as blockchain for decentralized trust management. Based on this trajectory, the paper analyzes existing work from three perspectives: trust management models, privacy protection, and blockchain-IoV integration. Furthermore, this article systematically surveys experimental simulation platforms and evaluation indicators to clarify validation practices. Finally, by synthesizing the research landscape and highlighting key limitations and bottlenecks, the paper outlines future directions and priorities for IoV trust management in light of both technological advances and application needs.
{"title":"A survey of trust management mechanisms in the Internet of Vehicles","authors":"Zijiang Yang, Qi Tao","doi":"10.1016/j.adhoc.2025.104131","DOIUrl":"10.1016/j.adhoc.2025.104131","url":null,"abstract":"<div><div>With the rapid growth of the automotive industry, the Internet of Vehicles (IoV) has become a vital platform for information exchange among vehicles and between vehicles and roadside infrastructure. However, due to complex network structures and high vehicle mobility, such exchanges are not always reliable. Trust management in IoV is therefore critical to ensuring safety and reliability. This paper reviews research on IoV trust management mechanisms. It first introduces the basic IoV models, and then identifies an evolutionary trajectory based on the existing literature: from early trust management models, to growing attention to privacy protection, and more recently to the adoption of emerging technologies such as blockchain for decentralized trust management. Based on this trajectory, the paper analyzes existing work from three perspectives: trust management models, privacy protection, and blockchain-IoV integration. Furthermore, this article systematically surveys experimental simulation platforms and evaluation indicators to clarify validation practices. Finally, by synthesizing the research landscape and highlighting key limitations and bottlenecks, the paper outlines future directions and priorities for IoV trust management in light of both technological advances and application needs.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104131"},"PeriodicalIF":4.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.adhoc.2025.104124
Huogen Yang , Yiwen Hu , Zhongming Yang , Xiaohui Yang , Guangxue Yue
The introduction of Mobile edge computing enables resource-constrained maritime terminal users to access low-latency computing services; however, the dynamic nature of the marine environment and scarce resources render traditional computation offloading strategies inadequate for meeting actual demands, making task offloading a critical issue for achieving prompt and efficient service with optimal resource utilization. In particular, fine-tuning the offloading decision process is crucial for enhancing network stability and extending system endurance. To address these challenges, this paper proposes a deep reinforcement learning-based task offloading method for maritime edge computing. The method derives the optimal transmission power for task offloading and incorporates the power allocation problem into the offloading decision framework, ensuring that offloading decisions are efficiently executed within a specific power range. We model the task offloading problem as a Markov decision process, and based on this formulation, we design an improved Double Deep Q-Network (Double DQN) Energy-Delay Tradeoff Optimization algorithm (ID-EDTO), which enables the system to dynamically obtain state feedback from task requests and adapt its offloading strategies accordingly. Experimental results demonstrate that the proposed method outperforms both traditional baseline methods, such as random selection, Lyapunov optimization, and joint resource allocation, as well as DRL based algorithms including PPO, SAC, and A3C, in terms of reducing latency and energy consumption.
移动边缘计算的引入使资源受限的海上终端用户能够访问低延迟的计算服务；然而，由于海洋环境的动态性和资源的稀缺性，传统的计算卸载策略已不能满足实际需求，任务卸载成为实现资源优化利用的快速高效服务的关键问题。特别是，微调卸载决策过程对于增强网络稳定性和延长系统耐久性至关重要。为了解决这些问题，本文提出了一种基于深度强化学习的海上边缘计算任务卸载方法。该方法推导出任务卸载的最优传输功率，并将功率分配问题纳入到卸载决策框架中，保证了任务卸载决策在特定功率范围内有效执行。将任务卸载问题建模为马尔可夫决策过程，并在此基础上设计了一种改进的双深度q -网络（Double Deep Q-Network， DQN）能量-延迟权衡优化算法（ID-EDTO），使系统能够从任务请求中动态获取状态反馈，并相应地调整卸载策略。实验结果表明，该方法在降低延迟和能耗方面优于随机选择、Lyapunov优化、联合资源分配等传统基线方法，也优于PPO、SAC、A3C等基于DRL的算法。
{"title":"Collaborative DRL-driven task offloading for maritime edge computing","authors":"Huogen Yang , Yiwen Hu , Zhongming Yang , Xiaohui Yang , Guangxue Yue","doi":"10.1016/j.adhoc.2025.104124","DOIUrl":"10.1016/j.adhoc.2025.104124","url":null,"abstract":"<div><div>The introduction of Mobile edge computing enables resource-constrained maritime terminal users to access low-latency computing services; however, the dynamic nature of the marine environment and scarce resources render traditional computation offloading strategies inadequate for meeting actual demands, making task offloading a critical issue for achieving prompt and efficient service with optimal resource utilization. In particular, fine-tuning the offloading decision process is crucial for enhancing network stability and extending system endurance. To address these challenges, this paper proposes a deep reinforcement learning-based task offloading method for maritime edge computing. The method derives the optimal transmission power for task offloading and incorporates the power allocation problem into the offloading decision framework, ensuring that offloading decisions are efficiently executed within a specific power range. We model the task offloading problem as a Markov decision process, and based on this formulation, we design an improved Double Deep Q-Network (Double DQN) Energy-Delay Tradeoff Optimization algorithm (ID-EDTO), which enables the system to dynamically obtain state feedback from task requests and adapt its offloading strategies accordingly. Experimental results demonstrate that the proposed method outperforms both traditional baseline methods, such as random selection, Lyapunov optimization, and joint resource allocation, as well as DRL based algorithms including PPO, SAC, and A3C, in terms of reducing latency and energy consumption.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104124"},"PeriodicalIF":4.8,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1016/j.adhoc.2025.104123
Yan Zhao, Lintao Huang, Mengzhe Ren, Hanyang Shi
In a wireless sensor network (WSN), achieving efficient data transmission while extending network lifetime remains a critical issue. Sensor nodes may fail due to energy depletion during mass data transmission, which results in frequent changes in the network topology. The frequent topology changes not only increase the complexity of network management and maintenance, but also severely impair both energy efficiency and quality-of-service (QoS). To address these challenges, in this paper, we construct a small-world WSN (SW-WSN) and address its joint optimization problem of energy efficiency and QoS. In the constructed SW-WSN, long-range (LoRa) links are used to replace some of the conventional links between sensor nodes as well as between nodes and the gateway, and a small-world model is introduced to reduce the intermediate hop count for data transmission. A Q-learning-based adaptive link allocation algorithm is proposed to map all conventional and LoRa links to state space and action space respectively and learn to form state–action pairs through training, which determines an optimal link replacement strategy under different network states and ultimately constructs a self-optimizing network architecture SW-WSN. Then, considering the network topology, link capacity, delay tolerance, and node energy of SW-WSN, the joint optimization problem of energy efficiency and QoS is formulated as an instance of linear programming (LP) with the objective of maximizing energy efficiency while ensuring QoS, and a heuristic algorithm is further designed to obtain the optimal solution. As shown by the simulation results, the Q-learning-based SW-WSN exhibits excellent learning capability and convergence stability across different network scales, successfully achieving a relatively ideal balance between energy efficiency and QoS. The Q-learning-based SW-WSN demonstrates substantial improvements in both energy efficiency (alive or dead devices, and network residual energy) and QoS (average transmission delay, data throughput, and bandwidth utilization) compared with the reinforcement learning (RL)-based routing, low-energy adaptive clustering hierarchy (LEACH), conventional small-world characteristics (SWC), multihop data transmission, and direct data transmission methods.
{"title":"Enhancing energy efficiency and QoS in Q-learning-based small-world WSNs","authors":"Yan Zhao, Lintao Huang, Mengzhe Ren, Hanyang Shi","doi":"10.1016/j.adhoc.2025.104123","DOIUrl":"10.1016/j.adhoc.2025.104123","url":null,"abstract":"<div><div>In a wireless sensor network (WSN), achieving efficient data transmission while extending network lifetime remains a critical issue. Sensor nodes may fail due to energy depletion during mass data transmission, which results in frequent changes in the network topology. The frequent topology changes not only increase the complexity of network management and maintenance, but also severely impair both energy efficiency and quality-of-service (QoS). To address these challenges, in this paper, we construct a small-world WSN (SW-WSN) and address its joint optimization problem of energy efficiency and QoS. In the constructed SW-WSN, long-range (LoRa) links are used to replace some of the conventional links between sensor nodes as well as between nodes and the gateway, and a small-world model is introduced to reduce the intermediate hop count for data transmission. A Q-learning-based adaptive link allocation algorithm is proposed to map all conventional and LoRa links to state space and action space respectively and learn to form state–action pairs through training, which determines an optimal link replacement strategy under different network states and ultimately constructs a self-optimizing network architecture SW-WSN. Then, considering the network topology, link capacity, delay tolerance, and node energy of SW-WSN, the joint optimization problem of energy efficiency and QoS is formulated as an instance of linear programming (LP) with the objective of maximizing energy efficiency while ensuring QoS, and a heuristic algorithm is further designed to obtain the optimal solution. As shown by the simulation results, the Q-learning-based SW-WSN exhibits excellent learning capability and convergence stability across different network scales, successfully achieving a relatively ideal balance between energy efficiency and QoS. The Q-learning-based SW-WSN demonstrates substantial improvements in both energy efficiency (alive or dead devices, and network residual energy) and QoS (average transmission delay, data throughput, and bandwidth utilization) compared with the reinforcement learning (RL)-based routing, low-energy adaptive clustering hierarchy (LEACH), conventional small-world characteristics (SWC), multihop data transmission, and direct data transmission methods.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104123"},"PeriodicalIF":4.8,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1016/j.adhoc.2025.104121
Haoran Cheng, Xiangyu Bai, Xiaoying Yang
In underwater acoustic sensor networks (UASNs), topology optimization strategies are effective ways to enhance network robustness, reduce transmission delay, and address issues such as high bit error rates and limited energy. However, current topology optimization strategies exhibit limitations in multifactorial consideration and dynamic adaptability, resulting in low energy efficiency and compromised robustness in UASNs. To address these issues, this paper proposes an adaptive scale-free topology optimization algorithm based on deep reinforcement learning (SFTG-DRL), aiming to ensure the network’s lifetime, reduce delay, and enhance the network’s fault tolerance. First, we optimize the transmission power of each sensor node to obtain its optimal power level, while proposing an improved preferential attachment model that incorporates node energy and depth information to achieve scale-free network characteristics. Then, deep reinforcement learning is applied to constrain minimum connections, further refining the topology for improved dynamic adaptability. Finally, extensive simulation experiments are conducted to validate the performance of the proposed algorithm, assessing aspects such as exploration strategy, node degree distribution, fault tolerance, network lifetime, and end-to-end delay.
{"title":"Adaptive scale-free topology optimization using deep reinforcement learning in UASNs","authors":"Haoran Cheng, Xiangyu Bai, Xiaoying Yang","doi":"10.1016/j.adhoc.2025.104121","DOIUrl":"10.1016/j.adhoc.2025.104121","url":null,"abstract":"<div><div>In underwater acoustic sensor networks (UASNs), topology optimization strategies are effective ways to enhance network robustness, reduce transmission delay, and address issues such as high bit error rates and limited energy. However, current topology optimization strategies exhibit limitations in multifactorial consideration and dynamic adaptability, resulting in low energy efficiency and compromised robustness in UASNs. To address these issues, this paper proposes an adaptive scale-free topology optimization algorithm based on deep reinforcement learning (SFTG-DRL), aiming to ensure the network’s lifetime, reduce delay, and enhance the network’s fault tolerance. First, we optimize the transmission power of each sensor node to obtain its optimal power level, while proposing an improved preferential attachment model that incorporates node energy and depth information to achieve scale-free network characteristics. Then, deep reinforcement learning is applied to constrain minimum connections, further refining the topology for improved dynamic adaptability. Finally, extensive simulation experiments are conducted to validate the performance of the proposed algorithm, assessing aspects such as exploration strategy, node degree distribution, fault tolerance, network lifetime, and end-to-end delay.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104121"},"PeriodicalIF":4.8,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1016/j.adhoc.2025.104094
Xuan Li , Wen Jiang , Wanting Wang , Tianqing Zhou , Kan Wang , Shuai Liu
In cognitive vehicular networks (CVNs), cognitive vehicles are permitted to opportunistically utilize idle spectrum bands. However, the reclaiming of channels by licensed users may result in significant interference or even network disconnection, failing to meet the reliable data transmission requirements in CVNs. Vehicles need to frequently exchange a large amount of information to cope with unpredictable topology changes and channel reuse scenarios, resulting in significant communication and computational overhead, which conflicts with the low-latency requirements of vehicular networks. To address this, we introduce digital twin (DT) technology into CVNs, enabling cognitive vehicles to effectively avoid transmission interruption caused by primary user channel occupancy. First, we propose a DT-assisted connectivity algorithm (DT-CA) that maps real-world vehicular networks to their digital replicas, enabling interaction in the virtual world. DT-CA assists vehicles in forming specific clusters to ensure channel connectivity. Subsequently, we propose a vehicle-to-vehicle (V2V) connectivity algorithm that quantifies vehicle mobility using communication probabilities and dynamically optimizes cluster structures. Finally, we conduct extensive simulation studies in different traffic scenarios, such as T-junctions and crossroads, which demonstrate that the DT-assisted algorithms have significant advantages in enhancing the connectivity and cluster stability of CVNs, while also exhibiting dynamic adaptability and low complexity.
{"title":"Joint channel connectivity and interference management in DT-assisted cognitive vehicular networks","authors":"Xuan Li , Wen Jiang , Wanting Wang , Tianqing Zhou , Kan Wang , Shuai Liu","doi":"10.1016/j.adhoc.2025.104094","DOIUrl":"10.1016/j.adhoc.2025.104094","url":null,"abstract":"<div><div>In cognitive vehicular networks (CVNs), cognitive vehicles are permitted to opportunistically utilize idle spectrum bands. However, the reclaiming of channels by licensed users may result in significant interference or even network disconnection, failing to meet the reliable data transmission requirements in CVNs. Vehicles need to frequently exchange a large amount of information to cope with unpredictable topology changes and channel reuse scenarios, resulting in significant communication and computational overhead, which conflicts with the low-latency requirements of vehicular networks. To address this, we introduce digital twin (DT) technology into CVNs, enabling cognitive vehicles to effectively avoid transmission interruption caused by primary user channel occupancy. First, we propose a DT-assisted connectivity algorithm (DT-CA) that maps real-world vehicular networks to their digital replicas, enabling interaction in the virtual world. DT-CA assists vehicles in forming specific clusters to ensure channel connectivity. Subsequently, we propose a vehicle-to-vehicle (V2V) connectivity algorithm that quantifies vehicle mobility using communication probabilities and dynamically optimizes cluster structures. Finally, we conduct extensive simulation studies in different traffic scenarios, such as T-junctions and crossroads, which demonstrate that the DT-assisted algorithms have significant advantages in enhancing the connectivity and cluster stability of CVNs, while also exhibiting dynamic adaptability and low complexity.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104094"},"PeriodicalIF":4.8,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-13DOI: 10.1016/j.adhoc.2025.104122
Zhengze Liu , Nianmin Yao , Shengyuan Bai , Tengyi Mai
As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold of across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.
{"title":"PriV2I: Privacy-preserving V2I authentication protocol with fine-grained access control","authors":"Zhengze Liu , Nianmin Yao , Shengyuan Bai , Tengyi Mai","doi":"10.1016/j.adhoc.2025.104122","DOIUrl":"10.1016/j.adhoc.2025.104122","url":null,"abstract":"<div><div>As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold <span><math><mi>t</mi></math></span> of <span><math><mi>n</mi></math></span> across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104122"},"PeriodicalIF":4.8,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-06DOI: 10.1016/j.adhoc.2025.104118
John Breeden , Mohammad S. Khan , Simone Silvestri , Biju Bajracharya , Chandler Scott
Network slicing in the fifth generation (5G) cellular architecture provides fundamental support for the key performance required by users. To create the infrastructure of network slicing, enablers such as software-defined networking and network function virtualization facilitate a separation between the physical distributed infrastructure and the controls and functions that provide the services and that construct the individual, isolated network slices. In the modern framework of 5G, a single service provider no longer needs to maintain the physical infrastructure and the enabling functions. Third-party vendors and suppliers lease out their facilities and services to the 5G framework of the primary service provider. The 5G architecture derives from multiple services, distributed over great distances, and managed by multiple parties. Each of these enablers and providers adds vulnerabilities to the architecture and to network slicing specifically. In this work, we will examine the interweaving of the technological enablers and external infrastructure providers, identify vulnerabilities created by this complexity, and discuss the potential mitigation for these vulnerabilities. In order to rigorously evaluate vulnerabilities and mitigation, analysis requires a clearly-defined perspective. Network defenders often follow a perspective of defense-in-depth which concentrates on covering all possible vulnerabilities from multiple threat vectors. In this discussion, we will apply the concepts of attack-in-depth and the cyber kill chain from a threat actor’s perspective as contrasted to the cybersecurity concept of defense-in-depth. Finally, we will examine a case study that demonstrates how a series of vulnerabilities cascade into an advanced attack that infiltrates and exploits users’ network slices.
{"title":"Mitigating slice vulnerabilities in 5G core networks: A comprehensive survey","authors":"John Breeden , Mohammad S. Khan , Simone Silvestri , Biju Bajracharya , Chandler Scott","doi":"10.1016/j.adhoc.2025.104118","DOIUrl":"10.1016/j.adhoc.2025.104118","url":null,"abstract":"<div><div>Network slicing in the fifth generation (5G) cellular architecture provides fundamental support for the key performance required by users. To create the infrastructure of network slicing, enablers such as software-defined networking and network function virtualization facilitate a separation between the physical distributed infrastructure and the controls and functions that provide the services and that construct the individual, isolated network slices. In the modern framework of 5G, a single service provider no longer needs to maintain the physical infrastructure and the enabling functions. Third-party vendors and suppliers lease out their facilities and services to the 5G framework of the primary service provider. The 5G architecture derives from multiple services, distributed over great distances, and managed by multiple parties. Each of these enablers and providers adds vulnerabilities to the architecture and to network slicing specifically. In this work, we will examine the interweaving of the technological enablers and external infrastructure providers, identify vulnerabilities created by this complexity, and discuss the potential mitigation for these vulnerabilities. In order to rigorously evaluate vulnerabilities and mitigation, analysis requires a clearly-defined perspective. Network defenders often follow a perspective of defense-in-depth which concentrates on covering all possible vulnerabilities from multiple threat vectors. In this discussion, we will apply the concepts of attack-in-depth and the cyber kill chain from a threat actor’s perspective as contrasted to the cybersecurity concept of defense-in-depth. Finally, we will examine a case study that demonstrates how a series of vulnerabilities cascade into an advanced attack that infiltrates and exploits users’ network slices.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104118"},"PeriodicalIF":4.8,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-05DOI: 10.1016/j.adhoc.2025.104120
Shaolin Qiu , Shuchao Deng , Honglei Pang
Vehicle Ad Hoc Network (VANET) is becoming an essential vehicle communication network paradigm. However, the safe and reliable operation of VANETs faces serious challenges, especially in how users authenticate to the network. Idealized access control rules for trusted network entities are highly susceptible to the leakage of critical private data from inbound users. In addition, the open network environment puts the security of networked entities at significant risk, and the presence of Byzantine devices severely hampers legitimate vehicles from requesting network access services. To address the above problems, this paper proposes an efficient vehicle cross-domain identity batch authentication framework that is functional, privacy-preserving, and resistant to Byzantine attacks. The scheme uses non-interactive zero-knowledge proofs to achieve anonymous vehicle authentication. We integrate an improved Practical Byzantine Fault Tolerance (PBFT) protocol to enhance the robustness of zero-knowledge authentication. It achieves efficient consensus for inbound vehicle authentication in cross-domain scenarios, even in the presence of a small number of Byzantine entities. The scheme is further integrated into cross-domain authentication frameworks to support seamless inter-domain access. Under dynamic VANET topologies, we derive consensus probability and authentication accuracy; simulations confirm high accuracy with low communication overhead.
{"title":"Towards byzantine fault-tolerant non-interactive zero knowledge cross-domain batch authentication in VANET","authors":"Shaolin Qiu , Shuchao Deng , Honglei Pang","doi":"10.1016/j.adhoc.2025.104120","DOIUrl":"10.1016/j.adhoc.2025.104120","url":null,"abstract":"<div><div>Vehicle Ad Hoc Network (VANET) is becoming an essential vehicle communication network paradigm. However, the safe and reliable operation of VANETs faces serious challenges, especially in how users authenticate to the network. Idealized access control rules for trusted network entities are highly susceptible to the leakage of critical private data from inbound users. In addition, the open network environment puts the security of networked entities at significant risk, and the presence of Byzantine devices severely hampers legitimate vehicles from requesting network access services. To address the above problems, this paper proposes an efficient vehicle cross-domain identity batch authentication framework that is functional, privacy-preserving, and resistant to Byzantine attacks. The scheme uses non-interactive zero-knowledge proofs to achieve anonymous vehicle authentication. We integrate an improved Practical Byzantine Fault Tolerance (PBFT) protocol to enhance the robustness of zero-knowledge authentication. It achieves efficient consensus for inbound vehicle authentication in cross-domain scenarios, even in the presence of a small number of Byzantine entities. The scheme is further integrated into cross-domain authentication frameworks to support seamless inter-domain access. Under dynamic VANET topologies, we derive consensus probability and authentication accuracy; simulations confirm high accuracy with low communication overhead.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104120"},"PeriodicalIF":4.8,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-04DOI: 10.1016/j.adhoc.2025.104109
Yuan Tao , Taochun Wang , Fulong Chen , Biao Jie , Junmei Cai , Dong Xie
With the development of mobile computing and the Internet of Things (IoT), Sparse mobile crowd sensing (SMCS) has emerged as a new data collection paradigm, demonstrating significant application potential in fields. However, due to budget constraints and area inaccessibility, maximizing the quality of inferred data with limited sensing users and resources has become an important research challenge. This paper proposes a fix-based data inference method (FS-MCS) based on deep reinforcement learning, which aims to reduce error accumulation and improve the accuracy of data inference by optimizing the cell selection strategy. Specifically, FS-MCS uses Kriging interpolation to compute the weights of spatiotemporal data and combines Deep Q Learning to dynamically select sensing cells. This allows for the collection of the most informative data in each time series, fixing the inference model and inferring the remaining data. By considering the data correlation over different time periods and budget constraints, FS-MCS can maximize data quality and minimize the impact of outliers on inference results, while ensuring the budget is adhered to. The results show that the proposed method performs well in terms of data quality, convergence speed, and budget utilization, especially when dealing with complex spatiotemporal data and dynamic environmental changes, where it demonstrates significant advantages.
{"title":"FS-MCS: A reinforcement learning-based data inference scheme for sparse mobile crowd sensing","authors":"Yuan Tao , Taochun Wang , Fulong Chen , Biao Jie , Junmei Cai , Dong Xie","doi":"10.1016/j.adhoc.2025.104109","DOIUrl":"10.1016/j.adhoc.2025.104109","url":null,"abstract":"<div><div>With the development of mobile computing and the Internet of Things (IoT), Sparse mobile crowd sensing (SMCS) has emerged as a new data collection paradigm, demonstrating significant application potential in fields. However, due to budget constraints and area inaccessibility, maximizing the quality of inferred data with limited sensing users and resources has become an important research challenge. This paper proposes a fix-based data inference method (FS-MCS) based on deep reinforcement learning, which aims to reduce error accumulation and improve the accuracy of data inference by optimizing the cell selection strategy. Specifically, FS-MCS uses Kriging interpolation to compute the weights of spatiotemporal data and combines Deep Q Learning to dynamically select sensing cells. This allows for the collection of the most informative data in each time series, fixing the inference model and inferring the remaining data. By considering the data correlation over different time periods and budget constraints, FS-MCS can maximize data quality and minimize the impact of outliers on inference results, while ensuring the budget is adhered to. The results show that the proposed method performs well in terms of data quality, convergence speed, and budget utilization, especially when dealing with complex spatiotemporal data and dynamic environmental changes, where it demonstrates significant advantages.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104109"},"PeriodicalIF":4.8,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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