Pub Date : 2026-01-20DOI: 10.1016/j.comnet.2026.112033
Vincent Omollo Nyangaresi , Mohd Shariq , Daisy Nyang’anyi Ondwari , Muhammad Shafiq , Khalid Alsubhi , Mehedi Masud
Smart home networks deploy a myriad of sensors and intelligent devices to collect and disseminate massive and sensitive data, facilitating task automation for enhancing comfort, quality of life, efficiency, and sustainability. However, the utilization of public channels for interactions between users and smart home devices raises serious privacy and security issues. Numerous authentication schemes have been proposed in recent literature; most of them are prone to security attacks, including offline guessing, privileged insiders, and impersonation. In addition, some of them have complicated architectures that result in high resource consumption. In this paper, efficient Chebyshev polynomials and hashing functions are leveraged to develop a robust authentication protocol for smart homes. The Burrows–Abadi–Needham (BAN) logic-based detailed formal security analysis confirms the robustness of the joint authentication and key negotiation procedures. In addition, informal security analysis shows that the proposed protocol is secure against the Dolev-Yao (D-Y) and Canetti and Krawczyk (C-K) adversary models, mitigating several known security attacks. In terms of performance, the developed scheme incurs relatively low computation, energy, and communication costs.
{"title":"Efficient smart home message verification protocol based on Chebyshev chaotic mapping","authors":"Vincent Omollo Nyangaresi , Mohd Shariq , Daisy Nyang’anyi Ondwari , Muhammad Shafiq , Khalid Alsubhi , Mehedi Masud","doi":"10.1016/j.comnet.2026.112033","DOIUrl":"10.1016/j.comnet.2026.112033","url":null,"abstract":"<div><div>Smart home networks deploy a myriad of sensors and intelligent devices to collect and disseminate massive and sensitive data, facilitating task automation for enhancing comfort, quality of life, efficiency, and sustainability. However, the utilization of public channels for interactions between users and smart home devices raises serious privacy and security issues. Numerous authentication schemes have been proposed in recent literature; most of them are prone to security attacks, including offline guessing, privileged insiders, and impersonation. In addition, some of them have complicated architectures that result in high resource consumption. In this paper, efficient Chebyshev polynomials and hashing functions are leveraged to develop a robust authentication protocol for smart homes. The Burrows–Abadi–Needham (BAN) logic-based detailed formal security analysis confirms the robustness of the joint authentication and key negotiation procedures. In addition, informal security analysis shows that the proposed protocol is secure against the Dolev-Yao (D-Y) and Canetti and Krawczyk (C-K) adversary models, mitigating several known security attacks. In terms of performance, the developed scheme incurs relatively low computation, energy, and communication costs.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112033"},"PeriodicalIF":4.6,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-17DOI: 10.1016/j.comnet.2026.112029
Nikos Filinis , Ioannis Dimolitsas , Dimitrios Spatharakis , Paolo Bono , Anastasios Zafeiropoulos , Cristina Emilia Costa , Roberto Bruschi , Symeon Papavassiliou
The rapid advancements in technologies across the Computing Continuum have reinforced the need for the interplay of various network and compute orchestration mechanisms within distributed infrastructure architectures to support the hyper-distributed application (HDA) deployments. A unified approach to managing heterogeneous components is crucial for reconciling conflicting objectives and creating a synergetic framework. To undertake these challenges, we present NEPHELE, a platform that realizes a hierarchical multi-layered orchestration architecture that incorporates infrastructure and application orchestration workflows across diverse resource management layers. The proposed platform integrates well-defined components spanning network and multi-cluster compute domains to enable intent-driven, dynamic orchestration. At its core, the Synergetic Meta-Orchestrator (SMO) integrates diverse application requirements, generating deployment plans by interfacing with underlying orchestrators over distributed compute and network infrastructure. In the current work, we present the NEPHELE architecture, enumerate its interaction workflows, and evaluate key components of the overall architecture based on the instantiation and usage of the NEPHELE platform. The platform is evaluated in a multi-domain infrastructure setup to assess the operational overhead of the introduced orchestration functionality, considering also the assessment of different topology configurations on resource instantiation times and allocation dynamics, and network latency. Finally, we demonstrate the platform’s effectiveness in orchestrating distributed application graphs under varying placement intents, performance constraints, and workload stress conditions. The evaluation results outline the effectiveness of NEPHELE in orchestrating various infrastructure layers and application lifecycle scenarios through a unified interface.
{"title":"A platform perspective for the computing continuum: Synergetic orchestration of compute and network resources for hyper-distributed applications","authors":"Nikos Filinis , Ioannis Dimolitsas , Dimitrios Spatharakis , Paolo Bono , Anastasios Zafeiropoulos , Cristina Emilia Costa , Roberto Bruschi , Symeon Papavassiliou","doi":"10.1016/j.comnet.2026.112029","DOIUrl":"10.1016/j.comnet.2026.112029","url":null,"abstract":"<div><div>The rapid advancements in technologies across the Computing Continuum have reinforced the need for the interplay of various network and compute orchestration mechanisms within distributed infrastructure architectures to support the hyper-distributed application (HDA) deployments. A unified approach to managing heterogeneous components is crucial for reconciling conflicting objectives and creating a synergetic framework. To undertake these challenges, we present NEPHELE, a platform that realizes a hierarchical multi-layered orchestration architecture that incorporates infrastructure and application orchestration workflows across diverse resource management layers. The proposed platform integrates well-defined components spanning network and multi-cluster compute domains to enable intent-driven, dynamic orchestration. At its core, the Synergetic Meta-Orchestrator (SMO) integrates diverse application requirements, generating deployment plans by interfacing with underlying orchestrators over distributed compute and network infrastructure. In the current work, we present the NEPHELE architecture, enumerate its interaction workflows, and evaluate key components of the overall architecture based on the instantiation and usage of the NEPHELE platform. The platform is evaluated in a multi-domain infrastructure setup to assess the operational overhead of the introduced orchestration functionality, considering also the assessment of different topology configurations on resource instantiation times and allocation dynamics, and network latency. Finally, we demonstrate the platform’s effectiveness in orchestrating distributed application graphs under varying placement intents, performance constraints, and workload stress conditions. The evaluation results outline the effectiveness of NEPHELE in orchestrating various infrastructure layers and application lifecycle scenarios through a unified interface.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112029"},"PeriodicalIF":4.6,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-16DOI: 10.1016/j.comnet.2026.112022
Yiyang Huang , Mingxin Cui , Gaopeng Gou , Chang Liu , Yong Wang , Bing Xia , Guoming Ren , Zheyuan Gu , Xiyuan Zhang , Gang Xiong
Dynamic IP technologies such as IP address pool rotation by Internet operators and elastic IP drift by cloud service providers are widely adopted, breaking the static binding between IP addresses and geographical locations and posing severe challenges to the accuracy, efficiency, and robustness of IP cross-regional detection. Traditional solutions rely on third-party IP geolocation databases, whose large-scale batch update mode fails to synchronize IP regional attribution in a timely manner, struggling to adapt to dynamic IP changes. This results in insufficient detection accuracy and efficiency, compromising the stability of geographically related network services. To address this issue, this paper proposes TrafficCL, a traffic feature-based IP cross-regional detection method: it constructs a geographically associated traffic feature set, aligns traffic embedding distance with geographical distance via contrastive learning to enhance geographical attributes, integrates data augmentation to improve model robustness, designs a lightweight binary classification task for regional deviation detection, and adopts a targeted update strategy to avoid large-scale update latency. Experimental results show that TrafficCL significantly outperforms the active probing method PoP: on the Beijing cross-district dataset, the accuracy increases from 0.781 to 0.982, the F1-score improves by 2.2 times, and the processing efficiency for ten-thousand-level samples is enhanced by 23.6 times. When facing 10 % data loss, 10 % network feature fluctuation, and a positional offset of approximately 500 m, the F1-score degradation is less than 3 % in all cases, demonstrating excellent robustness. This method effectively improves the accuracy, efficiency, and robustness of IP cross-regional detection, and has practical significance for ensuring the stability of geographically related network services.
{"title":"TrafficCL: Contrastive learning on network traffic for accurate, efficient and robust IP cross-regional detection","authors":"Yiyang Huang , Mingxin Cui , Gaopeng Gou , Chang Liu , Yong Wang , Bing Xia , Guoming Ren , Zheyuan Gu , Xiyuan Zhang , Gang Xiong","doi":"10.1016/j.comnet.2026.112022","DOIUrl":"10.1016/j.comnet.2026.112022","url":null,"abstract":"<div><div>Dynamic IP technologies such as IP address pool rotation by Internet operators and elastic IP drift by cloud service providers are widely adopted, breaking the static binding between IP addresses and geographical locations and posing severe challenges to the accuracy, efficiency, and robustness of IP cross-regional detection. Traditional solutions rely on third-party IP geolocation databases, whose large-scale batch update mode fails to synchronize IP regional attribution in a timely manner, struggling to adapt to dynamic IP changes. This results in insufficient detection accuracy and efficiency, compromising the stability of geographically related network services. To address this issue, this paper proposes TrafficCL, a traffic feature-based IP cross-regional detection method: it constructs a geographically associated traffic feature set, aligns traffic embedding distance with geographical distance via contrastive learning to enhance geographical attributes, integrates data augmentation to improve model robustness, designs a lightweight binary classification task for regional deviation detection, and adopts a targeted update strategy to avoid large-scale update latency. Experimental results show that TrafficCL significantly outperforms the active probing method PoP: on the Beijing cross-district dataset, the accuracy increases from 0.781 to 0.982, the F1-score improves by 2.2 times, and the processing efficiency for ten-thousand-level samples is enhanced by 23.6 times. When facing 10 % data loss, 10 % network feature fluctuation, and a positional offset of approximately 500 m, the F1-score degradation is less than 3 % in all cases, demonstrating excellent robustness. This method effectively improves the accuracy, efficiency, and robustness of IP cross-regional detection, and has practical significance for ensuring the stability of geographically related network services.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112022"},"PeriodicalIF":4.6,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-16DOI: 10.1016/j.comnet.2026.112018
Yingjie Cai , Tianbo Lu , Jiaze Shang , Yanfang Li , Qitai Gong , Hanrui Chen
Authentication key agreement (AKA) protocol is an effective method for achieving secure communication between Internet of Things (IoT) devices. However, existing public key infrastructure-based and identity-based AKA protocols face limitations due to complex certificate management and key escrow issues. Furthermore, cross-domain communication is a fundamental requirement for IoT. However, current solutions addressing this challenge rely on trusted third parties, which undoubtedly increases the communication overhead and system complexity during the authentication phase. To address these challenges, we propose a new provably secure lightweight certificateless cross-domain authentication key agreement protocol (LCC-AKA). By introducing a certificateless public key cryptographic mechanism during the registration phase, we eliminate the need for complex certificate management and the limitations of key escrow, while also preventing insider attacks even under the semi-honest Key Generation Center (KGC) assumption. In the cross-domain authentication key agreement phase, we present a mechanism that enables direct cross-domain authentication and key agreement between devices without relying on trusted third parties, utilizing lightweight elliptic curve and hash function operations to achieve efficiency. In terms of security, we analyze the security vulnerabilities of existing certificateless cross-domain AKA schemes and extend the Real-Or-Random (ROR) model. The LCC-AKA protocol is provably secure under the extended ROR model and BAN logic. Security and performance analyses demonstrate that the LCC-AKA protocol can resist both insider and outsider attacks, including public key replacement attacks, while maintaining low computational and communication overhead.
{"title":"LCC-AKA: Lightweight certificateless cross-domain authentication key agreement protocol for IoT devices","authors":"Yingjie Cai , Tianbo Lu , Jiaze Shang , Yanfang Li , Qitai Gong , Hanrui Chen","doi":"10.1016/j.comnet.2026.112018","DOIUrl":"10.1016/j.comnet.2026.112018","url":null,"abstract":"<div><div>Authentication key agreement (AKA) protocol is an effective method for achieving secure communication between Internet of Things (IoT) devices. However, existing public key infrastructure-based and identity-based AKA protocols face limitations due to complex certificate management and key escrow issues. Furthermore, cross-domain communication is a fundamental requirement for IoT. However, current solutions addressing this challenge rely on trusted third parties, which undoubtedly increases the communication overhead and system complexity during the authentication phase. To address these challenges, we propose a new provably secure lightweight certificateless cross-domain authentication key agreement protocol (LCC-AKA). By introducing a certificateless public key cryptographic mechanism during the registration phase, we eliminate the need for complex certificate management and the limitations of key escrow, while also preventing insider attacks even under the semi-honest Key Generation Center (KGC) assumption. In the cross-domain authentication key agreement phase, we present a mechanism that enables direct cross-domain authentication and key agreement between devices without relying on trusted third parties, utilizing lightweight elliptic curve and hash function operations to achieve efficiency. In terms of security, we analyze the security vulnerabilities of existing certificateless cross-domain AKA schemes and extend the Real-Or-Random (ROR) model. The LCC-AKA protocol is provably secure under the extended ROR model and BAN logic. Security and performance analyses demonstrate that the LCC-AKA protocol can resist both insider and outsider attacks, including public key replacement attacks, while maintaining low computational and communication overhead.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112018"},"PeriodicalIF":4.6,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-16DOI: 10.1016/j.comnet.2026.112028
Junfeng Tian, Yuyang Zhao, Jin Tian
Sharding technology significantly enhances blockchain performance by dividing nodes into multiple groups to propose blocks in parallel. However, cross-shard transactions require timely communication between shards to ensure information consistency, which has a great impact on the performance of sharded blockchains. To address this challenge, we propose ControlShard, which builds shards specifically for cross-shard transactions and allows users to have different account status in different shards, further reducing communication overhead. ControlShard allows multiple blocks to be committed in a single consensus round and employs a congestion control strategy to determine the maximum number of blocks suitable for the current network conditions. Additionally, ControlShard employs a pipelining strategy to further enhance parallel processing capabilities. We implemented a system prototype and evaluated its performance using real Ethereum transactions. Under the experimental conditions specified in this paper, ControlShard outperforms existing advanced solutions in terms of throughput and transaction confirmation latency, achieving a throughput up to 2.2 times that of BrokerChain and 4.1 times that of Monoxide.
{"title":"ControlShard: A cross-shard transaction processing method based on congestion control strategy","authors":"Junfeng Tian, Yuyang Zhao, Jin Tian","doi":"10.1016/j.comnet.2026.112028","DOIUrl":"10.1016/j.comnet.2026.112028","url":null,"abstract":"<div><div>Sharding technology significantly enhances blockchain performance by dividing nodes into multiple groups to propose blocks in parallel. However, cross-shard transactions require timely communication between shards to ensure information consistency, which has a great impact on the performance of sharded blockchains. To address this challenge, we propose ControlShard, which builds shards specifically for cross-shard transactions and allows users to have different account status in different shards, further reducing communication overhead. ControlShard allows multiple blocks to be committed in a single consensus round and employs a congestion control strategy to determine the maximum number of blocks suitable for the current network conditions. Additionally, ControlShard employs a pipelining strategy to further enhance parallel processing capabilities. We implemented a system prototype and evaluated its performance using real Ethereum transactions. Under the experimental conditions specified in this paper, ControlShard outperforms existing advanced solutions in terms of throughput and transaction confirmation latency, achieving a throughput up to 2.2 times that of BrokerChain and 4.1 times that of Monoxide.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112028"},"PeriodicalIF":4.6,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wireless Sensor Networks (WSNs) are essential in modern applications like environmental monitoring, industrial automation, and security. However, they suffer from pivotal issues of internal threats, congestion, and power constraint, crippling their efficiency and reliability. These threats include data manipulation and congestion, which degrade network performance. Addressing these challenges is crucial to ensure WSNs can maintain reliable operation while effectively managing energy resources and combating malicious activities. Thus, to address these limitations, this paper introduces CATER (Congestion Aware Trust-Enabled Routing), a new trust-based congestion-aware energy-constrained routing protocol for WSNs. CATER's primary contributions are: (1) an end-to-end multi-dimension trust evaluation model consisting of direct trust, indirect trust, energy trust, and an expected positive probability trust model for robust trust estimation; (2) an adaptive congestion control model for dynamically adjusting transmission rates according to real-time network status using the buffer capacity and queue length as variables; and (3) a trust-enabled routing module performing secure and efficient data transmission employing trusted sensor node selection. After trust evaluation, the proposed CATER protocol detects the congestion in the WSN. The congestion is detected using buffer capacity and queue length at a sensor node. Once congestion is detected at a node, the congested node sends a feedback message to the neighbor (source) node to optimize the transmission rate. The proposed CATER protocol always selects energy-efficient and trustworthy next-hop to transfer available data packets toward the sink. MATLAB simulations readily verify that CATER improves energy efficiency by 15.82%, packet drop ratio by 46.95%, and decreases end-to-end delay by 31.81% compared to other state-of-the-art existing routing protocols like CHicDra and SEFR. CATER also improves network throughput and packet delivery ratio (PDR) even in over-loaded and adversarial environments up to 50% adversarial nodes. The findings conclusively demonstrate CATER to be a very effective tool for providing efficient, reliable, and congestion-aware routing for WSNs with profound implications for real-world industrial and IoT applications.
{"title":"CATER: Congestion aware trust-enabled routing for energy-constrained wireless sensor networks","authors":"Mahendra Kumar Jangir , Karan Singh , Tayyab Khan , Ali Ahmadian","doi":"10.1016/j.comnet.2026.112030","DOIUrl":"10.1016/j.comnet.2026.112030","url":null,"abstract":"<div><div>Wireless Sensor Networks (WSNs) are essential in modern applications like environmental monitoring, industrial automation, and security. However, they suffer from pivotal issues of internal threats, congestion, and power constraint, crippling their efficiency and reliability. These threats include data manipulation and congestion, which degrade network performance. Addressing these challenges is crucial to ensure WSNs can maintain reliable operation while effectively managing energy resources and combating malicious activities. Thus, to address these limitations, this paper introduces CATER (Congestion Aware Trust-Enabled Routing), a new trust-based congestion-aware energy-constrained routing protocol for WSNs. CATER's primary contributions are: (1) an end-to-end multi-dimension trust evaluation model consisting of direct trust, indirect trust, energy trust, and an expected positive probability trust model for robust trust estimation; (2) an adaptive congestion control model for dynamically adjusting transmission rates according to real-time network status using the buffer capacity and queue length as variables; and (3) a trust-enabled routing module performing secure and efficient data transmission employing trusted sensor node selection. After trust evaluation, the proposed CATER protocol detects the congestion in the WSN. The congestion is detected using buffer capacity and queue length at a sensor node. Once congestion is detected at a node, the congested node sends a feedback message to the neighbor (source) node to optimize the transmission rate. The proposed CATER protocol always selects energy-efficient and trustworthy next-hop to transfer available data packets toward the sink. MATLAB simulations readily verify that CATER improves energy efficiency by 15.82%, packet drop ratio by 46.95%, and decreases end-to-end delay by 31.81% compared to other state-of-the-art existing routing protocols like CHicDra and SEFR. CATER also improves network throughput and packet delivery ratio (PDR) even in over-loaded and adversarial environments up to 50% adversarial nodes. The findings conclusively demonstrate CATER to be a very effective tool for providing efficient, reliable, and congestion-aware routing for WSNs with profound implications for real-world industrial and IoT applications.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112030"},"PeriodicalIF":4.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-15DOI: 10.1016/j.comnet.2026.112027
Fahui Wu , Shi Peng , Wei Wang , Jiangling Cao , Dingcheng Yang , Sihan Fu , Xiaoli Ye
The burgeoning development of low-altitude economy networks (LAEN) relies heavily on the efficient and sustainable operation of cellular-connected unmanned aerial vehicles (UAVs) for critical services, particularly in infrastructure inspection. While UAV-aided inspection offers significant advantages over traditional inspection methods, the challenge of minimizing total system energy consumption remains paramount, constrained by both limited onboard battery capacity and highly dynamic low-altitude wireless networks (LAWN). To address this issue, a cellular-connected UAV-aided inspection system is investigated, and a dual radio map-driven hierarchical optimization algorithm is proposed to enhance the system´s energy efficiency. Initially, urban map data and relevant transmitter information are utilized to construct a dual radio map, which comprises the communication outage probability (COP) map and the reference signal received power (RSRP) map. Subsequently, leveraging this a priori channel knowledge, the A star (A*) algorithm is employed to optimize the UAV’s trajectory between any two inspection points, thereby stabilizing the communication link. Finally, the optimized trajectory information is fed into the particle swarm optimization (PSO) algorithm to determine the optimal task assignment strategy for the energy-constrained UAV, achieving the overall energy minimization objective. The numerical results validate that our proposed algorithm achieves lower energy consumption compared to benchmark schemes, while simultaneously guaranteeing the required communication quality for the low-altitude UAV.
{"title":"Energy minimization for cellular-connected UAV-aided inspection systems: A dual radio map-driven hierarchical optimization algorithm","authors":"Fahui Wu , Shi Peng , Wei Wang , Jiangling Cao , Dingcheng Yang , Sihan Fu , Xiaoli Ye","doi":"10.1016/j.comnet.2026.112027","DOIUrl":"10.1016/j.comnet.2026.112027","url":null,"abstract":"<div><div>The burgeoning development of low-altitude economy networks (LAEN) relies heavily on the efficient and sustainable operation of cellular-connected unmanned aerial vehicles (UAVs) for critical services, particularly in infrastructure inspection. While UAV-aided inspection offers significant advantages over traditional inspection methods, the challenge of minimizing total system energy consumption remains paramount, constrained by both limited onboard battery capacity and highly dynamic low-altitude wireless networks (LAWN). To address this issue, a cellular-connected UAV-aided inspection system is investigated, and a dual radio map-driven hierarchical optimization algorithm is proposed to enhance the system´s energy efficiency. Initially, urban map data and relevant transmitter information are utilized to construct a dual radio map, which comprises the communication outage probability (COP) map and the reference signal received power (RSRP) map. Subsequently, leveraging this a priori channel knowledge, the A star (A*) algorithm is employed to optimize the UAV’s trajectory between any two inspection points, thereby stabilizing the communication link. Finally, the optimized trajectory information is fed into the particle swarm optimization (PSO) algorithm to determine the optimal task assignment strategy for the energy-constrained UAV, achieving the overall energy minimization objective. The numerical results validate that our proposed algorithm achieves lower energy consumption compared to benchmark schemes, while simultaneously guaranteeing the required communication quality for the low-altitude UAV.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112027"},"PeriodicalIF":4.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-15DOI: 10.1016/j.comnet.2026.112015
Yang Xu , Zuan Xie , Hongli Xu , Yunming Liao , Zhiwei Yao
In order to avoid the possible bottleneck of conventional parameter server architecture, the decentralized federated learning (DFL) is developed on the peer-to-peer (P2P) communication. However, due to the intrinsic features of edge computing (EC), such as limited and heterogeneous computing capability, constrained bandwidth and non-IID data, DFL with a unified model structure and fixed communication topology results in a slow convergence rate. To address these challenges, we adopt the vertical model parallelism and propose an efficient model training framework called SWIFT. Specifically, we divide the complete model into multiple non-overlapping columns, and allow each client to train a sub-model with different length of column group according to its computing capacity. We theoretically analyze the convergence upper bound of SWIFT, and reveal that the convergence rate will be impacted by the consensus distance among different clients. To reduce the consensus distance and accelerate training, we propose a dynamic topology construction algorithm by selecting appropriate neighbors for each client before model aggregation. Experimental results demonstrate that, compared with the baseline methods, SWIFT can approximately achieve a 2.3 × speedup and save 46% communication traffic.
{"title":"Decentralized federated learning with vertical model parallelism and topology construction","authors":"Yang Xu , Zuan Xie , Hongli Xu , Yunming Liao , Zhiwei Yao","doi":"10.1016/j.comnet.2026.112015","DOIUrl":"10.1016/j.comnet.2026.112015","url":null,"abstract":"<div><div>In order to avoid the possible bottleneck of conventional parameter server architecture, the decentralized federated learning (DFL) is developed on the peer-to-peer (P2P) communication. However, due to the intrinsic features of edge computing (EC), such as limited and heterogeneous computing capability, constrained bandwidth and non-IID data, DFL with a unified model structure and fixed communication topology results in a slow convergence rate. To address these challenges, we adopt the vertical model parallelism and propose an efficient model training framework called SWIFT. Specifically, we divide the complete model into multiple non-overlapping columns, and allow each client to train a sub-model with different length of column group according to its computing capacity. We theoretically analyze the convergence upper bound of SWIFT, and reveal that the convergence rate will be impacted by the consensus distance among different clients. To reduce the consensus distance and accelerate training, we propose a dynamic topology construction algorithm by selecting appropriate neighbors for each client before model aggregation. Experimental results demonstrate that, compared with the baseline methods, SWIFT can approximately achieve a <strong>2.3 × </strong> speedup and save <strong>46%</strong> communication traffic.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112015"},"PeriodicalIF":4.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-15DOI: 10.1016/j.comnet.2026.112026
Zhenqi Wang , Peng Yang , Angze Du , Dongmei Yang , Xiaoyan Liu
Vehicular Ad Hoc Networks (VANETs) are critical for enhancing road safety and optimizing traffic flow. Attribute-based searchable encryption (ABSE) with blockchain supports one-to-many encryption, ciphertext search, and decentralized storage, providing a solution to enable data sharing in VANETs. However, existing VANETs schemes face many limitations: blockchain miner nodes are at risk of stealing vehicle attribute keys during key verification; the lack of permission control may also lead to unauthorized downloads; and multi-keyword search, encryption, and decryption require a lot of computing power. To address these issues, we propose an attribute-based searchable encryption scheme for secure data sharing with blockchain (ABSE-VANETs). Specifically, we introduce multiple authorization authorities to manage attributes and generate obfuscated key parameters via bilinear pairing to prevent the leakage of vehicle attribute keys. We combine linear integer secret sharing (LISS) to bind the ciphertext download permission and the vehicle attribute set to prevent unauthorized ciphertext download. Moreover, we optimize the multi-keyword index generation algorithm and trapdoor structure and construct a multi-keyword index with the help of blockchain, which achieves the fast positioning of the ciphertext address on the chain. Finally, we utilize the edge cloud server to realize proxy decryption of ciphertexts, which further reduces the computation pressure on the vehicle terminal. Formal security analysis and performance comparison experiments show that our scheme is highly secure under the DBDH assumption and performs well in terms of communication and computation efficiency.
{"title":"Attribute-based searchable encryption for secure data sharing with blockchain in VANETs","authors":"Zhenqi Wang , Peng Yang , Angze Du , Dongmei Yang , Xiaoyan Liu","doi":"10.1016/j.comnet.2026.112026","DOIUrl":"10.1016/j.comnet.2026.112026","url":null,"abstract":"<div><div>Vehicular Ad Hoc Networks (VANETs) are critical for enhancing road safety and optimizing traffic flow. Attribute-based searchable encryption (ABSE) with blockchain supports one-to-many encryption, ciphertext search, and decentralized storage, providing a solution to enable data sharing in VANETs. However, existing VANETs schemes face many limitations: blockchain miner nodes are at risk of stealing vehicle attribute keys during key verification; the lack of permission control may also lead to unauthorized downloads; and multi-keyword search, encryption, and decryption require a lot of computing power. To address these issues, we propose an attribute-based searchable encryption scheme for secure data sharing with blockchain (ABSE-VANETs). Specifically, we introduce multiple authorization authorities to manage attributes and generate obfuscated key parameters via bilinear pairing to prevent the leakage of vehicle attribute keys. We combine linear integer secret sharing (LISS) to bind the ciphertext download permission and the vehicle attribute set to prevent unauthorized ciphertext download. Moreover, we optimize the multi-keyword index generation algorithm and trapdoor structure and construct a multi-keyword index with the help of blockchain, which achieves the fast positioning of the ciphertext address on the chain. Finally, we utilize the edge cloud server to realize proxy decryption of ciphertexts, which further reduces the computation pressure on the vehicle terminal. Formal security analysis and performance comparison experiments show that our scheme is highly secure under the DBDH assumption and performs well in terms of communication and computation efficiency.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 112026"},"PeriodicalIF":4.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2026-01-15DOI: 10.1016/j.comnet.2026.111994
Ling Xing, Jingjing Cui, Kaikai Deng, Honghai Wu, Huahong Ma
Federated Learning (FL) has emerged as a key distributed framework for processing real-time vehicular data in the Internet of Vehicles (IoV), aiming to enhance driving experiences and service quality. However, challenges such as network congestion, limited signal coverage leading to latency, and inefficiencies arising from data and device heterogeneity hinder FL performance in IoV environments. To address these issues, we propose HierFed, a hierarchical federated learning algorithm that combines synchronous and asynchronous aggregation strategies, which comprises two components: (i) an asynchronous cache aggregation mechanism, where client-side cache updates are transmitted to the edge server for asynchronous aggregation. By calibrating the relationship between cache updates and model deviations, clients can update models without synchronous waiting, thereby mitigating latency; (ii) a synchronous aggregation mechanism, where the cloud server optimizes global model updates by computing aggregation weights under gradient normalization and constraint conditions, followed by reducing inter-client model inconsistency and enhances overall model convergence. Experimental results show that HierFed outperforms all baselines, achieving an 8.83% improvement in model accuracy at the edge tier (asynchronous FL), a 13.84% improvement at the cloud tier (synchronous FL), and a 10.79% overall improvement in hierarchical FL.
{"title":"Hierarchical federated learning algorithm with synchronous and asynchronous aggregation collaboration in internet of vehicles","authors":"Ling Xing, Jingjing Cui, Kaikai Deng, Honghai Wu, Huahong Ma","doi":"10.1016/j.comnet.2026.111994","DOIUrl":"10.1016/j.comnet.2026.111994","url":null,"abstract":"<div><div>Federated Learning (FL) has emerged as a key distributed framework for processing real-time vehicular data in the Internet of Vehicles (IoV), aiming to enhance driving experiences and service quality. However, challenges such as network congestion, limited signal coverage leading to latency, and inefficiencies arising from data and device heterogeneity hinder FL performance in IoV environments. To address these issues, we propose <em>HierFed</em>, a hierarchical federated learning algorithm that combines synchronous and asynchronous aggregation strategies, which comprises two components: (i) an <em>asynchronous cache aggregation mechanism</em>, where client-side cache updates are transmitted to the edge server for asynchronous aggregation. By calibrating the relationship between cache updates and model deviations, clients can update models without synchronous waiting, thereby mitigating latency; (ii) a <em>synchronous aggregation mechanism</em>, where the cloud server optimizes global model updates by computing aggregation weights under gradient normalization and constraint conditions, followed by reducing inter-client model inconsistency and enhances overall model convergence. Experimental results show that <em>HierFed</em> outperforms all baselines, achieving an 8.83% improvement in model accuracy at the edge tier (asynchronous FL), a 13.84% improvement at the cloud tier (synchronous FL), and a 10.79% overall improvement in hierarchical FL.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"277 ","pages":"Article 111994"},"PeriodicalIF":4.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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