Ad Hoc Networks最新文献

Sum rate maximization for RSMA aided small cells edge users using meta-learning variational quantum algorithm

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

Ad Hoc Networks

Pub Date : 2025-02-25 DOI: 10.1016/j.adhoc.2025.103802

Deepak Gupta, Ishan Budhiraja, Bireshwar Dass Mazumdar

This study aims to enhance wireless communication efficiency by maximizing the sum rate through optimized rate allocation and power control for edge users in small cell networks. Small cells improve coverage and bandwidth in congested networks but face challenges such as interference and limited resources, particularly for users at the cell edge. This article introduces a Meta-LVQA technique to boost system throughput by optimizing rate allocation and power control, ensuring equitable resource distribution among users, and managing in-cell interference using Rate Splitting Multiple Access (RSMA). The problem is initially framed using classical methods. However, this manuscript employs the Meta-Learning Variational Quantum Algorithm (Meta-LVQA) to optimize the sum rate. Therefore, it is necessary to transform the classical equation into an equivalent quantum equation using a quantum circuit. Numerical results demonstrate that RSMA with Meta-LVQA consistently outperforms all other methods. Specifically, RSMA with Meta-LVQA surpasses RSMA with Variational Quantum Algorithm (VQA), NOMA with Meta-LVQA, and NOMA with VQA by

3.91 %, 10.11 %,

and

31.99 %,

respectively, when the sum rate is measured against a minimum rate requirement of 1.15 Mbps at SCEU1. When computing the sum rate using four SCEUs, RSMA with Meta-LVQA outperforms RSMA with VQA, NOMA with Meta-LVQA, and NOMA with VQA by

13.91 %, 18.63 %,

and

43.06 %,

respectively.

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引用次数: 0

Reliability and bandwidth aware routing in SDN-based fog computing for IoT applications

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

Ad Hoc Networks

Pub Date : 2025-02-25 DOI: 10.1016/j.adhoc.2025.103803

Parisa Valizadeh, Mohammad Hossein Yaghmaee, Yasser Sedaghat

Software-Defined Networking (SDN) and fog computing are pivotal in supporting computationally intensive tasks within Internet of Things (IoT) applications, enhancing efficiency and reliability. However, many IoT applications are constrained by communication paths prone to link failures, necessitating robust fault tolerance techniques to ensure reliable traffic flow. In particular, real-time IoT applications demand stringent reliability and bandwidth requirements (constraints), which are challenging to meet simultaneously. Although previous research has investigated SDN-based routing to improve reliability, developing a routing algorithm that satisfies both reliability and bandwidth constraints remains an NP-hard problem. In this paper, we propose two novel routing algorithms: Reliability Aware Bandwidth constrained Routing (RABR) and Reliability and Bandwidth Constrained Routing (RBCR), specifically designed for SDN-enabled environments. Our approach prioritizes service reliability while meeting strict reliability and bandwidth criteria. The proposed solution integrates several phases, including reliability aware and bandwidth constrained path routing and flow duplication through parallel/hybrid and sequential routing methods. Furthermore, we introduce a greedy heuristic algorithm, implemented by the SDN controller with an efficient time complexity. Simulation results demonstrate that our algorithm surpasses state-of-the-art approaches in critical metrics such as reliability, reliability-bandwidth success rate, and Runtime. As such, our solution emerges as a robust choice for SDN-enabled IoT environments.

{"title":"Reliability and bandwidth aware routing in SDN-based fog computing for IoT applications","authors":"Parisa Valizadeh, Mohammad Hossein Yaghmaee, Yasser Sedaghat","doi":"10.1016/j.adhoc.2025.103803","DOIUrl":"10.1016/j.adhoc.2025.103803","url":null,"abstract":"<div><div>Software-Defined Networking (SDN) and fog computing are pivotal in supporting computationally intensive tasks within Internet of Things (IoT) applications, enhancing efficiency and reliability. However, many IoT applications are constrained by communication paths prone to link failures, necessitating robust fault tolerance techniques to ensure reliable traffic flow. In particular, real-time IoT applications demand stringent reliability and bandwidth requirements (constraints), which are challenging to meet simultaneously. Although previous research has investigated SDN-based routing to improve reliability, developing a routing algorithm that satisfies both reliability and bandwidth constraints remains an NP-hard problem. In this paper, we propose two novel routing algorithms: Reliability Aware Bandwidth constrained Routing (RABR) and Reliability and Bandwidth Constrained Routing (RBCR), specifically designed for SDN-enabled environments. Our approach prioritizes service reliability while meeting strict reliability and bandwidth criteria. The proposed solution integrates several phases, including reliability aware and bandwidth constrained path routing and flow duplication through parallel/hybrid and sequential routing methods. Furthermore, we introduce a greedy heuristic algorithm, implemented by the SDN controller with an efficient time complexity. Simulation results demonstrate that our algorithm surpasses state-of-the-art approaches in critical metrics such as reliability, reliability-bandwidth success rate, and Runtime. As such, our solution emerges as a robust choice for SDN-enabled IoT environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"172 ","pages":"Article 103803"},"PeriodicalIF":4.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511460","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}

引用次数: 0

Collaborative multi-target-tracking via graph-based deep reinforcement learning in UAV swarm networks

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

Ad Hoc Networks

Pub Date : 2025-02-23 DOI: 10.1016/j.adhoc.2025.103801

Qianchen Ren , Yuanyu Wang, Han Liu, Yu Dai, Wenhui Ye, Yuliang Tang

Due to unmanned aerial vehicles (UAVs) flexibility and affordability, the UAVs swarm network (USNET) is widely used for various complex, challenging tasks such as tracking, surveillance, and monitoring, and the key to accomplishing these tasks lies in the capabilities of the UAVs to collaborate. However, due to the high complexity of real-time information sharing and task cooperation among numerous UAVs in the USNET, it poses significant challenges for multi-target tracking in complex scenarios. In this paper, we study the collaborative multi-target-tracking (CMTT) problem based on the USNET and aim to improve task collaboration capabilities within the USNET. We first design a heuristic target assignment algorithm to simplify the CMTT problem into the optimal topology control problem of the USNET, and then propose an integrated sensing and communication multi-agent reinforcement learning for the USNET topology control algorithm (ISAC-TC) to maximize the collaborative tracking performance of UAVs within the USNET. Specifically, in heterogeneous observation graph representation, the ISAC-TC first utilizes a graph neural network to solve the time-varying dimensions of the agent observation space. Then, an encoder–decoder-based information sharing module is used to achieve efficient communication between agents in the CMTT tasks. Simulation results show that the proposed scheme achieves a higher tracking success rate and tracking fairness than other baselines.

{"title":"Collaborative multi-target-tracking via graph-based deep reinforcement learning in UAV swarm networks","authors":"Qianchen Ren , Yuanyu Wang, Han Liu, Yu Dai, Wenhui Ye, Yuliang Tang","doi":"10.1016/j.adhoc.2025.103801","DOIUrl":"10.1016/j.adhoc.2025.103801","url":null,"abstract":"<div><div>Due to unmanned aerial vehicles (UAVs) flexibility and affordability, the UAVs swarm network (USNET) is widely used for various complex, challenging tasks such as tracking, surveillance, and monitoring, and the key to accomplishing these tasks lies in the capabilities of the UAVs to collaborate. However, due to the high complexity of real-time information sharing and task cooperation among numerous UAVs in the USNET, it poses significant challenges for multi-target tracking in complex scenarios. In this paper, we study the collaborative multi-target-tracking (CMTT) problem based on the USNET and aim to improve task collaboration capabilities within the USNET. We first design a heuristic target assignment algorithm to simplify the CMTT problem into the optimal topology control problem of the USNET, and then propose an integrated sensing and communication multi-agent reinforcement learning for the USNET topology control algorithm (ISAC-TC) to maximize the collaborative tracking performance of UAVs within the USNET. Specifically, in heterogeneous observation graph representation, the ISAC-TC first utilizes a graph neural network to solve the time-varying dimensions of the agent observation space. Then, an encoder–decoder-based information sharing module is used to achieve efficient communication between agents in the CMTT tasks. Simulation results show that the proposed scheme achieves a higher tracking success rate and tracking fairness than other baselines.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"172 ","pages":"Article 103801"},"PeriodicalIF":4.4,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510402","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}

引用次数: 0

An optimizing geo-distributed edge layering with double deep Q-networks for predictive mobility-aware offloading in mobile edge computing

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

Ad Hoc Networks

Pub Date : 2025-02-21 DOI: 10.1016/j.adhoc.2025.103804

Amir Masoud Rahmani , Amir Haider , Saqib Ali , Shakiba Rajabi , Farhad Soleimanian Gharehchopogh , Parisa Khoshvaght , Mehdi Hosseinzadeh

In Mobile Edge Computing (MEC), the exponential growth of connected devices and user mobility presents significant challenges in optimizing task offloading, reducing latency, and energy usage. Predictive and adaptive task offloading mechanisms are essential as devices become more mobile and generate demanding tasks. Current methods, such as local computing and random scheduling, struggle to efficiently manage resources and maintain Quality of Service (QoS) in dynamic environments. This paper proposes an optimized Geographic Distributed Edge Layering (GDEL) architecture integrated with Double Deep Q-Networks (DDQN) to enable predictive, mobility-aware offloading. Our model leverages reinforcement learning through a Markov Decision Process (MDP) framework to dynamically allocate resources across distributed edge nodes, making optimal decisions on whether to offload or process tasks locally based on real-time conditions. Simulations show that our model outperforms other methods in key performance metrics, reducing task completion time by up to 48 %, lowering offloading decision latency by 49.3 %, and decreasing energy consumption by 26.5 % compared to traditional models.

{"title":"An optimizing geo-distributed edge layering with double deep Q-networks for predictive mobility-aware offloading in mobile edge computing","authors":"Amir Masoud Rahmani , Amir Haider , Saqib Ali , Shakiba Rajabi , Farhad Soleimanian Gharehchopogh , Parisa Khoshvaght , Mehdi Hosseinzadeh","doi":"10.1016/j.adhoc.2025.103804","DOIUrl":"10.1016/j.adhoc.2025.103804","url":null,"abstract":"<div><div>In Mobile Edge Computing (MEC), the exponential growth of connected devices and user mobility presents significant challenges in optimizing task offloading, reducing latency, and energy usage. Predictive and adaptive task offloading mechanisms are essential as devices become more mobile and generate demanding tasks. Current methods, such as local computing and random scheduling, struggle to efficiently manage resources and maintain Quality of Service (QoS) in dynamic environments. This paper proposes an optimized Geographic Distributed Edge Layering (GDEL) architecture integrated with Double Deep Q-Networks (DDQN) to enable predictive, mobility-aware offloading. Our model leverages reinforcement learning through a Markov Decision Process (MDP) framework to dynamically allocate resources across distributed edge nodes, making optimal decisions on whether to offload or process tasks locally based on real-time conditions. Simulations show that our model outperforms other methods in key performance metrics, reducing task completion time by up to 48 %, lowering offloading decision latency by 49.3 %, and decreasing energy consumption by 26.5 % compared to traditional models.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"172 ","pages":"Article 103804"},"PeriodicalIF":4.4,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480670","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}

引用次数: 0

QRAVDR: A deep Q-learning-based RSU-Assisted Video Data Routing algorithm for VANETs

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

Ad Hoc Networks

Pub Date : 2025-02-19 DOI: 10.1016/j.adhoc.2025.103790

Huahong Ma, Shuangjin Li, Honghai Wu, Ling Xing, Xiaohui Zhang

With the rapid development of Internet of Vehicles (IoV) and the increasing demand for video services, video data routing in Vehicular Ad-hoc Networks (VANETs) has become a popular research topic. Challenges such as real-time transmission demands, instability of wireless channels, and high network topology dynamics significantly affect video transmission quality. Although some related studies have used multipath transmission and priority scheduling to improve performance, they usually require accurate models or use a static approach to make decisions, which lack the learning mechanism and the ability to adapt to the dynamic network, resulting in poor video reconstruction quality. To address the above problems, A Deep Q-Learning (DQL)-based RoadSide Unit (RSU)-Assisted Video Data Routing algorithm, named QRAVDR, is proposed for urban VANET environments. The algorithm coordinates the forwarding road segments of different layers of Scalable Video Coding (SVC) video data at the RSUs through DQL, maximizing the video quality at the receiver while minimizing the transmission delay. The Neutrosophic Set Analytic Hierarchy Process method is applied to select the best relay vehicle within the road segments, which guarantees the transmission of keyframes and improves the decoding possibility. Extensive simulation experiments on QRAVDR and other existing algorithms have been conducted using NS-2 employing simulated datasets. The results show that QRAVDR achieves a better overall performance in improving the average frame delivery ratio by about 8.02%, reducing the average end-to-end delay by approximately 9.61%, and improving the average peak signal-to-noise ratio by roughly 7.97%.

{"title":"QRAVDR: A deep Q-learning-based RSU-Assisted Video Data Routing algorithm for VANETs","authors":"Huahong Ma, Shuangjin Li, Honghai Wu, Ling Xing, Xiaohui Zhang","doi":"10.1016/j.adhoc.2025.103790","DOIUrl":"10.1016/j.adhoc.2025.103790","url":null,"abstract":"<div><div>With the rapid development of Internet of Vehicles (IoV) and the increasing demand for video services, video data routing in Vehicular Ad-hoc Networks (VANETs) has become a popular research topic. Challenges such as real-time transmission demands, instability of wireless channels, and high network topology dynamics significantly affect video transmission quality. Although some related studies have used multipath transmission and priority scheduling to improve performance, they usually require accurate models or use a static approach to make decisions, which lack the learning mechanism and the ability to adapt to the dynamic network, resulting in poor video reconstruction quality. To address the above problems, A Deep Q-Learning (DQL)-based RoadSide Unit (RSU)-Assisted Video Data Routing algorithm, named QRAVDR, is proposed for urban VANET environments. The algorithm coordinates the forwarding road segments of different layers of Scalable Video Coding (SVC) video data at the RSUs through DQL, maximizing the video quality at the receiver while minimizing the transmission delay. The Neutrosophic Set Analytic Hierarchy Process method is applied to select the best relay vehicle within the road segments, which guarantees the transmission of keyframes and improves the decoding possibility. Extensive simulation experiments on QRAVDR and other existing algorithms have been conducted using NS-2 employing simulated datasets. The results show that QRAVDR achieves a better overall performance in improving the average frame delivery ratio by about 8.02%, reducing the average end-to-end delay by approximately 9.61%, and improving the average peak signal-to-noise ratio by roughly 7.97%.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"171 ","pages":"Article 103790"},"PeriodicalIF":4.4,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453558","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}

引用次数: 0

NOMA-based intelligent resource allocation and trajectory optimization for multi-UAVs assisted semantic communication networks

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

Ad Hoc Networks

Pub Date : 2025-02-18 DOI: 10.1016/j.adhoc.2025.103762

Ping Xie, Qian Chen, JingYan Wu, Xiangrui Gao, Ling Xing, Yu Zhang, Hanxiao Sun

The limited spectrum resources have a particular impact on UAV-assisted semantic communication networks, which undoubtedly leads to poorer quality of service for users and inefficient communication. Therefore, a NOMA-based multi-UAVs assisted semantic cellular network framework is proposed in this paper, in which each UAV transmits semantic information to multiple users in the shared spectrum resource with different power using non-orthogonal multiple access transmission protocol, thereby achieving higher spectrum utilization. We optimize the quantity of semantic symbols, UAV trajectories, and power allocation concurrently to increase communication efficiency by maximizing the sum rate of semantic information transmission for all users. However, conventional convex optimization approaches have difficulty solving it due to the bi-directional mobility of UAVs and users. Therefore, an enhanced K-means algorithm is employed to create the relationship between UAVs and users periodically. Additionally, a deep reinforcement learning technique based on shared dueling double deep Q networks (SD3QN) is also presented to maximize the quantity of semantic symbols, 3D trajectories, and power allocation. Experimental results show that the proposed semantic cellular network achieves higher spectral efficiency. Meanwhile, the proposed algorithm can effectively reduce the training time and avoid the overestimation problem in Deep Q Networks (DQN). Furthermore, the suggested optimization strategy outperforms the benchmark schemes in terms of semantic sum rate.

{"title":"NOMA-based intelligent resource allocation and trajectory optimization for multi-UAVs assisted semantic communication networks","authors":"Ping Xie, Qian Chen, JingYan Wu, Xiangrui Gao, Ling Xing, Yu Zhang, Hanxiao Sun","doi":"10.1016/j.adhoc.2025.103762","DOIUrl":"10.1016/j.adhoc.2025.103762","url":null,"abstract":"<div><div>The limited spectrum resources have a particular impact on UAV-assisted semantic communication networks, which undoubtedly leads to poorer quality of service for users and inefficient communication. Therefore, a NOMA-based multi-UAVs assisted semantic cellular network framework is proposed in this paper, in which each UAV transmits semantic information to multiple users in the shared spectrum resource with different power using non-orthogonal multiple access transmission protocol, thereby achieving higher spectrum utilization. We optimize the quantity of semantic symbols, UAV trajectories, and power allocation concurrently to increase communication efficiency by maximizing the sum rate of semantic information transmission for all users. However, conventional convex optimization approaches have difficulty solving it due to the bi-directional mobility of UAVs and users. Therefore, an enhanced K-means algorithm is employed to create the relationship between UAVs and users periodically. Additionally, a deep reinforcement learning technique based on shared dueling double deep Q networks (SD3QN) is also presented to maximize the quantity of semantic symbols, 3D trajectories, and power allocation. Experimental results show that the proposed semantic cellular network achieves higher spectral efficiency. Meanwhile, the proposed algorithm can effectively reduce the training time and avoid the overestimation problem in Deep Q Networks (DQN). Furthermore, the suggested optimization strategy outperforms the benchmark schemes in terms of semantic sum rate.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"171 ","pages":"Article 103762"},"PeriodicalIF":4.4,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143444307","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}

引用次数: 0

URNFresh: Age-of-infomation-based 60 GHz UAV relay networks for video surveillance in linear environments

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

Ad Hoc Networks

Pub Date : 2025-02-14 DOI: 10.1016/j.adhoc.2025.103789

Wenjia Wu , Hui Lv , Shengyu Sun

In recent years, video surveillance has been widely deployed and utilized, with linear deployment environments such as roads and rivers being very common. With the rapid development and widespread application of 60 GHz communication and unmanned aerial vehicle (UAV) technologies, 60 GHz UAV relay networks have become an ideal solution for high-rate data collection in video surveillance. In this network scenario, the collaborative scheduling of multiple UAVs has become a key issue. However, the existing scheduling schemes are usually designed for two-dimensional or three-dimensional scenarios, lacking relevant considerations and designs for the characteristics of one-dimensional linear scenarios. In addition, these methods rarely consider ensuring data freshness and the age-of-information (AoI) metric to meet the needs of latency-sensitive applications. To this end, we consider the 60 GHz UAV relay network for video surveillance, and investigate the AoI-based multi-UAV collaborative scheduling mechanism in linear environments. Firstly, We formulate the energy-storage-limited and AoI-guaranteed Multi-UAV scheduling problem, which aims to minimize the average cumulative AoI, while considering the constraints of their energy and data storage capacity. Then, we propose the hierarchical reinforcement learning-based multi-UAV collaborative scheduling mechanism called URNFresh, and design corresponding strategies for option selection and fine-grained action selection in aspects such as flight control, data collection, data offloading, and battery replacement. Finally, we conduct simulation experiments to evaluate the performance of URNFresh mechanism. Experimental results demonstrate that the proposed solution outperforms traditional reinforcement learning approaches, and achieves a significant improvement in average cumulative AoI.

{"title":"URNFresh: Age-of-infomation-based 60 GHz UAV relay networks for video surveillance in linear environments","authors":"Wenjia Wu , Hui Lv , Shengyu Sun","doi":"10.1016/j.adhoc.2025.103789","DOIUrl":"10.1016/j.adhoc.2025.103789","url":null,"abstract":"<div><div>In recent years, video surveillance has been widely deployed and utilized, with linear deployment environments such as roads and rivers being very common. With the rapid development and widespread application of 60 GHz communication and unmanned aerial vehicle (UAV) technologies, 60 GHz UAV relay networks have become an ideal solution for high-rate data collection in video surveillance. In this network scenario, the collaborative scheduling of multiple UAVs has become a key issue. However, the existing scheduling schemes are usually designed for two-dimensional or three-dimensional scenarios, lacking relevant considerations and designs for the characteristics of one-dimensional linear scenarios. In addition, these methods rarely consider ensuring data freshness and the age-of-information (AoI) metric to meet the needs of latency-sensitive applications. To this end, we consider the 60 GHz UAV relay network for video surveillance, and investigate the AoI-based multi-UAV collaborative scheduling mechanism in linear environments. Firstly, We formulate the energy-storage-limited and AoI-guaranteed Multi-UAV scheduling problem, which aims to minimize the average cumulative AoI, while considering the constraints of their energy and data storage capacity. Then, we propose the hierarchical reinforcement learning-based multi-UAV collaborative scheduling mechanism called URNFresh, and design corresponding strategies for option selection and fine-grained action selection in aspects such as flight control, data collection, data offloading, and battery replacement. Finally, we conduct simulation experiments to evaluate the performance of URNFresh mechanism. Experimental results demonstrate that the proposed solution outperforms traditional reinforcement learning approaches, and achieves a significant improvement in average cumulative AoI.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"171 ","pages":"Article 103789"},"PeriodicalIF":4.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427949","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}

引用次数: 0

Fast convergent actor–critic reinforcement learning based interference coordination algorithm in D2D networks

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

Ad Hoc Networks

Pub Date : 2025-02-14 DOI: 10.1016/j.adhoc.2025.103788

Chen Sun, Jijun Yang, Zhicheng Cao, Zhiyong Yang, Youfeng Yang, Jian Shu

This paper presents a Fast Convergent Advantage Actor–Critic (FC-A2C) reinforcement learning algorithm designed to address interference coordination in Device-to-Device (D2D) networks. Traditional reinforcement learning-based interference coordination algorithms often suffer from high complexity and prolonged convergence times. To overcome these limitations, the proposed FC-A2C algorithm integrates a feature extraction network to reduce computational redundancy, a dual-head actor network to separately handle resource allocation and power control, and a central critic network to generate advantage values based on the rewards collected from the nearby agents. These improvements collectively accelerate the convergence of the algorithm while maintaining optimal network performance. Simulation results demonstrate that the FC-A2C algorithm significantly outperforms conventional and typical reinforcement learning-based interference coordination algorithms in terms of convergence speed and multiple performance metrics. The proposed algorithm achieves up to 83% faster convergence and up to 6.1% better network performance compared to existing methods, making it a promising solution for efficient interference coordination in D2D networks.

{"title":"Fast convergent actor–critic reinforcement learning based interference coordination algorithm in D2D networks","authors":"Chen Sun, Jijun Yang, Zhicheng Cao, Zhiyong Yang, Youfeng Yang, Jian Shu","doi":"10.1016/j.adhoc.2025.103788","DOIUrl":"10.1016/j.adhoc.2025.103788","url":null,"abstract":"<div><div>This paper presents a Fast Convergent Advantage Actor–Critic (FC-A2C) reinforcement learning algorithm designed to address interference coordination in Device-to-Device (D2D) networks. Traditional reinforcement learning-based interference coordination algorithms often suffer from high complexity and prolonged convergence times. To overcome these limitations, the proposed FC-A2C algorithm integrates a feature extraction network to reduce computational redundancy, a dual-head actor network to separately handle resource allocation and power control, and a central critic network to generate advantage values based on the rewards collected from the nearby agents. These improvements collectively accelerate the convergence of the algorithm while maintaining optimal network performance. Simulation results demonstrate that the FC-A2C algorithm significantly outperforms conventional and typical reinforcement learning-based interference coordination algorithms in terms of convergence speed and multiple performance metrics. The proposed algorithm achieves up to 83% faster convergence and up to 6.1% better network performance compared to existing methods, making it a promising solution for efficient interference coordination in D2D networks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"171 ","pages":"Article 103788"},"PeriodicalIF":4.4,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427950","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}

引用次数: 0

A deep learning-based strategy for energy-efficient parallel computation offloading in mobile edge networks

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

Ad Hoc Networks

Pub Date : 2025-02-13 DOI: 10.1016/j.adhoc.2025.103787

Haris Khan , Zaiwar Ali , Ziaul Haq Abbas , Ghulam Abbas , Sheroz Khan , Muhammad Yahya

The growing demand for real-time computing applications on mobile devices is burdening their processing power and battery life. Mobile edge computing helps by allowing these tasks to be offloaded to nearby servers having more processing power. However, when it comes to multiple servers and tasks, choosing the optimal components for offloading becomes challenging. This is because we need to balance between reducing the amount of data transferred and keeping communication latency low. To address this problem, an energy-efficient parallel computation offloading mechanism through deep learning (EPCOD), is proposed. An algorithm using deep learning (DL) is developed and trained as a decision-making system. This system selects the best combination of application components taking into account various factors, such as energy consumption, network conditions, computational load, data transfer volume, and communication latency. A cost function that includes all these factors is developed to calculate the cost for each possible offloading policy combination. By analyzing a large dataset, we find the best policies. Additionally, we use a DL network to efficiently handle this computational task. Simulation results demonstrate that EPCOD effectively minimizes both latency and energy consumption, achieving a high accuracy of deep neural network of up to 73.5%.

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引用次数: 0

PFCS: An efficient Path setup and Fast Channel Switching protocol for cognitive mobile IoTs

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

Ad Hoc Networks

Pub Date : 2025-02-11 DOI: 10.1016/j.adhoc.2025.103786

Muhammad Nadeem , Muhammad Maaz Rehan , Saima Gulzar Ahmad , Ehsan Ullah Munir , Muhammad Waqas Rehan

Cognitive Radio Networks (CRNs) have become a prominent platform in recent years, particularly in the context of the Internet of Things (IoT) and Industry 5.0. CRNs include Primary Users (PUs) and Secondary Users (SUs). PUs are licensed users with priority over SUs for spectrum utilization. The growth rate of wireless devices is 40% annually. The available wireless spectrum is not quite enough to handle this immense growth rate. These facts makes channel assignment in CRNs as one of the highly explored research areas. In this study, we propose a cross-layer efficient routing protocol and channel selection algorithm for IoT named, an efficient Path setup and Fast Channel Switching (PFCS) for IoT-based Mobile Cognitive Radio Ad Hoc Networks (CRAHNs). PFCS evaluates multiple paths using various important network parameters and selects the path with minimal end-to-end channel switching and the highest score. Additionally, PFCS proposes a local channel recovery algorithm to handle PU activity through controlled broadcast. The recovery algorithm aims to resume ongoing communication with minimal control overhead. The proposed PFCS is compared with state-of-the-art, Software-Defined Routing Protocol (SDRP) and Optimal Channel Selection algorithm for CRahNs (OCSCRN) with varying numbers of PUs and SUs in Mobile Cognitive Radio Networks (MCRNs). Results demonstrate that PFCS outperforms SDRP and OCSCRN in terms of Delay, Packet Delivery Function (PDF), Routing overhead, and Network life.

近年来，认知无线电网络（CRN）已成为一个突出的平台，尤其是在物联网（IoT）和工业 5.0 的背景下。CRN 包括主用户（PU）和次用户（SU）。PU 是获得许可的用户，在频谱利用方面比 SU 具有优先权。无线设备的年增长率为 40%。可用的无线频谱不足以应对这一巨大的增长率。这些事实使得 CRN 中的信道分配成为备受关注的研究领域之一。在这项研究中，我们为物联网提出了一种跨层高效路由协议和信道选择算法，即基于物联网的移动认知无线电特设组网（CRAHNs）的高效路径设置和快速信道切换（PFCS）。PFCS 利用各种重要的网络参数评估多个路径，并选择端到端信道切换最少、得分最高的路径。此外，PFCS 还提出了一种本地信道恢复算法，通过受控广播处理 PU 活动。恢复算法旨在以最小的控制开销恢复正在进行的通信。在移动认知无线电网络（MCRN）中，将 PFCS 与最先进的软件定义路由协议（SDRP）和具有不同数量 PU 和 SU 的 CRahNs 最佳信道选择算法（OCSCRN）进行了比较。结果表明，PFCS 在延迟、数据包传送功能（PDF）、路由开销和网络寿命方面均优于 SDRP 和 OCSCRN。

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引用次数: 0