Pub Date : 2025-02-06DOI: 10.1016/j.adhoc.2025.103767
Faezeh Pasandideh , Alireza Najafzadeh , João Paulo Javidi da Costa , Giovanni Almeida Santos , Daniel Valle de Lima , Edison Pignaton de Freitas
As wireless communication continues to advance, the move towards Sixth-Generation (6G) networks has heightened the need for faster data speeds and reliable connections, prompting new approaches to connectivity. In scenarios such as natural disasters, where Ground Base Stations (GBSs) may be compromised, the use of Unmanned Aerial Vehicles (UAVs) has become increasingly important. A promising approach is to deploy low-altitude UAVs equipped with compact Base Stations (BSs) to reestablish essential communication networks and offer temporary coverage. However, identifying the optimal locations for these UAV-BSs presents a complex challenge. This paper proposes an innovative solution using UAVs as base stations (UAV-BSs) and introduces a Mixed-Integer Non-Linear Programming (MINLP) optimization model to position UAV-BSs based on real-time demand and network conditions. Traditional methods struggle with the complexity of UAV-BS deployment, so a novel algorithm combining the JAYA optimization technique is used. Extensive experiments show this approach maximizes network coverage and connectivity while minimizing UAV-BS power consumption, outperforming other methods in placement accuracy, power usage, packet loss, and latency. The algorithm also adapts to varying network conditions, making it a valuable tool for optimizing UAV-BS locations in dynamic environments.
{"title":"Providing an energy efficient UAV BS positioning mechanism to improve wireless connectivity","authors":"Faezeh Pasandideh , Alireza Najafzadeh , João Paulo Javidi da Costa , Giovanni Almeida Santos , Daniel Valle de Lima , Edison Pignaton de Freitas","doi":"10.1016/j.adhoc.2025.103767","DOIUrl":"10.1016/j.adhoc.2025.103767","url":null,"abstract":"<div><div>As wireless communication continues to advance, the move towards Sixth-Generation (6G) networks has heightened the need for faster data speeds and reliable connections, prompting new approaches to connectivity. In scenarios such as natural disasters, where Ground Base Stations (GBSs) may be compromised, the use of Unmanned Aerial Vehicles (UAVs) has become increasingly important. A promising approach is to deploy low-altitude UAVs equipped with compact Base Stations (BSs) to reestablish essential communication networks and offer temporary coverage. However, identifying the optimal locations for these UAV-BSs presents a complex challenge. This paper proposes an innovative solution using UAVs as base stations (UAV-BSs) and introduces a Mixed-Integer Non-Linear Programming (MINLP) optimization model to position UAV-BSs based on real-time demand and network conditions. Traditional methods struggle with the complexity of UAV-BS deployment, so a novel algorithm combining the JAYA optimization technique is used. Extensive experiments show this approach maximizes network coverage and connectivity while minimizing UAV-BS power consumption, outperforming other methods in placement accuracy, power usage, packet loss, and latency. The algorithm also adapts to varying network conditions, making it a valuable tool for optimizing UAV-BS locations in dynamic environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103767"},"PeriodicalIF":4.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-05DOI: 10.1016/j.adhoc.2025.103764
Taewoo Lee , Hyunbum Kim , Sherali Zeadally
As the urban population grows and city infrastructures become more complex, the need for efficient and responsive security systems in smart buildings becomes increasingly crucial. Traditional security systems, which rely heavily on fixed surveillance cameras and sensors, face challenges in adapting to real-time situational changes and handling large volumes of data. To address these issues, this study leverages deep learning technology to enhance smart building security through two innovative surveillance adjustment algorithms for smart building with classified levels: the Centralized Surveillance Adjustment Algorithm and the Hierarchical Surveillance Adjustment Algorithm. These algorithms are designed to optimize the placement of security nodes and generate security barriers, ensuring comprehensive coverage and efficient resource allocation. The Centralized Surveillance Adjustment Algorithm monitor areas with the highest surveillance levels and adjusts surrounding regions’ surveillance accordingly, and allocates resources where they are most needed. The Hierarchical Surveillance Adjustment Algorithm classifies areas into different risk levels and adjusts surveillance hierarchically to prioritize high-risk areas. We developed a deep learning model to predict the required surveillance levels based on real-time data, facilitating dynamic and responsive security adjustments. We evaluate the performance of our proposed algorithms in a simulation environment. The results demonstrated that the Centralized Algorithm consistently outperforms the Hierarchical Algorithm in larger areas, providing superior coverage and adaptability.
{"title":"Designing deep learning-enabled surveillance model with classified security levels for smart area networks","authors":"Taewoo Lee , Hyunbum Kim , Sherali Zeadally","doi":"10.1016/j.adhoc.2025.103764","DOIUrl":"10.1016/j.adhoc.2025.103764","url":null,"abstract":"<div><div>As the urban population grows and city infrastructures become more complex, the need for efficient and responsive security systems in smart buildings becomes increasingly crucial. Traditional security systems, which rely heavily on fixed surveillance cameras and sensors, face challenges in adapting to real-time situational changes and handling large volumes of data. To address these issues, this study leverages deep learning technology to enhance smart building security through two innovative surveillance adjustment algorithms for smart building with classified levels: the Centralized Surveillance Adjustment Algorithm and the Hierarchical Surveillance Adjustment Algorithm. These algorithms are designed to optimize the placement of security nodes and generate security barriers, ensuring comprehensive coverage and efficient resource allocation. The Centralized Surveillance Adjustment Algorithm monitor areas with the highest surveillance levels and adjusts surrounding regions’ surveillance accordingly, and allocates resources where they are most needed. The Hierarchical Surveillance Adjustment Algorithm classifies areas into different risk levels and adjusts surveillance hierarchically to prioritize high-risk areas. We developed a deep learning model to predict the required surveillance levels based on real-time data, facilitating dynamic and responsive security adjustments. We evaluate the performance of our proposed algorithms in a simulation environment. The results demonstrated that the Centralized Algorithm consistently outperforms the Hierarchical Algorithm in larger areas, providing superior coverage and adaptability.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103764"},"PeriodicalIF":4.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348633","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 : 2025-02-04DOI: 10.1016/j.adhoc.2025.103785
Jiaxin Li , Peng Yi , Tong Duan , Zhen Zhang , Junfei Li , Yawen Wang , Jing Yu
The massive failures of unmanned aerial vehicles (UAVs) caused by malicious attacks or physical faults may lead to the UAV swarm splitting into several disconnected clusters. Each cluster cannot ascertain the statuses and positions of some other UAVs, making restoring connectivity face tremendous challenges. In this paper, a distributed connectivity restoration method for UAV communication networks is proposed, which combines the power of the deep reinforcement learning approach and the artificial potential field (APF) model. First, a connectivity restoration mechanism is designed, including UAV failure identification and UAV position relocation. The UAV position relocation involves an efficient exploration mechanism for outstanding performance in connectivity restoration. Subsequently, a hybrid connectivity restoration algorithm is proposed to train the agents to learn desired mobility strategies by applying multi-agent deep deterministic policy gradient (MADDPG) to accelerate connectivity restoration and APF for collision avoidance and connectivity maintenance within each cluster. The proposed algorithm is distributed and each UAV only utilizes the positions of other UAVs in the same cluster. Finally, the simulation results validate that the algorithm achieves faster connectivity restoration with shorter total motion distances of all operational UAVs than existing methods.
{"title":"Fast connectivity restoration of UAV communication networks based on distributed hybrid MADDPG and APF algorithm","authors":"Jiaxin Li , Peng Yi , Tong Duan , Zhen Zhang , Junfei Li , Yawen Wang , Jing Yu","doi":"10.1016/j.adhoc.2025.103785","DOIUrl":"10.1016/j.adhoc.2025.103785","url":null,"abstract":"<div><div>The massive failures of unmanned aerial vehicles (UAVs) caused by malicious attacks or physical faults may lead to the UAV swarm splitting into several disconnected clusters. Each cluster cannot ascertain the statuses and positions of some other UAVs, making restoring connectivity face tremendous challenges. In this paper, a distributed connectivity restoration method for UAV communication networks is proposed, which combines the power of the deep reinforcement learning approach and the artificial potential field (APF) model. First, a connectivity restoration mechanism is designed, including UAV failure identification and UAV position relocation. The UAV position relocation involves an efficient exploration mechanism for outstanding performance in connectivity restoration. Subsequently, a hybrid connectivity restoration algorithm is proposed to train the agents to learn desired mobility strategies by applying multi-agent deep deterministic policy gradient (MADDPG) to accelerate connectivity restoration and APF for collision avoidance and connectivity maintenance within each cluster. The proposed algorithm is distributed and each UAV only utilizes the positions of other UAVs in the same cluster. Finally, the simulation results validate that the algorithm achieves faster connectivity restoration with shorter total motion distances of all operational UAVs than existing methods.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"171 ","pages":"Article 103785"},"PeriodicalIF":4.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402670","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 : 2025-02-03DOI: 10.1016/j.adhoc.2025.103784
Miguel Barón , Luis Diez , Mihail Zverev , José R. Juárez , Ramón Agüero
Robust and efficient communication frameworks have become essential for the advancement of manufacturing and industrial processes in the era of Industry 4.0. This paper presents a comprehensive performance analysis of Eclipse Zenoh, a promising solution for the Industrial Internet of Things (IIoT). The analysis is conducted using a real testbed built with Raspberry Pi devices, comparing Eclipse Zenoh’s performance against the widely used Message Queuing Telemetry Transport (MQTT) protocol. The study assesses Eclipse Zenoh’s capabilities in terms of latency, as well as its reliability and congestion control mechanisms over various network topologies, using both Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). The results indicate that Eclipse Zenoh offers significant advantages in specific scenarios, making it a compelling choice for IIoT applications. Additionally, this paper contributes to a deeper understanding of Eclipse Zenoh’s underlying principles and its communication capabilities, positioning it as a versatile and efficient solution for modern industrial environments.
{"title":"On the performance of Zenoh in Industrial IoT Scenarios","authors":"Miguel Barón , Luis Diez , Mihail Zverev , José R. Juárez , Ramón Agüero","doi":"10.1016/j.adhoc.2025.103784","DOIUrl":"10.1016/j.adhoc.2025.103784","url":null,"abstract":"<div><div>Robust and efficient communication frameworks have become essential for the advancement of manufacturing and industrial processes in the era of Industry 4.0. This paper presents a comprehensive performance analysis of Eclipse Zenoh, a promising solution for the Industrial Internet of Things (IIoT). The analysis is conducted using a real testbed built with Raspberry Pi devices, comparing Eclipse Zenoh’s performance against the widely used Message Queuing Telemetry Transport (MQTT) protocol. The study assesses Eclipse Zenoh’s capabilities in terms of latency, as well as its reliability and congestion control mechanisms over various network topologies, using both Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). The results indicate that Eclipse Zenoh offers significant advantages in specific scenarios, making it a compelling choice for IIoT applications. Additionally, this paper contributes to a deeper understanding of Eclipse Zenoh’s underlying principles and its communication capabilities, positioning it as a versatile and efficient solution for modern industrial environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103784"},"PeriodicalIF":4.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143284269","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 : 2025-01-29DOI: 10.1016/j.adhoc.2025.103781
Per Natzschka, Burkhard Hensel, Christoph Sommer
Platooning control – the automatic distance control in a chain of vehicles – has seen more than 2000 publications in the past 30 years, along with a corresponding number of different algorithms. However, comparisons between such controllers have rarely been done. Moreover, a fair comparison requires that all controller parameters are chosen to be optimal, often manually, which is a labor intensive and hard to replicate process. In this article we demonstrate the benefits of a methodology for parameter selection that encompasses: an evaluator employing a common metric, a simulator component, and an optimizer, all integrated into an optimization framework – along with an open-source reference implementation. We also discuss the trade-offs of different optimization algorithms, both from the literature and custom-built, for parameter optimization of platooning controllers.
{"title":"Simopticon: Automated optimization of vehicular platooning controllers","authors":"Per Natzschka, Burkhard Hensel, Christoph Sommer","doi":"10.1016/j.adhoc.2025.103781","DOIUrl":"10.1016/j.adhoc.2025.103781","url":null,"abstract":"<div><div>Platooning control – the automatic distance control in a chain of vehicles – has seen more than 2000 publications in the past 30 years, along with a corresponding number of different algorithms. However, comparisons between such controllers have rarely been done. Moreover, a fair comparison requires that all controller parameters are chosen to be optimal, often manually, which is a labor intensive and hard to replicate process. In this article we demonstrate the benefits of a methodology for parameter selection that encompasses: an evaluator employing a common metric, a simulator component, and an optimizer, all integrated into an optimization framework – along with an open-source reference implementation. We also discuss the trade-offs of different optimization algorithms, both from the literature and custom-built, for parameter optimization of platooning controllers.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103781"},"PeriodicalIF":4.4,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-28DOI: 10.1016/j.adhoc.2025.103777
Sofiane Hamrioui , Jaime Lloret , Pascal Lorenz
The rapid expansion of connected devices has ushered in the Internet of Everything (IoE), enabling seamless integration among machines, people, and systems across diverse applications. However, the IoE faces significant challenges in ensuring efficient, reliable, and energy-conscious data transmission at scale. To address these issues, we present CLIC-IoE (Cross-Layer Solutions to Improve Communications under IoE), an innovative cross-layer framework designed to significantly enhance communication performance within IoE environments. By intelligently coordinating multiple communication layers, CLIC-IoE achieves remarkable results: a 39.47% reduction in data errors, a 38.33% increase in delivery rates, and a decrease of 0.8 nanoseconds in end-to-end delays. Additionally, it optimizes energy consumption, demonstrating a 51.67% improvement in energy efficiency (CEA) and a 20% boost in Active Things Rate (ATR). These advancements position CLIC-IoE as a transformative solution that enhances the scalability and reliability of IoE systems while promoting sustainable energy use. This manuscript provides a comprehensive exploration of the CLIC-IoE architecture, algorithms, and performance evaluation, emphasizing its potential impact on future IoE deployments. By addressing the critical challenges faced in IoE environments, CLIC-IoE not only enhances communication performance but also paves the way for more sustainable and efficient IoT systems.
{"title":"CLIC-IoE — Cross Layers Solution to Improve Communications under IoE","authors":"Sofiane Hamrioui , Jaime Lloret , Pascal Lorenz","doi":"10.1016/j.adhoc.2025.103777","DOIUrl":"10.1016/j.adhoc.2025.103777","url":null,"abstract":"<div><div>The rapid expansion of connected devices has ushered in the Internet of Everything (IoE), enabling seamless integration among machines, people, and systems across diverse applications. However, the IoE faces significant challenges in ensuring efficient, reliable, and energy-conscious data transmission at scale. To address these issues, we present CLIC-IoE (Cross-Layer Solutions to Improve Communications under IoE), an innovative cross-layer framework designed to significantly enhance communication performance within IoE environments. By intelligently coordinating multiple communication layers, CLIC-IoE achieves remarkable results: a 39.47% reduction in data errors, a 38.33% increase in delivery rates, and a decrease of 0.8 nanoseconds in end-to-end delays. Additionally, it optimizes energy consumption, demonstrating a 51.67% improvement in energy efficiency (CEA) and a 20% boost in Active Things Rate (ATR). These advancements position CLIC-IoE as a transformative solution that enhances the scalability and reliability of IoE systems while promoting sustainable energy use. This manuscript provides a comprehensive exploration of the CLIC-IoE architecture, algorithms, and performance evaluation, emphasizing its potential impact on future IoE deployments. By addressing the critical challenges faced in IoE environments, CLIC-IoE not only enhances communication performance but also paves the way for more sustainable and efficient IoT systems.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103777"},"PeriodicalIF":4.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133831","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 : 2025-01-27DOI: 10.1016/j.adhoc.2025.103770
Ahmed Bensaoud, Jugal Kalita
The rapid expansion of Internet of Things (IoT) devices has significantly increased the potential for cyber-attacks, making effective detection methods crucial for securing IoT networks. This paper presents a novel approach for detecting cyber-attacks in IoT environments by combining Self-Organizing Maps (SOMs), Deep Belief Networks (DBNs), and Autoencoders. These techniques are employed to create a system capable of identifying both known and previously unseen attack patterns. A comprehensive experimental framework is established to evaluate the methodology using both simulated and real-world traffic data. The models are fine-tuned using Particle Swarm Optimization (PSO) to achieve optimal performance. The system’s effectiveness is assessed using standard cybersecurity metrics, with results showing an accuracy of up to 99.99% and Matthews Correlation Coefficient (MCC) values exceeding 99.50%. Experiments conducted on three well-established datasets NSL-KDD, UNSW-NB15, and CICIoT2023 demonstrate the model’s strong performance in detecting various attack types. These findings suggest that the proposed approach can significantly enhance the security of IoT systems by accurately identifying emerging threats and adapting to evolving attack strategies.
{"title":"Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models","authors":"Ahmed Bensaoud, Jugal Kalita","doi":"10.1016/j.adhoc.2025.103770","DOIUrl":"10.1016/j.adhoc.2025.103770","url":null,"abstract":"<div><div>The rapid expansion of Internet of Things (IoT) devices has significantly increased the potential for cyber-attacks, making effective detection methods crucial for securing IoT networks. This paper presents a novel approach for detecting cyber-attacks in IoT environments by combining Self-Organizing Maps (SOMs), Deep Belief Networks (DBNs), and Autoencoders. These techniques are employed to create a system capable of identifying both known and previously unseen attack patterns. A comprehensive experimental framework is established to evaluate the methodology using both simulated and real-world traffic data. The models are fine-tuned using Particle Swarm Optimization (PSO) to achieve optimal performance. The system’s effectiveness is assessed using standard cybersecurity metrics, with results showing an accuracy of up to 99.99% and Matthews Correlation Coefficient (MCC) values exceeding 99.50%. Experiments conducted on three well-established datasets NSL-KDD, UNSW-NB15, and CICIoT2023 demonstrate the model’s strong performance in detecting various attack types. These findings suggest that the proposed approach can significantly enhance the security of IoT systems by accurately identifying emerging threats and adapting to evolving attack strategies.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103770"},"PeriodicalIF":4.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133729","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}
The large-scale advancement of beyond-fifth-generation (B5G) wireless networks cannot be achieved without addressing the unprecedented requirements of IoT networks, such as massive connectivity, spectrum efficiency, and energy efficiency. Accordingly, integrating non-orthogonal multiple access (NOMA) with cognitive radio (CR) has been identified as a potential solution for B5G due to its ability to support massive number of IoT devices while improving the spectrum utilization. In particular, CR networks (CRNs) permit spectrum sharing by allowing a set of secondary users to under-utilize the available spectrum without interfering with primary users (i.e., licensed users), which improves spectral efficiency. Furthermore, unlike orthogonal multiple access (OMA), NOMA can serve more than one user at each orthogonal resource block (i.e., time or frequency) through power-domain multiplexing, which supports the massive connectivity requirements of B5G networks. Incorporating intelligent-reflecting surfaces (IRS) into NOMA-enabled CRNs can improve coverage, data rates, and power efficiency, especially when CR users lack direct line-of-sight to base stations. However, this IRS-assisted NOMA CRN system cannot be fully exploited without an efficient power-allocation framework that reduces power consumption while adhering to IRS, CR, NOMA, and quality of service (QoS) constraints. This paper introduces an IRS-assisted OMA-NOMA power allocation framework for CRNs that utilizes time and frequency domains with NOMA and IRS to serve more CR users with minimal power by optimizing power allocation and IRS reflection coefficients. The proposed framework dynamically divides every idle channel into time slots, creating adaptive frequency–time resource blocks (RBs) to accommodate more users using power-domain NOMA. The power-minimization problem over these adaptive RBs, considering IRS, CR, NOMA, and QoS constraints, is formulated as a non-convex optimization problem. An iterative approach is applied to convert the problem into a solvable convex optimization. Simulation results demonstrate that the proposed framework significantly outperforms traditional IRS-based approaches across multiple metrics.
{"title":"Adaptive power optimization in IRS-assisted hybrid OFDMA-NOMA cognitive radio networks with dynamic TDMA slot allocation","authors":"Haythem Bany Salameh , Haitham Al-Obiedollah , Yaser Jararweh , Waffa abu Eid , Sharief Abdel-Razeq","doi":"10.1016/j.adhoc.2025.103778","DOIUrl":"10.1016/j.adhoc.2025.103778","url":null,"abstract":"<div><div>The large-scale advancement of beyond-fifth-generation (B5G) wireless networks cannot be achieved without addressing the unprecedented requirements of IoT networks, such as massive connectivity, spectrum efficiency, and energy efficiency. Accordingly, integrating non-orthogonal multiple access (NOMA) with cognitive radio (CR) has been identified as a potential solution for B5G due to its ability to support massive number of IoT devices while improving the spectrum utilization. In particular, CR networks (CRNs) permit spectrum sharing by allowing a set of secondary users to under-utilize the available spectrum without interfering with primary users (i.e., licensed users), which improves spectral efficiency. Furthermore, unlike orthogonal multiple access (OMA), NOMA can serve more than one user at each orthogonal resource block (i.e., time or frequency) through power-domain multiplexing, which supports the massive connectivity requirements of B5G networks. Incorporating intelligent-reflecting surfaces (IRS) into NOMA-enabled CRNs can improve coverage, data rates, and power efficiency, especially when CR users lack direct line-of-sight to base stations. However, this IRS-assisted NOMA CRN system cannot be fully exploited without an efficient power-allocation framework that reduces power consumption while adhering to IRS, CR, NOMA, and quality of service (QoS) constraints. This paper introduces an IRS-assisted OMA-NOMA power allocation framework for CRNs that utilizes time and frequency domains with NOMA and IRS to serve more CR users with minimal power by optimizing power allocation and IRS reflection coefficients. The proposed framework dynamically divides every idle channel into time slots, creating adaptive frequency–time resource blocks (RBs) to accommodate more users using power-domain NOMA. The power-minimization problem over these adaptive RBs, considering IRS, CR, NOMA, and QoS constraints, is formulated as a non-convex optimization problem. An iterative approach is applied to convert the problem into a solvable convex optimization. Simulation results demonstrate that the proposed framework significantly outperforms traditional IRS-based approaches across multiple metrics.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103778"},"PeriodicalIF":4.4,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133748","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 : 2025-01-26DOI: 10.1016/j.adhoc.2025.103783
Yang Zhang , Lei Sun , Zhangchao Ma , Jianquan Wang , Meixia Fu , Jinoo Joung
As the Industrial Internet of Things (IIoT) evolves, the rapid growth of connected devices in industrial networks generates massive amounts of data. These transmissions impose stringent requirements on network communications, including reliable bounded latency and high throughput. To address these challenges, the integration of the fifth-generation (5G) mobile cellular networks and Time-Sensitive Networking (TSN) has emerged as a prominent solution for scheduling diverse traffic flows. While Deep Reinforcement Learning (DRL) algorithms have been widely employed to tackle scheduling issues within the 5G-TSN architecture, existing approaches often neglect throughput optimization in multi-user scenarios and the impact of Channel Quality Indicators (CQI) on resource allocation. To overcome these limitations, this study introduces ME-DDPG, a novel joint resource scheduling algorithm. ME-DDPG extends the Deep Deterministic Policy Gradient (DDPG) model by embedding a Modulation and Coding Scheme (MCS)-based priority scheme. This improvement in computational efficiency is critical for real-time scheduling in IIoT environments. Specifically, ME-DDPG provides latency guarantees for time-triggered applications, ensures throughput for video applications, and maximizes overall system throughput across 5 G and TSN domains. Simulation results demonstrate that the proposed ME-DDPG achieves 100 % latency reliability for time-triggered flows and improves system throughput by 10.84 % over existing algorithms under varying Gate Control List (GCL) configurations and user ratios. Furthermore, due to the combination of MCS-based resource allocation scheme with DDPG model, the proposed ME-DDPG achieves faster convergence speed of the reward function compared to the original DDPG method.
{"title":"A 5G-TSN joint resource scheduling algorithm based on optimized deep reinforcement learning model for industrial networks","authors":"Yang Zhang , Lei Sun , Zhangchao Ma , Jianquan Wang , Meixia Fu , Jinoo Joung","doi":"10.1016/j.adhoc.2025.103783","DOIUrl":"10.1016/j.adhoc.2025.103783","url":null,"abstract":"<div><div>As the Industrial Internet of Things (IIoT) evolves, the rapid growth of connected devices in industrial networks generates massive amounts of data. These transmissions impose stringent requirements on network communications, including reliable bounded latency and high throughput. To address these challenges, the integration of the fifth-generation (5G) mobile cellular networks and Time-Sensitive Networking (TSN) has emerged as a prominent solution for scheduling diverse traffic flows. While Deep Reinforcement Learning (DRL) algorithms have been widely employed to tackle scheduling issues within the 5G-TSN architecture, existing approaches often neglect throughput optimization in multi-user scenarios and the impact of Channel Quality Indicators (CQI) on resource allocation. To overcome these limitations, this study introduces ME-DDPG, a novel joint resource scheduling algorithm. ME-DDPG extends the Deep Deterministic Policy Gradient (DDPG) model by embedding a Modulation and Coding Scheme (MCS)-based priority scheme. This improvement in computational efficiency is critical for real-time scheduling in IIoT environments. Specifically, ME-DDPG provides latency guarantees for time-triggered applications, ensures throughput for video applications, and maximizes overall system throughput across 5 G and TSN domains. Simulation results demonstrate that the proposed ME-DDPG achieves 100 % latency reliability for time-triggered flows and improves system throughput by 10.84 % over existing algorithms under varying Gate Control List (GCL) configurations and user ratios. Furthermore, due to the combination of MCS-based resource allocation scheme with DDPG model, the proposed ME-DDPG achieves faster convergence speed of the reward function compared to the original DDPG method.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103783"},"PeriodicalIF":4.4,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-26DOI: 10.1016/j.adhoc.2025.103765
Jaspreet Kaur, Kang Tan, Arslan Shafique, Olaoluwa R. Popoola, Muhammad A. Imran, Qammer H. Abbasi, Hasan T. Abbas
This study presents a machine learning (ML)-based localization method for improving location estimation accuracy in wireless networks, especially in challenging environments where traditional techniques often fall short. Conventional methods rely on a limited number of multipath components (MPCs), leading to inaccurate localization in complex environments. By leveraging a novel dataset generated from ray-tracing simulations in urban and campus environments, we propose a deep neural network (DNN)-based method that incorporates rich channel metrics such as angle of arrival (AoA), time of arrival (ToA), and received signal strength (RSS). The DNN is trained on diverse scenarios, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, and outperforms traditional MPC-based methods, reducing localization error by up to 20%. Our approach challenges the conventional use of only 3 MPCs for localization and demonstrates that a larger number of MPCs enhances accuracy, particularly in urban and obstructed environments. This research provides important insights into the potential of ML-driven solutions for improving localization accuracy in next-generation wireless systems, such as 5G and beyond.
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