Ad Hoc Networks最新文献

{"title":"CLIC-IoE — Cross Layers Solution to Improve Communications under IoE","authors":"Sofiane Hamrioui ,&nbsp;Jaime Lloret ,&nbsp;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}

{"title":"Optimized detection of cyber-attacks on IoT networks via hybrid deep learning models","authors":"Ahmed Bensaoud,&nbsp;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}

{"title":"Adaptive power optimization in IRS-assisted hybrid OFDMA-NOMA cognitive radio networks with dynamic TDMA slot allocation","authors":"Haythem Bany Salameh ,&nbsp;Haitham Al-Obiedollah ,&nbsp;Yaser Jararweh ,&nbsp;Waffa abu Eid ,&nbsp;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}

{"title":"A 5G-TSN joint resource scheduling algorithm based on optimized deep reinforcement learning model for industrial networks","authors":"Yang Zhang ,&nbsp;Lei Sun ,&nbsp;Zhangchao Ma ,&nbsp;Jianquan Wang ,&nbsp;Meixia Fu ,&nbsp;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}

{"title":"Location estimation for supporting adaptive beamforming","authors":"Jaspreet Kaur,&nbsp;Kang Tan,&nbsp;Arslan Shafique,&nbsp;Olaoluwa R. Popoola,&nbsp;Muhammad A. Imran,&nbsp;Qammer H. Abbasi,&nbsp;Hasan T. Abbas","doi":"10.1016/j.adhoc.2025.103765","DOIUrl":"10.1016/j.adhoc.2025.103765","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103765"},"PeriodicalIF":4.4,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133749","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}

{"title":"Predicting diverse QoS metrics in IoT: An adaptive deep learning cross-layer approach for performance balancing","authors":"Yassin Eljakani ,&nbsp;Abdellah Boulouz ,&nbsp;Craig Thomson","doi":"10.1016/j.adhoc.2025.103769","DOIUrl":"10.1016/j.adhoc.2025.103769","url":null,"abstract":"<div><div>Wireless sensor networks (WSNs) present dynamic challenges in various environments, often requiring careful balance between conflicting Quality of Service (QoS) metrics to optimize stack parameters and enhance network performance. This paper introduces a novel approach that incorporates proposed trade-off parameters at the application layer to model the interplay between multiple QoS metrics, including Packet Delivery Ratio (PDR), signal-to-noise ratio (SNR), Maximum Goodput (MGP), and Energy Consumption (EC). Our approach utilizes a multi-layer perceptron (MLP) model optimized using a custom Bayesian algorithm. The model employs a dynamic loss function called Weighted Error Squared (WES). It adapts dynamically to QoS statistical distributions through a scaling hyperparameter, enabling it to uncover intricate patterns specific to IEEE 802.15.4 networks. Empirical results from testing our model against a public dataset are compelling; we significantly improved prediction accuracy compared to baseline models, with R-squared values of 97%, 99%, 98%, and 93% for SNR, PDR, MGP, and EC, respectively. These results demonstrate the effectiveness of our model in predicting network behavior. Additionally, this paper presents a conceptual operational design for implementing the model in diverse real-world scenarios, suggesting avenues for future practical applications. To the best of our knowledge, this is the first design of such an integrated approach in WSNs, making our model an adaptable solution for network designers aiming to achieve optimal configurations.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103769"},"PeriodicalIF":4.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133745","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}

{"title":"Preface of Special Issue on Performance Evaluation of Wireless Ad-Hoc and Ubiquitous Networks","authors":"Mónica Aguilar Igartua,&nbsp;Luis J. de la Cruz Llopis,&nbsp;Thomas Begin","doi":"10.1016/j.adhoc.2025.103782","DOIUrl":"10.1016/j.adhoc.2025.103782","url":null,"abstract":"","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"173 ","pages":"Article 103782"},"PeriodicalIF":4.4,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748466","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}

{"title":"Regularized constrained total least squares localization for underwater acoustic sensor networks using angle-delay-doppler measurements","authors":"Feng Qiu,&nbsp;Dongsheng Guo","doi":"10.1016/j.adhoc.2025.103768","DOIUrl":"10.1016/j.adhoc.2025.103768","url":null,"abstract":"<div><div>This paper addresses the challenge of underwater acoustic localization using measurements of angle, time delay and Doppler shift. Poor sensor placement can cause numerical instability in the coefficient matrix, which diminishes localization accuracy. To tackle this, we propose a two-stage localization approach based on the regularized constrained total least squares (RCTLS) method. First, we linearize the time delay and Doppler shift equations using azimuth and elevation angles, and apply a weighted least squares (WLS) method for an initial position estimation. Second, to mitigate the impact of ill-conditioned equations and measurement errors, we employ the RCTLS method for a more robust estimation, thus reducing localization error. Due to the unique characteristics of the underwater communications, the presence of errors in the sound speed and sensor position and velocity, as well as the sensor motion effect during the observation period are considered. We also derive the hybrid Cramer Rao lower bound (CRLB) as a benchmark to evaluate estimation performance. Simulations demonstrate that our method significantly improves localization accuracy compared to conventional approaches.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103768"},"PeriodicalIF":4.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133751","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}

{"title":"Detection and Mitigation of Clock Deviation in the Verification & Validation of Drone-aided Lifting Operations","authors":"Abdelhakim Baouya ,&nbsp;Brahim Hamid ,&nbsp;Otmane Ait Mohamed ,&nbsp;Saddek Bensalem","doi":"10.1016/j.adhoc.2024.103745","DOIUrl":"10.1016/j.adhoc.2024.103745","url":null,"abstract":"<div><div>Modern Cyber–Physical systems rely on diverse computation logic, communication protocols, and technologies and are susceptible to environmental phenomena and production errors that can significantly impact system behavior. The resilience of these systems necessitates considering factors during the high-level design stages to enable accurate functional forecasting and correctness. This paper presents an approach that models clock deviation’s effects within physical and environmental conditions to perform verification &amp; validation in the context of Unmanned Aerial Vehicle domain (UAV). We employ the OMNeT++ simulation framework to define the system’s behavior in a components–port–connectors fashion. The approach leverages Probabilistic Decision Tree rules derived from the OMNeT++ simulation chart. The resulting rule-based model is then interpreted in the PRISM language for automated model verification. To validate our approach, we investigate how clock deviations influence the correctness of drone-aided lifting operations which is our primary focus, serving as a representative application scenario. The research examines clock deviations from multiple sources, including conformance to standard specifications, product manufacturing variations, operational failures, humidity, and operating temperature changes. Our examination explores the potential of validation through simulation and model checking while also studying the approach’s effectiveness through a sensitive analysis. Furthermore, the approach is demonstrated in the context of robot orchestration and water dam infrastructure for generalization purposes in Cyber–Physical Systems modeling. The research highlights the approach’s effectiveness by demonstrating its applicability, including those that incorporate degradation factors.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103745"},"PeriodicalIF":4.4,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133746","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}

{"title":"Computational Offloading and Resource Allocation for IoT applications using Decision Tree based Reinforcement Learning","authors":"Guneet Kaur Walia,&nbsp;Mohit Kumar","doi":"10.1016/j.adhoc.2024.103751","DOIUrl":"10.1016/j.adhoc.2024.103751","url":null,"abstract":"<div><div>The pervasive penetration of IoT devices in various domains such as autonomous vehicles, supply chain management, video surveillance, healthcare, industrial automation etc. necessitates for advanced computing paradigms to achieve real time response delivery. Edge computing offers prompt service response via its competent decentralized platform for catering disseminate workload, hence serving as front-runner for competently handling a wide spectrum of IoT applications. However, optimal distribution of workload in the form of incoming tasks to appropriate destinations remains a challenging issue due to multiple factors such as dynamic offloading decision, optimal resource allocation, heterogeneity of devices, unbalanced workload etc in collaborative Cloud-Edge layered architecture. Employing advanced Artificial Intelligence (AI)-based techniques, provides promising solutions to address the complex task assignment problem. However, existing solutions encounter significant challenges, including prolonged convergence time, extended learning periods for agents and inability to adapt to a stochastic environment. Hence, our work aims to design a unified framework for performing computational offloading and resource allocation in diverse IoT applications using Decision Tree Empowered Reinforcement Learning (DTRL) technique. The proposed work formulates the optimization problem for offloading decisions at runtime and allocates the optimal resources for incoming tasks to improve the Quality-of-Service parameters (QoS). The computational results conducted over a simulation environment proved that the proposed approach has the high convergence ability, exploration and exploitation capability and outperforms the existing state-of-the-art approaches in terms of delay, energy consumption, waiting time, task acceptance ratio and service cost.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"170 ","pages":"Article 103751"},"PeriodicalIF":4.4,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133752","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}