Ad Hoc Networks最新文献

{"title":"Time-dependent distributed collaboration and incentive mechanism for Mobile Crowdsensing","authors":"Junjie Yan ,&nbsp;Wenli Wang ,&nbsp;Haohao Yuan ,&nbsp;Jingxian Liu ,&nbsp;Junyi Deng","doi":"10.1016/j.adhoc.2025.103979","DOIUrl":"10.1016/j.adhoc.2025.103979","url":null,"abstract":"<div><div>In Mobile Crowd Sensing (MCS), the increasing complexity of sensing tasks and the rising demand for data quality have rendered traditional single-participant sensing paradigms inadequate. Collaborative sensing involving multiple participants has emerged as a crucial approach to enhance sensing efficiency and accuracy. However, centralized collaboration strategies often impose significant computational and processing burdens on the platform, while neglecting participants’ actual capabilities and willingness to cooperate. Moreover, existing research rarely addresses the sensing time redundancy that arises when multiple participants collaborate on the same task. Additionally, most studies assume participants have long, continuous time slots available for sensing, which does not align with real-world scenarios where participants’ available time is often fragmented. To address these challenges, this paper proposes a Time-dependent Mobile Crowdsensing Distributed Group Collaboration System (TMDCS). First, we construct a task selection model that accounts for participants’ sensing capabilities and allocates different suitable tasks across their multiple fragmented time slots. We also develop a task collaboration incentive model aimed at encouraging greater participation and ensuring high-quality sensing data. Second, a distributed task optimization mechanism is designed to improve overall social welfare. This mechanism selects leaders and forms collaborative groups based on coalition game theory. Finally, a reverse auction scheme is applied to select the optimal coalition for each task and determine incentive distribution. Experimental results demonstrate that the proposed TMDCS outperforms baseline methods, achieving average improvements of 49.7% in social welfare, 37.5% in task coverage, and 25.3% in task quality.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103979"},"PeriodicalIF":4.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711857","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":"QoS-aware Network Slicing and Resource Management for Internet of Vehicles in 5G networks","authors":"Wafa Hamdi,&nbsp;Orhan Dağdeviren,&nbsp;Hasan Bulut","doi":"10.1016/j.adhoc.2025.103976","DOIUrl":"10.1016/j.adhoc.2025.103976","url":null,"abstract":"<div><div>The development of Internet of Vehicles (IoV) technologies brings with it numerous challenges, such as heterogeneous Quality of Service (QoS) in 5G and beyond (B5G). These challenges go hand in hand with spectrum scarcity, one of the biggest challenges of future wireless technologies. This will be exacerbated especially in the era of Vehicle-to-Everything (V2X) communication due to the limitation of radio resources such as channels, bandwidth, and power. This challenge is further intensified by the increasing number of vehicles. Therefore, spectrum sharing initiatives have a significant impact on traffic safety and efficiency. In response to these challenges and to meet the diverse QoS requirements of vehicular applications within 5G/B5G, new paradigms such as New Radio Vehicle-to-Everything (NR-V2X) and Network Slicing (NS) have emerged as important solutions. Network Slicing efficiently divides the physical network into slices tailored to Ultra-Reliable Low Latency Communications (URLLC), Enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC). In this paper, we investigate the IoV slicing problem with QoS support, focusing on partitioning the physical network into three different slices: URLLC, eMBB and mMTC. To ensure seamless communication in vehicular networks, our mixed method approach effectively incorporates handover mechanisms, emergency traffic prioritization, based services, and road network-specific parameters. In addition, we propose two resource allocation algorithms that enable efficient allocation of resources to vehicles and comply with the standardization principles of the 3rd Generation Partnership Project (3GPP). These algorithms aim to prioritize emergency traffic during incidents while maintaining an acceptable QoS for non-safety services in a resource-constrained environment, thus improving Key Performance Indicators (KPIs). Detailed simulations show the effectiveness of the proposed algorithms, and confirm their ability to improve the performance of emergency services in terms of end-to-end delay while ensuring acceptable reliability and throughput. Comparative evaluations further highlight the superiority of the proposed NS method over other existing approaches.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103976"},"PeriodicalIF":4.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685746","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 guard band and power control for resource allocation in mobile and fixed mission-critical IoUT networks","authors":"Walid K. Hasan,&nbsp;Iftekhar Ahmad,&nbsp;Quoc Viet Phung,&nbsp;Daryoush Habibi","doi":"10.1016/j.adhoc.2025.103978","DOIUrl":"10.1016/j.adhoc.2025.103978","url":null,"abstract":"<div><div>The Internet of Underwater Things (IoUT) is transforming underwater communication by enabling essential mission-critical applications such as precise navigation, emergency response coordination, diver safety, robust security and surveillance systems, and real-time environmental monitoring. However, Underwater Acoustic Communication (UAC), which serves as the primary communication medium for IoUT, experiences substantial challenges, including limited bandwidth availability, severe signal attenuation and Doppler-induced frequency shifts, especially pronounced in mobile underwater environments. These challenges degrade throughput and increase latency, making it difficult to meet the strict delay and reliability demands of mission-critical IoUT applications. Without adaptive solutions, real-time underwater communication remains unreliable and inefficient. This paper introduces an Adaptive Guard band and Power control resource allocation scheme for mission critical applications (AGP-MCA), specifically designed to improve underwater communication. The AGP-MCA framework optimizes the acoustic spectrum based on the criticality of IoUT applications. AGP-MCA dynamically adjusts guard bands to effectively mitigate Doppler caused by mobile nodes and strategically manages transmission power to reduce power consumption significantly, and handles non-critical data through buffering. We formulate a comprehensive mathematical optimization model and employ a Whale Optimization Algorithm (WOA)-based meta heuristic approach to achieve near-optimal solutions while ensuring minimal computational complexity. Extensive simulations demonstrate that AGP-MCA enhances throughput, reduces both end-to-end delay and power consumption, and consistently outperforms existing protocols and configurations without adaptive guard bands. Further, it offers a robust and power-efficient solution for real-time mission-critical IoUT applications.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103978"},"PeriodicalIF":4.4,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711911","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":"Multi-UAV path planning for connectivity-based sweep coverage","authors":"Survi Kumari ,&nbsp;Seshan Srirangarajan","doi":"10.1016/j.adhoc.2025.103966","DOIUrl":"10.1016/j.adhoc.2025.103966","url":null,"abstract":"<div><div>Quality of service in wireless surveillance networks relies on two key factors: area coverage and connectivity. In this work, we investigate the spatial coverage and connectivity of a wireless surveillance network comprising of unmanned aerial vehicles (UAVs) used for surveying an area of interest. In many such applications, the data collected by the UAVs must be relayed, perhaps through multiple hops, to the base station in real-time. The goal of the network, in such applications, is to improve area coverage of the network while ensuring the UAVs are connected to the base station/sink throughout their flight. To achieve this, we propose two mixed-integer linear programming (MILP)-based formulations to plan the path of a set of UAVs. In the first formulation, we maximize the area covered by the UAVs while ensuring that they maintain their connectivity to the base station. In the second formulation, we minimize the number of movements required by the UAVs to achieve a desired coverage level while maintaining connectivity to the base station. We present extensive performance evaluation of the proposed algorithms and their comparison with path-planning approaches that do not consider connectivity constraints. We explore some of the trade-offs involved in meeting the connectivity requirement.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103966"},"PeriodicalIF":4.4,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685744","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":"Interplay of ML and blockchain for secure Internet of Military Vehicles communication underlying 5G","authors":"Maulik Sojitra ,&nbsp;Nilesh Kumar Jadav ,&nbsp;Rajesh Gupta ,&nbsp;Usha Patel ,&nbsp;Janam Patel ,&nbsp;Sudeep Tanwar ,&nbsp;Giovanni Pau ,&nbsp;Fayez Alqahtani ,&nbsp;Amr Tolba","doi":"10.1016/j.adhoc.2025.103968","DOIUrl":"10.1016/j.adhoc.2025.103968","url":null,"abstract":"<div><div>Internet of Things (IoT) networks have rapidly transformed various sectors, including modern warfare, where Internet of Military Vehicles (IoMVs) enable remote connection, monitoring, and data sharing. However, IoMV sensors lack inherent security measures to combat threats such as DDoS, jamming, and spoofing. Traditional security solutions relying on AI face challenges such as inefficient feature selection, lack of transparency, and susceptibility to data tampering. In this paper, we propose an AI and Blockchain based secure data exchange architecture for battlefield IoMV networks. Our approach employs an Explainable Artificial Intelligence (XAI) technique for optimal feature selection and uses five different Machine Learning algorithms to classify malicious and non-malicious data. Notably, the XGBoost model achieves an accuracy of 98.8%. Non-malicious data is securely forwarded to a blockchain network, where a smart contract validates its legitimacy, and stored off-chain using the Inter-Planetary File System (IPFS) to enhance scalability and reduce storage costs. Additionally, leveraging low latency 5G communication ensures rapid and reliable data transmission. This integration of AI for real-time threat detection, blockchain for tamper-proof storage, and 5G for enhanced communication significantly improves battlefield operations by enabling secure and efficient decision-making.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103968"},"PeriodicalIF":4.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685745","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":"Deep reinforcement learning for optimizing computation latency in wireless-powered Multi-Access Edge Computing systems: A partial offloading approach","authors":"Bowen Huang ,&nbsp;Xiaolong Chen ,&nbsp;Jianqing Li ,&nbsp;Hongfei Guo ,&nbsp;Mohammed Atiquzzaman ,&nbsp;Jindan Zhang","doi":"10.1016/j.adhoc.2025.103971","DOIUrl":"10.1016/j.adhoc.2025.103971","url":null,"abstract":"<div><div>The integration of wireless power transfer with multi-access edge computing (MEC) is critical for next-generation wireless networks, yet the surge in users challenges ultra-low latency. This study examines a wireless-powered MEC network that employs a partial offloading strategy. The aim of this research is to devise an online algorithm that optimally manages task offloading and resource management, adapting to dynamic channel conditions. To achieve this, we design a Deep Reinforcement Online Offloading with Two-Stage Optimization (DROO-TSO) framework. This framework is aimed at predicting partial offloading ratios and optimizing charging time and resource management. Empirical results show DROO-TSO achieves sub-millisecond execution times on both GPU and CPU platforms. Compared to DDPG-based baselines, DROO-TSO reduces the total computation delay by 21.49% while adaptively converging to environment-optimized strategies. Both algorithm runtime and the total computation delay meet stringent low-latency requirements, validating its capability in dynamic wireless-powered MEC networks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103971"},"PeriodicalIF":4.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721989","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 multi-depot provisioned UAV swarm trajectory optimization scheme for collaborative data acquisition in a large-scale IoT environment","authors":"Saugata Roy ,&nbsp;Nabajyoti Mazumdar ,&nbsp;Rajendra Pamula","doi":"10.1016/j.adhoc.2025.103974","DOIUrl":"10.1016/j.adhoc.2025.103974","url":null,"abstract":"<div><div>Due to autonomous flying ability and high manoeuvrability, unmanned aerial vehicle (UAV) assisted sensory data acquisition is becoming prevalent in outdoor IoT applications. However, UAV’s limited onboard power source results in a restricted flight time, necessitating the use of a multi-UAV platform to ensure uninterrupted service in a large-scale network. Nonetheless, very few studies have considered the use of multiple UAVs from distinct depots to improve network coverage with an optimal UAV swarm. This article investigates a multi-depot, energy-constrained vehicle routing problem (MDEVRP) where a fleet of UAVs is dispatched from different depots to collect sensory data from the ground nodes, provided that UAVs never run out of energy. Our objective is to discover an optimal set of UAVs with detailed hovering and travelling plans, which is an NP-hard problem. To solve such a computationally hard problem, we first leverage the variable dimensional particle swarm optimization (VD-PSO) algorithm that jointly optimizes the number of UAVs deployed, their depots, and association with the hovering locations. Then, minimal cost UAV trajectories are established, which preserves data freshness at the UAV depots. Simulation results manifest the dominance of the proposed scheme over the related state-of-the-art protocols.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103974"},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144663394","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":"Energy consumption minimized wireless powered edge computing","authors":"Ke Wang ,&nbsp;Kaikai Chi ,&nbsp;Anwer Al-Dulaimi","doi":"10.1016/j.adhoc.2025.103973","DOIUrl":"10.1016/j.adhoc.2025.103973","url":null,"abstract":"<div><div>Most Internet of Things (IoT) devices face challenges in handling complex computational tasks due to their limited computing capabilities. To address this issue, Mobile Edge Computing (MEC) has been introduced, which significantly enhances computational efficiency and response speed by offloading tasks to the cloud or the network edge. Additionally, by integrating Wireless Power Transfer (WPT) technology, IoT devices can harvest energy wirelessly, thereby alleviating energy constraints. This paper investigates a WPT-enabled MEC network with the goal of minimizing the system’s overall energy consumption. First, we formulate the energy minimization problem as a mixed-integer nonlinear programming (MINLP) problem. Then, we propose a Deep Reinforcement Learning (DRL)-based algorithm to jointly optimize offloading decisions and time allocation. Simulation results demonstrate that the proposed approach not only converges quickly but also achieves performance comparable to that of the exhaustive search method. Furthermore, it significantly reduces energy consumption compared to baseline schemes.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103973"},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656300","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":"An efficient and reliable IoT-enabled application for electric vehicle charging stations","authors":"Arafat Miah,&nbsp;Faisal Amin,&nbsp;Fakir Sharif Hossain","doi":"10.1016/j.adhoc.2025.103970","DOIUrl":"10.1016/j.adhoc.2025.103970","url":null,"abstract":"<div><div>The Electric Vehicle (EV) offers an eco-friendly transport solution, reducing fuel reliance and greenhouse gas emissions. However, efficient and a reliable Charging Station (CS) pose challenges. To address this, a vital network combining station and EV data with secure communication is essential. Prior studies aimed for efficiency and security but often lacked efficacy and reliability. This article presents a pioneering app-based solution that forges a cost-effective, dependable connection between CS and EV users utilizing secret keys from a cross over ring oscillator and a modified AES algorithm. The proposed method lies in a novel Internet-of-Things (IoT) enabled approach that grants EV users restricted access to CSs, establishing a self-contained intra-CS connection using Power Line Communication (PLC). This new strategy not only reduces charging duration and expenses but also elevates overall service quality, all the while upholding robust security measures. By harnessing the suggested app-based framework, in conjunction with employing the Physical Unclonable Function for generating cipher keys and adopting the lightweight cryptography core for IoT sensors with the Diffe-Hellman key exchange protocol for intra-CS communication via PLC, the objective is to drive the domain of EV charging services towards a future that is both more secure and sustainable.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103970"},"PeriodicalIF":4.4,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656298","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":"Supervised machine learning-based ETX optimization for energy-efficient routing in IoT-enabled WSNs","authors":"Oussama Senouci,&nbsp;Nadjib Benaouda","doi":"10.1016/j.adhoc.2025.103972","DOIUrl":"10.1016/j.adhoc.2025.103972","url":null,"abstract":"<div><div>This paper addresses the challenge of energy-efficient and reliable data routing in Wireless Sensor Networks (WSNs) within Internet of Things (IoT) environments by optimizing the Expected Transmission Count (ETX) metric for efficient routing. Traditional ETX-based routing struggles with dynamic network conditions, leading to suboptimal path selection and increased energy consumption. To overcome these limitations, we propose a Machine Learning-Based ETX Optimization Approach, which dynamically adjusts ETX values based on real-time network conditions and historical transmission patterns. The approach employs a supervised learning model, specifically a CatBoost classifier, to predict the most energy-efficient and reliable routes. The model achieves a high classification accuracy of 98.9%, enabling precise differentiation between optimal and non-optimal links, thereby reducing retransmissions and balancing energy consumption across the network. Our approach is evaluated using extensive simulations, analyzing key performance metrics such as energy consumption, network lifespan, Packet Delivery Ratio (PDR), and communication overhead. Experimental results demonstrate that the proposed method significantly enhances routing efficiency, minimizes energy expenditure, and improves overall network performance. Specifically, our method improves network lifetime by 14.3%, energy efficiency by 16.7%, PDR by 26.4% and communication overhead by 8.06% compared to existing protocols. These results highlight the robustness and predictive power of our approach, making it a highly effective solution for integrating WSNs into IoT ecosystems while ensuring sustainable and efficient operation.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103972"},"PeriodicalIF":4.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656402","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}