Ad Hoc Networks最新文献

{"title":"RELTO: A reliability-oriented DRL approach with context-aware adaptive reward weighting for multi-objective task offloading in MEC","authors":"Anam Nasir,&nbsp;Xiang He,&nbsp;Teng Wang,&nbsp;Haomai Shi,&nbsp;Zhongjie Wang","doi":"10.1016/j.adhoc.2025.104065","DOIUrl":"10.1016/j.adhoc.2025.104065","url":null,"abstract":"<div><div>Task offloading in Mobile Edge Computing (MEC) enables resource-constrained IoT devices to reduce latency and energy consumption while enhancing computational performance. However, designing effective offloading strategies presents a multi-objective optimization challenge, particularly in ensuring task reliability while optimizing energy efficiency and latency under dynamic conditions with unpredictable task failures caused by fluctuating computation demands and unstable communication links that severely degrade Quality of Service (QoS). Existing Deep Reinforcement Learning (DRL) approaches struggle to address these reliability-centered challenges, primarily due to their limited adaptability to dynamic reliability requirements, inadequate hybrid action space management, and insufficient handling of complex system state representations. To address these limitations, this work formulates a reliability-aware task offloading problem that explicitly integrates communication and computation reliability with latency and energy consumption into a multi-objective optimization formulation. To solve this optimization problem, the proposed Reliability Energy Latency Task Offloading (RELTO) algorithm employs Proximal Policy Optimization (PPO) within hybrid action spaces and incorporates a context-aware adaptive reward weighting mechanism driven by dual-attention architecture. The mechanism dynamically adjusts objective prioritization particularly emphasizing reliability based on real-time conditions, while attention-based state representation enables proactive decision-making through temporal pattern recognition. Extensive experiments in simulated MEC environments demonstrate that RELTO achieves optimal trade-offs across the key performance metrics, providing a more adaptive and robust solution for multi-objective task offloading.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104065"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419849","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":"Location-based early detection and prevention of DDoS attacks in mMTC networks","authors":"Zeinab Rezaeifar ,&nbsp;Zahra Alavikia ,&nbsp;Changhee Hahn","doi":"10.1016/j.adhoc.2025.104090","DOIUrl":"10.1016/j.adhoc.2025.104090","url":null,"abstract":"<div><div>The Random Access (RA) procedure of the current 3GPP cellular network has been adopted for small data packet transmissions by massive Machine Type Communication Devices (MTCDs). However, the initial steps of the RA procedure lack an authentication mechanism, making it susceptible to Distributed Denial of Service (DDoS) attacks, particularly in massive access scenarios. In these cases, attackers can hide among a large number of legitimate stationary devices with limited processing capabilities, such as installed sensors or smart meters. To address this issue, this paper proposes an early DDoS attack detection and prevention method that leverages the Timing Advance (TA) information from stationary MTCDs. The proposed method detects the approximate location of malicious devices sending consecutive preamble codes and blocks them by withholding Resource Blocks (RBs) during the RA procedure. Numerical results from a simulated Physical Random Access Channel (PRACH) for Machine Type Communications (MTC), considering noise and multipath effects, demonstrate the effectiveness of the proposed method in detecting and mitigating DDoS attacks. Under intense attack scenarios, the proposed method effectively identifies network attackers while reducing RA delay and RB consumption for MTCDs by approximately 50% compared to the baseline. This improvement enhances overall network performance and sustainability.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104090"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468553","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":"TOP: A forward and reverse offloading strategy in MEC-enabled Cooperative Vehicle–Infrastructure System","authors":"Dun Cao ,&nbsp;Weijia Xiao ,&nbsp;Dan Cai ,&nbsp;Yifan Yang ,&nbsp;Fayez Alqahtani ,&nbsp;Jin Wang","doi":"10.1016/j.adhoc.2025.104058","DOIUrl":"10.1016/j.adhoc.2025.104058","url":null,"abstract":"<div><div>Enabled by Mobile Edge Computing (MEC) equipped on Base Station (BS), Collaborative Vehicle–Infrastructure Systems (CVIS) can provide efficient and reliable computing services for mobile vehicles. Vehicles can achieve intelligent applications such as autonomous driving by forward offloading tasks to base stations. However, most existing studies focus on the BS merely as a task receiver and integrator in CVIS, and neglecting its role as a task generator for information processing. When the BS is overloaded, CVIS will deteriorate drastically. Reverse offloading from BS to idle vehicles can relieve the pressure. Nonetheless, with the huge volume of tasks generated by both some task vehicles and the BS, how to select appropriate offloading and resource allocation strategies is a challenge. The situation will become more complex when facing heterogeneous nodes, i.e., task vehicles, the BS, and the idle vehicles in the range of the task vehicle or in the range of the BS but out of the task vehicle in dynamic scenarios. Thus, we propose an optimization problem joint multiple task offloading and resource partitioning to maximize the average task satisfaction of the system. To address the above proposed optimization problem, we propose Two-way Offloading &amp; Partitioning (TOP) strategy, where a Two-way Collaborative Edge Node Dividing and Offloading Algorithm determines the cooperative edge nodes for different tasks and obtains the offloading strategy for each task set. Furthermore, we optimize the resource partitioning using the Genetic Algorithm to avoid resource wastage while enhancing the overall satisfaction of the system. Extensive experimental results show that our proposed TOP strategy improves average system satisfaction by up to 33% compared to other baseline strategies.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104058"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468469","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":"CNN-MHBiGRU: A two-stage deep learning framework with multi-attention mechanisms for IoT intrusion detection","authors":"Yufeng Zhang ,&nbsp;Yulong Wang ,&nbsp;Liting Gao","doi":"10.1016/j.adhoc.2025.104082","DOIUrl":"10.1016/j.adhoc.2025.104082","url":null,"abstract":"<div><div>The growing complexity of cyberattacks in the Internet of Things (IoT) highlights the need for intrusion detection systems that are both accurate and efficient. We present CNN-MHBiGRU, a two-stage framework that integrates enhanced convolutional feature extraction with multi-head recurrent temporal modeling. The CNN stage employs residual blocks, SE attention, SoftPool, and attention pooling to obtain multi-scale spatial representations, while the second stage uses a multi-head bidirectional GRU with intra-head attention to capture diverse temporal dependencies. Data preprocessing follows a leakage-free protocol in which standardization and SMOTE are applied only to the training set, combined with Focal Loss to improve minority-class recognition. Experiments on four public benchmarks (NF-BoT-IoT v1/v2 and NF-ToN-IoT v1/v2) demonstrate that CNN-MHBiGRU consistently surpasses recent methods. On NF-BoT-IoT-v2 (binary), the model achieves an F1-score of 0.9997, while on NF-ToN-IoT-v2 (binary) it reaches 0.9907, both exceeding strong graph-based baselines. For multiclass tasks, weighted recall and F1-scores of 0.9901/0.9901 and 0.9601/0.9595 are obtained on the two v2 datasets. Temporal–spatial efficiency evaluations further show a compact footprint of fewer than 300K parameters and <span><math><mrow><mo>&lt;</mo><mn>1</mn><mo>.</mo><mn>2</mn></mrow></math></span> MB memory, with only moderate inference overhead. Unlike prior CNN–GRU or graph-based IDSs, CNN-MHBiGRU achieves state-of-the-art detection performance while maintaining this lightweight design, offering a distinctive accuracy–efficiency trade-off suitable for realistic IoT-edge deployment.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104082"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468467","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":"ADMZ: Adaptive Dynamic Mix Zone pseudonym change strategy for location privacy in Vehicular Ad-hoc Networks","authors":"Liyan Wang,&nbsp;Yingxu Lai,&nbsp;Congai Zeng","doi":"10.1016/j.adhoc.2025.104066","DOIUrl":"10.1016/j.adhoc.2025.104066","url":null,"abstract":"<div><div>Vehicular Ad-Hoc Networks (VANETs) enable efficient traffic coordination through real-time broadcasting of Basic Safety Messages (BSMs), yet they also introduce significant location privacy concerns. Existing dynamic mix zone strategies trigger pseudonym changes based on vehicle density awareness. However, their reliance on single-time-slot verification often results in residual ”ghost vehicles” and false mix zone activation, which not only wastes pseudonym resources but also compromises privacy protection. To overcome these limitations, we propose ADMZ, an Adaptive Dynamic Mix Zone pseudonym change strategy. ADMZ includes a time slot-based pseudonym cleanup mechanism carried out by Roadside Units (RSUs) to periodically remove expired pseudonyms. It also incorporates a dynamic boundary adjustment method based on trajectory prediction region overlap to counter semantic linking attacks, as well as a density-distance weighted priority model for mix zone selection. Simulation results show that ADMZ significantly improves location privacy protection, reducing the false trigger rate from 40.5% to 17.6% and reducing the adversary’s trajectory tracking success rate to 14.3% in scenarios with 300 vehicles. Furthermore, ADMZ reduces pseudonym consumption and achieves a better balance between privacy preservation and system stability.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104066"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468470","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":"Bi-functional glucose Sensing-Transmission (Sens-Tra) sensor for IoT-based applications","authors":"Swati Todi,&nbsp;Shankul Patel,&nbsp;Poonam Agarwal","doi":"10.1016/j.adhoc.2025.104086","DOIUrl":"10.1016/j.adhoc.2025.104086","url":null,"abstract":"<div><div>In the era of the Internet of Things (IoT) Ecosystem, multi-functional components have the potential to meet the needs of the exponential rise in the number of sensors/devices. Hence, in the future, the feasible complexity of the systems in IoT could be maintained using multi-functional components. This research aims to lower the hardware complexity at the edge devices in IoT architecture by incorporating both the sensing and data transmission capabilities into a single device, thereby eliminating the necessity of an external transmission device. Here, a state-of-the-art technique is developed, where a real-time, label-free, contact-based bi-functional Sens-Tra (Sensing-Transmission) operates as a sensor for glucose detection in aqueous solution as well as wireless transmission. To materialize this, a Circular Interdigital Capacitor Sensor (CIDCS) fed by a coplanar waveguide transmission line is proposed. The sensor operates over the Wi-Fi network, integrating leading-edge wireless technology with contact-based glucose sensing. The Wi-Fi network is established through ESP8266 Wi-Fi modules, and the Received Signal Strength Indicator (RSSI) is utilized as the sensing parameter for glucose detection. In addition to the sensing purpose, the bi-functional sensor is demonstrated for IoT-based applications, where RSSI is wirelessly transmitted from Sens-Tra to the cloud server, <em>ThingSpeak</em>, through mobile data. The data from the cloud can be processed and utilized through any web/mobile application, providing desired flexibility to the system. The experimental findings exhibit the potential of the bi-functional CIDCS for sensing as well as data transmission.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104086"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468468","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":"Transfer function-guided mixed-variable optimization for joint mining decisions and resource allocation in mobile edge computing-integrated blockchain networks","authors":"Mohamed Abdel-Basset ,&nbsp;Reda Mohamed ,&nbsp;Karam M. Sallam ,&nbsp;Saber Elsayed","doi":"10.1016/j.adhoc.2025.104088","DOIUrl":"10.1016/j.adhoc.2025.104088","url":null,"abstract":"<div><div>Recently, mobile edge computing (MEC) technology has been integrated with wireless blockchain networks to improve the computational capabilities of Internet of Things devices during the mining process. Jointly, optimizing miner selection (discrete) and resource allocation (continuous) in MEC-integrated blockchain networks is a challenging mixed-variable, NP-hard problem. Although several algorithms have been presented in the literature to solve it, they still suffer from low-quality results due to either slow convergence speed, local optima stagnation, or both, especially for small or medium problem sizes. To address this, we propose a transfer-function-guided encoding (TFE) framework that introduces a principled link between continuous metaheuristic search and discrete miner-operator control. Specifically, each individual maintains one discrete control variable determining insertion, deletion, or replacement of a miner and two continuous controls representing transmission power and computing resource allocation. Continuous metaheuristic outputs are converted to discrete decisions through families of S-shaped and V-shaped transfer functions, providing tunable exploration–exploitation balance and probabilistic control over operator selection. This mechanism is integrated with several state-of-the-art algorithms. Extensive experiments on MEC-blockchain networks with <span><math><mrow><mi>m</mi><mspace></mspace><mo>∈</mo><mspace></mspace><mrow><mo>[</mo><mn>50</mn><mo>,</mo><mn>1000</mn><mo>]</mo></mrow></mrow></math></span> miners demonstrate that TFE consistently accelerates convergence and improves system profit for small–medium scales, with HNOA-TFE achieving the best overall performance. The numerical results show that the hybrid nutcracker optimization algorithm with the TFE mechanism is effective across most problem instances. Also, the comparative study with recent MEC/blockchain resource-allocation and vehicular-edge benchmarks shows the robustness and scalability of the proposed method.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104088"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468556","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 blockchain-integrated PUF framework for secure authentication and communication","authors":"Koustav Kumar Mondal ,&nbsp;Debasis Das ,&nbsp;Arpit Khandelwal","doi":"10.1016/j.adhoc.2025.104059","DOIUrl":"10.1016/j.adhoc.2025.104059","url":null,"abstract":"<div><div>Prior work on Internet-of-Things (IoT) security often splits between hardware roots of trust and decentralized key management: Physically Unclonable Function (PUF) schemes frequently depend on centralized helper infrastructures, whereas blockchain systems typically lack a hardware seed. We present a unified, protocol-enforced framework that integrates PUFs, blockchain-backed Shamir’s Secret Sharing (SSS), and elliptic-curve cryptography (ECC) to remove this gap. Concretely: (i) a Static Monostable PUF with error correction derives device keys without storage, achieving <span><math><mrow><mo>&gt;</mo><mn>95</mn><mtext>%</mtext></mrow></math></span> reconstruction success at <span><math><mrow><mo>≈</mo><mn>10</mn><mtext>%</mtext></mrow></math></span> noise; (ii) SSS shares are posted on-chain but re-wrapped every epoch under fresh IND-CPA encryption, eliminating ciphertext staleness and bounding ledger-scraping advantage; and (iii) ECC (Elliptic Curve Cryptography) – including ephemeral Elliptic Curve Diffie–Hellman (ECDH) for forward secrecy and Elliptic Curve Digital Signature Algorithm (ECDSA) for authentication – must rotate each epoch from high-min-entropy PUF material. Our analysis proves hardware-anchored uniqueness, information-theoretic threshold secrecy, and forward secrecy under bounded leakage with non-compounding attacker advantage. We also derive a unified impersonation bound that composes the PUF/ML front-end with the ECC back-end (acceptance probability <span><math><mrow><mo>≤</mo><mi>α</mi><mo>+</mo><msub><mrow><mi>α</mi></mrow><mrow><mi>ML</mi></mrow></msub><mo>+</mo><mi>negl</mi><mrow><mo>(</mo><mi>κ</mi><mo>)</mo></mrow></mrow></math></span>). A duty-cycle/connectivity model shows constant device-resident state (independent of <span><math><mrow><mo>(</mo><mi>t</mi><mo>,</mo><mi>n</mi><mo>)</mo></mrow></math></span>), microjoule–millijoule energy per epoch, and graceful PUF-only operation during outages with automatic re-incorporation of on-chain shares upon reconnection. Containerized experiments across domain-specific deployments – secure supply-chain identification, critical-infrastructure control, and healthcare telemetry – demonstrate sub-second end-to-end handshakes under load, with SSS and ECC costs scaling linearly in <span><math><mi>t</mi></math></span> and <span><math><mrow><mi>Θ</mi><mrow><mo>(</mo><mo>log</mo><mi>p</mi><mo>)</mo></mrow></mrow></math></span>, respectively. These results indicate that enforced rotation plus on-chain ephemerality yields an efficient, scalable, and formally validated tamper-resistant root of trust for next-generation IoT networks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104059"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145365861","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":"Optimizing energy-efficient routing in Mobile Internet of Things (MIoT) networks using Grey Wolf Optimization and Recurrent Neural Networks","authors":"Seyed Salar Sefati ,&nbsp;Sanda Osiceanu Maiduc ,&nbsp;Bahman Arasteh ,&nbsp;Winfred Ofoe Larkotey ,&nbsp;Asgarali Bouyer ,&nbsp;Wali Ullah Khan","doi":"10.1016/j.adhoc.2025.104047","DOIUrl":"10.1016/j.adhoc.2025.104047","url":null,"abstract":"<div><div>The Mobile Internet of Things (MIoT) represents a significant evolution of traditional IoT by enabling seamless connectivity for mobile devices and sensors in dynamic environments. Given the resource constraints and mobility challenges in MIoT networks, developing adaptive and energy-efficient routing strategies is important. This paper proposes a novel routing protocol that integrates Grey Wolf Optimization (GWO) and Recurrent Neural Networks (RNNs) to enhance energy efficiency, reliability, and responsiveness in MIoT systems. The protocol features dynamic clustering, predictive traffic load balancing, and multi-objective optimization for Cluster Head (CH) selection, where RNNs forecast traffic trends and GWO optimizes routing paths. Simulation results demonstrate that the proposed method reduces energy consumption, lowers end-to-end delays, and improves packet delivery ratio (PDR) and network reliability under both static and mobile conditions. Compared to existing methods such as the Krill Herd (KH) algorithm, Dynamic Multi-Sink Routing Protocol (DMS-RP), and Evolutionary Fuzzy Rule-based (EFR) models, the proposed solution exhibits superior performance, validating its scalability and effectiveness for real-world MIoT applications.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104047"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419848","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":"Authentication protocol for the Internet of Drones with fog computing based on aggregate signatures for forest inventory","authors":"Manuela de Jesus Sousa ,&nbsp;Paulo Roberto L. Gondim ,&nbsp;Sandra Sendra ,&nbsp;Jaime Lloret","doi":"10.1016/j.adhoc.2025.104034","DOIUrl":"10.1016/j.adhoc.2025.104034","url":null,"abstract":"<div><div>The Internet of Drones (IoD) has become increasingly important in applications such as forest inventory, leveraging advanced sensors and internet connectivity to enable efficient data collection. Compared to traditional methods, IoD offers superior cost-effectiveness. However, its reliance on public channels, unreliable connectivity, and dynamic environments poses significant security and privacy challenges. Safeguarding forest inventory data is essential to maintaining accuracy, preventing unauthorized access, and mitigating the risk of data manipulation, which can lead to suboptimal management decisions. To address these concerns, it is essential to design a lightweight authentication protocol that secures IoD communication, accounts for network bandwidth limitations and scalability, and supports integration with emerging technologies. This manuscript introduces a new Authentication and Key Agreement (AKA) protocol specifically designed for the Internet of Drones (IoD), leveraging asymmetric cryptography and aggregate signatures to enhance security and privacy in forest inventories with fog computing. Its robustness was confirmed through informal and formal security analyses by the AVISPA tool and the ROR model, demonstrating resistance to known attacks and superior communication, computational, and energy performance compared to existing protocols.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104034"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419844","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}