A secure and resilient UAV-assisted edge-enabled transit offloading framework based on Security-Aware Multi-Agent Proximal Policy Optimization (SA-MAGPPO) that outperforms RP, GS, RBH, SPPO, and conventional MAGPPO approaches in terms of energy, operational efficiency, offloading reliability, and robustness against communication anomalies.
Abstract
Public transit systems play an important role in reducing traffic congestion, energy consumption, and greenhouse gas emissions in smart urban transportation environments. However, large-scale transit operations still suffer from inefficient routing, scheduling, and resource management decisions under highly dynamic traffic and passenger demand conditions. Furthermore, existing intelligent transportation approaches often ignore communication unreliability, anomalous transportation observations, computation offloading overhead, and network congestion in low-altitude intelligent transportation systems. To address these challenges, this paper proposes a secure and resilient UAV-assisted edge-enabled transit offloading framework based on Security-Aware Multi-Agent Proximal Policy Optimization (SA-MAGPPO). In the proposed framework, UAVs operate as low-altitude communication assistants that enhance V2I and V2V connectivity. These UAVs provide aerial traffic observations and reduce communication congestion in dense urban transportation networks. The proposed framework jointly optimizes transit routing, charging scheduling, fleet management, and computation offloading decisions between onboard units and edge servers. Unlike conventional MAGPPO, the proposed framework augments the agent state representation with communication reliability and anomaly information and incorporates security-aware policy learning to improve robustness against unreliable traffic observations, communication disruptions, and unstable network conditions. Extensive simulations under multiple operational scenarios demonstrate that the proposed framework consistently outperforms RP, GS, RBH, SPPO, and conventional MAGPPO approaches in terms of energy, operational efficiency, offloading reliability, and robustness against communication anomalies.
The rapid advancements of next-generation vehicular networks require intelligent, low-latency, and efficient resource management to support heterogeneous services. In this work, we propose a Traffic-aware Dynamic Resource Allocation (TADRA) architecture for UAV-assisted vehicular O-RAN to address the challenges of dynamic traffic conditions, infrastructure failures, and stringent quality of service (QoS) requirements. Due to the dynamic mobility and flexible deployment characteristics, UAV Open Radio Units (O-RUs) in the TADRA architecture support the terrestrial infrastructure under overload or failure conditions, dynamically extending coverage, balancing traffic loads, and restoring service to maintain uninterrupted QoS across diverse and heterogeneous traffic demands. Unlike existing static or single-layer solutions, our proposed TADRA integrates RAN Intelligent Controllers (RICs) with a Hierarchical Traffic-Aware Multi-Agent Twin-Delayed (TMT) algorithm to optimize the allocation of computation and radio resources. This joint optimization problem is NP-hard, highly dynamic, and coupled across agents, making TMT a tractable and adaptive alternative. This hierarchical framework performs traffic prioritization at the upper (application) layer and resource allocation at the lower (MAC) layer, facilitating adaptive decision-making under diverse vehicular traffic patterns. Numerical results demonstrate that our solution provides substantial gains over MATD3, MADDPG, and GA, achieving 17% lower latency, 10% higher throughput, 14% lower energy consumption, and 6.5% higher reliability.
Hayla Nahom Abishu, Ahmed Badawy, Amr Mohamed et al.· IEEE Transactions on Network...· 0 citations
The results confirm that the integration of artificial intelligence, energy-aware routing, and UAV trajectory optimization provides an effective and scalable solution for next-generation UAV-assisted IoT systems and establishes a robust foundation for intelligent 6G-enabled wireless sensor networks.
Mojtaba Nasehi· Internet of Things and Cloud...· 0 citations
This study proposes an intelligent Q-learning-enhanced Evolutionary Game Theory (QEGT) routing mechanism for USNs that leverages game-theoretic incentives and Q-learning to adaptively select strategies.
Anita Murmu, Saurabh Kumar Srivastava, Nuthan Chingeetham et al.· IEEE Open Journal of the Com...· 0 citations
Vehicular consumer electronics increasingly support computation-intensive and latency-sensitive services, imposing stringent efficiency, reliability, and security requirements on vehicular edge computing (VEC) systems. In dynamic vehicular environments, inference-based information leakage and anomalous communication behaviors further threaten system performance and data privacy. To address these challenges, this paper proposes a UAV-assisted cooperative VEC architecture that integrates threat-aware task offloading with intelligent spatiotemporal caching across roadside units (RSUs) and UAV edge nodes. A security-aware uplink transmission model is developed to capture potential information leakage risks and abnormal communication patterns, enabling adaptive offloading decisions. We formulate a joint optimization problem to minimize end-to-end task execution delay while improving cache utilization under limited computing and storage resources. To efficiently solve this problem, a Threat-Aware Joint Optimization (TAGO) framework is designed by combining proximal policy optimization for adaptive task offloading and a gradient-based caching update derived from the Frank-Wolfe algorithm to capture spatiotemporal service popularity. Simulation results demonstrate that the proposed approach significantly reduces task delay and improves cache efficiency compared with several baseline strategies, showing its effectiveness for secure and efficient UAV-assisted vehicular consumer electronics systems.
Vehicular Ad Hoc Networks (VANETs) face resource constraints, high node mobility, and stringent latency requirements, especially in safety-critical applications such as collision avoidance, path planning, and emergency braking. Task offloading to nearby vehicles or Roadside Units (RSUs) mitigates local computational limits, but dynamic conditions, unreliable nodes, and rapid topology changes complicate dependable node selection. This paper proposes a Tiny Machine Learning (TinyML)-enhanced, credibility-based task offloading framework for real-time decision-making in vehicular networks. RSUs evaluate vehicle reliability through a three-component Credibility Assessment Module: a Task Assignment Component that distributes lightweight test tasks and filters unreliable nodes via TinyML inference; a Verification Component that applies TinyML anomaly detection to validate execution with minimal cloud dependence; and a Scoring Component that updates credibility using an exponentially weighted delay-penalty model. A hybrid approach then integrates Random Forest (RF) for credibility-aware node selection with TinyLSTM for link stability prediction, while vehicles pre-screen candidates locally by mobility and resource availability before querying RSUs, reducing signalling overhead. Simulations in OMNeT++, Veins, and SUMO using a Luxembourg City mobility trace show a 24% higher task success rate, 9% lower completion time, 21% lower packet drop ratio, and 65% fewer disconnection-induced failures over heuristic, MLP, and deep-RL baselines, at only 9–18% RSU-side CPU overhead. The RF (380 KB) and TinyLSTM (48 KB post-quantization) models fit automotive-grade microcontroller budgets, confirming practical deployability for next-generation Intelligent Transportation Systems (ITS).
Muhammad Ali, Tariq Qayyum, A. Tariq et al.· IEEE Open Journal of the Com...· 0 citations
This article proposes a new approach to routing, termed RACER (Real-time Adaptive Congestion-aware Emergency Routing), which dynamically responds to changing traffic conditions without requiring additional traffic-signal-control infrastructure, relying instead on congestion information obtained through standard vehicle-to-infrastructure telemetry.
Harinath Ankarboina, Jasmini Kumari, Amit Kumar Singh et al.· IEEE Open Journal of the Com...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
