Securing the Skies: An IRS-Assisted AoI-Aware Secure Multi-UAV System with Efficient Task Offloading

• URL: http://arxiv.org/abs/2404.04692v1
• Date: Sat, 6 Apr 2024 17:41:00 GMT
• Title: Securing the Skies: An IRS-Assisted AoI-Aware Secure Multi-UAV System with Efficient Task Offloading
• Authors: Poorvi Joshi, Alakesh Kalita, Mohan Gurusamy,
• Abstract summary: Our framework incorporates exponential AoI metrics and emphasizes secrecy rate to tackle eavesdropping and jamming threats. We introduce a transformer-enhanced Deep Reinforcement Learning (DRL) approach to optimize task offloading processes.
• Score: 3.427366431933441
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned Aerial Vehicles (UAVs) are integral in various sectors like agriculture, surveillance, and logistics, driven by advancements in 5G. However, existing research lacks a comprehensive approach addressing both data freshness and security concerns. In this paper, we address the intricate challenges of data freshness, and security, especially in the context of eavesdropping and jamming in modern UAV networks. Our framework incorporates exponential AoI metrics and emphasizes secrecy rate to tackle eavesdropping and jamming threats. We introduce a transformer-enhanced Deep Reinforcement Learning (DRL) approach to optimize task offloading processes. Comparative analysis with existing algorithms showcases the superiority of our scheme, indicating its promising advancements in UAV network management.

Related papers

• Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks [3.489779105594534]
  backdoors can be exploited by malicious actors on deep neural networks (DNNs) and cloud services for data processing. Our approach leverages advanced tensor decomposition algorithms to meticulously analyze the weights of pre-trained DNNs and distinguish between backdoored and clean models. This advancement enhances the security of deep learning and AI in networked systems, providing essential cybersecurity against evolving threats in emerging technologies.
  arXiv  Detail & Related papers  (2024-03-13T03:10:11Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs [21.195346908715972]
  Unmanned aerial vehicles present an alternative means to offload data traffic from terrestrial BSs. This paper presents a novel approach to efficiently serve multiple UAVs for data offloading from terrestrial BSs.
  arXiv  Detail & Related papers  (2024-02-05T12:36:08Z)
• Segmentation of Drone Collision Hazards in Airborne RADAR Point Clouds Using PointNet [0.7067443325368975]
  A critical prerequisite for the integration is equipping UAVs with enhanced situational awareness to ensure safe operations. Our study leverages radar technology for novel end-to-end semantic segmentation of aerial point clouds to simultaneously identify multiple collision hazards. To our knowledge, this is the first approach addressing simultaneous identification of multiple collision threats in an aerial setting, achieving a robust 94% accuracy.
  arXiv  Detail & Related papers  (2023-11-06T16:04:58Z)
• Traj-MAE: Masked Autoencoders for Trajectory Prediction [69.7885837428344]
  Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers. We propose an efficient masked autoencoder for trajectory prediction (Traj-MAE) that better represents the complicated behaviors of agents in the driving environment. Our experimental results in both multi-agent and single-agent settings demonstrate that Traj-MAE achieves competitive results with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-12T16:23:27Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker Verification Spoof Detection [7.219077740523682]
  Models currently deployed for the task of Automatic Speaker Verification are, at their best, devoid of suitable degrees of generalization to unseen attacks. The present study proposes the Efficient Attention Branch Network (EABN) modular architecture with a combined loss function to address the generalization problem.
  arXiv  Detail & Related papers  (2021-09-05T12:10:16Z)
• 3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning [75.78929539923749]
  Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. It is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT) In this paper, we design a new UAV-assisted IoT systems relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices.
  arXiv  Detail & Related papers  (2021-06-06T14:08:41Z)
• Efficient UAV Trajectory-Planning using Economic Reinforcement Learning [65.91405908268662]
  We introduce REPlanner, a novel reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size.
  arXiv  Detail & Related papers  (2021-03-03T20:54:19Z)
• UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach [18.266087952180733]
  We propose a new end-to-end reinforcement learning approach to UAV-enabled data collection from Internet of Things (IoT) devices. An autonomous drone is tasked with gathering data from distributed sensor nodes subject to limited flying time and obstacle avoidance. We show that our proposed network architecture enables the agent to make movement decisions for a variety of scenario parameters.
  arXiv  Detail & Related papers  (2020-07-01T15:14:16Z)
• Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach [88.45509934702913]
  We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed. We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
  arXiv  Detail & Related papers  (2020-02-21T07:29:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.