UAV Networks Surveillance Implementing an Effective Load-Aware Multipath Routing Protocol (ELAMRP)
- URL: http://arxiv.org/abs/2407.09531v1
- Date: Tue, 25 Jun 2024 12:12:54 GMT
- Title: UAV Networks Surveillance Implementing an Effective Load-Aware Multipath Routing Protocol (ELAMRP)
- Authors: Raja Vavekanand, Kira Sam, Vijay Singh,
- Abstract summary: This work uses innovative multi-channel load-sensing techniques to deploy unmanned aerial vehicles (UAVs) for surveillance.
The research aims to improve the quality of data transmission methods and improve the efficiency and reliability of surveillance systems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work uses innovative multi-channel load-sensing techniques to deploy unmanned aerial vehicles (UAVs) for surveillance. The research aims to improve the quality of data transmission methods and improve the efficiency and reliability of surveillance systems by exploiting the mobility and adaptability of UAVs does the proposed protocol intelligently distribute network traffic across multiple channels, considering the load of each channel, While addressing challenges such as load balancing, this study investigates the effectiveness of the protocol by simulations or practical tests on The expected results have improved UAV-based surveillance systems, more flexible and efficient networks for applications such as security, emergency response and the environment alignment of monitoring -Offering infrastructures, which contribute to efficient and reliable monitoring solutions.
Related papers
- Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning [59.660724802286865]
Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications.
This work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users.
arXiv Detail & Related papers (2025-02-09T09:15:47Z) - A swarm algorithm for collaborative traffic in vehicular networks [2.6273514225715435]
We propose a swarm intelligence based distributed congestion control strategy to maintain the channel usage level under the threshold of network malfunction.
An exhaustive experimentation shows that the proposed strategy improves the throughput of the network, the channel usage, and the stability of the communications.
arXiv Detail & Related papers (2025-01-17T07:42:11Z) - Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms [4.387337528923525]
This paper introduces a novel Multi-Agent Reinforcement Learning (MARL) framework to enhance integrated sensing and communication networks.
By framing the positioning and trajectory optimization of UAVs as a Partially Observable Markov Decision Process, we develop a MARL approach.
We implement a decentralized cooperative MARL strategy to enable UAVs to develop effective communication protocols.
arXiv Detail & Related papers (2025-01-11T06:57:52Z) - UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks [71.23793087286703]
Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications.
This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems.
arXiv Detail & Related papers (2024-12-09T12:56:50Z) - Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework [80.39138462246034]
We propose the cooperative cognitive dynamic system (CCDS) to optimize the management for UAV swarms.
CCDS is a hierarchical and cooperative control structure that enables real-time data processing and decision.
In addition, CCDS can be integrated with the biomimetic mechanism to efficiently allocate tasks for UAV swarms.
arXiv Detail & Related papers (2024-05-18T12:45:00Z) - UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs.
We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
arXiv Detail & Related papers (2024-04-11T03:19:22Z) - Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and
Energy-Efficient Mobile Access via Multi-UAV Control [13.692977942834627]
This paper addresses a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration.
The primary objective of the proposed algorithm is to establish dependable mobile access networks for cellular vehicle-to-everything (C-V2X) communication.
arXiv Detail & Related papers (2022-10-03T14:01:52Z) - Distributed CNN Inference on Resource-Constrained UAVs for Surveillance
Systems: Design and Optimization [43.9909417652678]
Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones.
Thanks to the advancements in computer vision and machine learning, UAVs are being adopted for a broad range of solutions and applications.
Deep Neural Networks (DNNs) are progressing toward deeper and complex models that prevent them from being executed on-board.
arXiv Detail & Related papers (2021-05-23T20:19:43Z) - Network-wide traffic signal control optimization using a multi-agent
deep reinforcement learning [20.385286762476436]
Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste.
This paper proposes a novel multi-agent reinforcement learning method, named KS-DDPG, to achieve optimal control by enhancing the cooperation between traffic signals.
arXiv Detail & Related papers (2021-04-20T12:53:08Z) - Power Control for a URLLC-enabled UAV system incorporated with DNN-Based
Channel Estimation [82.16169603954663]
This letter is concerned with power control for ultra-reliable low-latency communications (URLLC) enabled unmanned aerial vehicle (UAV) system incorporated with deep neural network (DNN) based channel estimation.
arXiv Detail & Related papers (2020-11-14T02:31:04Z) - 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.