Deep Reinforcement Learning for Trustworthy and Time-Varying Connection Scheduling in a Coupled UAV-Based Femtocaching Architecture

Hajiakhondi-Meybodi, Zohreh; Mohammadi, Arash; Abouei, Jamshid

doi:10.1109/access.2021.3060323

Cited by 20 publications

(6 citation statements)

References 48 publications

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“…It is worth mentioning that the wireless channel is modeled based on the UAV 3GPP standard [27], [28]. In this model, the power gain of the channel between user u η and η th UAV at time t is calculated by [29]:…”

Section: System Model and Problem Descriptionmentioning

confidence: 99%

Distillation and Ordinary Federated Learning Actor-Critic Algorithms in Heterogeneous UAV-Aided Networks

2023

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In recent years, there has been growing enthusiasm for employing Unmanned Aerial Vehicles (UAVs) as an innovative technology with significant potential for the next generation of wireless networks. Hence, the Quality of Service (QoS) of clients within the Internet of Things (IoT) infrastructure can be considerably enhanced by integrating UAVs into wireless systems. However, maximizing QoS and ensuring user information security poses significant challenges in determining the trajectory of UAVs, as they need to account for the mobility and density of users. This article addresses this issue and considers a heterogeneous UAV-assisted network in which UAVs as Base Stations (BSs) have the role of finding the best trajectory based on the movement and density of Ground Users (GUs) in a way that the enhancement of the security of the covered users' information and QoS is guaranteed. To achieve these objectives, we first introduce the Actor-Critic (AC) scheme for the optimal trajectory of UAVs due to the use of a continuous environment with a large number of states and actions. In addition, the accuracy and learning speed of the agent in AC algorithms is higher than in other Reinforcement Learning (RL) schemes. Then, the combination of ordinary Federated Learning (FL) and Distillation Federated Learning (DFL) algorithms with AC is presented to increase the security of users' data, the learning speed of UAVs, and improve the QoS of GUs. In the FL scheme, access to the principal information of users is limited, but it cannot prevent reverse engineering techniques to attain users' data. To intercept such incidents, we use the DFL scheme. Due to the dynamic nature of weight updating in the neural network, this method is associated with a low hacking probability. Simulation results demonstrate that utilizing FL-and DFL-AC algorithms can significantly enhance the learning speed of the proposed schemes, while increasing the downlink rate of users by approximately 1.1% and 1.5%, respectively, compared to the AC method.INDEX TERMS Distillation federated learning (DFL), deep reinforcement learning (DRL), unmanned aerial vehicle (UAV), Actor-Critic (AC), multi-agent algorithm, UAVs' trajectory.

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Section: System Model and Problem Descriptionmentioning

confidence: 99%

Distillation and Ordinary Federated Learning Actor-Critic Algorithms in Heterogeneous UAV-Aided Networks

2023

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“…To avoid frequent handover in terrestrial infrastructure and UAV's signal attenuation in indoor areas, indoor requests are managed by FAPs, while outdoor users' requests are handled through FAPs/UAVs, depending on their movement speed (i.e., Low-Speed Users (LSU) are served through FAPs, otherwise, they are managed by UAVs) [33]. Due to the high mobility of users, the location of UAVs is determined using the K-means clustering algorithm [9]. Each UAV covers one intra-cluster and hovers at its location while delivering a request [34].…”

Section: System Model and Problem Descriptionmentioning

confidence: 99%

“…Recent advancements in heterogeneous cluster-centric cellular networks [8] have drawn focused research attention given provided considerable improvements in content diversity, which is due to the integration of the coded/uncoded content placement and Coordinated Multi-Point (CoMP) technology. Besides, integrating Unmanned Aerial Vehicles (UAVs) as flying caching nodes into the cluster-centric MEC networks [9] can further improve the network's Quality of Service (QoS) due to the high-quality Line of Sight (LoS) links, high mobility of UAVs, and their wide transmission range.…”

Section: Introductionmentioning

confidence: 99%

Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks

Hajiakhondi-Meybodi¹,

Mohammadi²,

Hou³

et al. 2022

Preprint

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“…Autonomous intrusion detection collects large data sets from drone networks with several attacks for protecting drone networks. The ML approaches involve a decision tree, naive Bayes, deep learning multi‐layer perceptron, k‐nearest neighbors, and support vector machine for classifying the attack and detecting the accuracy of a drone network 18–20 …”

Section: Introductionmentioning

confidence: 99%

“…The ML approaches involve a decision tree, naive Bayes, deep learning multi-layer perceptron, k-nearest neighbors, and support vector machine for classifying the attack and detecting the accuracy of a drone network. [18][19][20]…”

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confidence: 99%

SVM‐SFL based malicious UAV detection in wireless sensor networks

Prasad,

Khilar

2024

Concurrency and Computation

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SummaryIn the modern era, unmanned aerial vehicle (UAV) based wireless sensor networks (WSN) are rising technologies in wireless communication. Through UAV, the sensed data can be forwarded to the base station. However, the increase in network users leads to several malicious attacks on UAVs. Hence, it affects the performance of a WSN platform while transmitting private information through UAVs. Therefore, the proposed study intends to develop an effective malicious UAV detection approach using a machine‐learning algorithm. Initially, the deployed sensor nodes in WSN are utilized to collect the environmental data. These sensor nodes transmit the collected data to the UAV. During data transmission, the sensor nodes generate a feed packet (authentication parameter) and forward it to the UAV along with the sensed information. The feedback packet is encrypted through a proxy re‐encryption scheme to secure the input data. These encrypted packets with the sensed input data are then transmitted to the base station. Finally, the feedback packet is decrypted and attains the actual input information. From the received data, the classification is performed using a proposed support vector machine with a shuffled frog leap (SVM‐SFL) approach. The proposed approach is implemented with the NS3 Python tool, and the results are analyzed by evaluating several performance matrices. Compared with other existing methods, the proposed study obtained improved results in terms of accuracy (98.61%), precision (98.5%), sensitivity (98.63%), and F‐measure (98.62%).

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Deep Reinforcement Learning for Trustworthy and Time-Varying Connection Scheduling in a Coupled UAV-Based Femtocaching Architecture

Cited by 20 publications

References 48 publications

Distillation and Ordinary Federated Learning Actor-Critic Algorithms in Heterogeneous UAV-Aided Networks

Distillation and Ordinary Federated Learning Actor-Critic Algorithms in Heterogeneous UAV-Aided Networks

Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks

SVM‐SFL based malicious UAV detection in wireless sensor networks

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