On Coverage of Critical Nodes in UAV-Assisted Emergency Networks

Waheed, Maham; Ahmad, Rizwan; Ahmed, Waqas; Alam, Muhammad Mahtab; Magarini, Maurizio

doi:10.3390/s23031586

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…In [44], a cluster-based node mechanism is used which uses the k-value selection method, and UAV trajectories with minimum distance and total flight time are proposed. For an emergency scenario, optimization of UAV placement and trajectory planning for critical nodes is studied in [45]. Based on the capacity and Age of Information, two optimization problems are formulated and the RL technique is used to work out the optimal UAV placement.…”

Section: A Related Workmentioning

confidence: 99%

A DRL Strategy for Optimal Resource Allocation Along With 3D Trajectory Dynamics in UAV-MEC Network

et al. 2023

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Advances in Unmanned Air Vehicle (UAV) technology have paved a way for numerous configurations and applications in communication systems. However, UAV dynamics play an important role in determining its effective use. In this article, while considering UAV dynamics, we evaluate the performance of a UAV equipped with a Mobile-Edge Computing (MEC) server that provides services to End-user Devices (EuDs). The EuDs due to their limited energy resources offload a portion of their computational task to nearby MEC-based UAV. To this end, we jointly optimize the computational cost and 3D UAV placement along with resource allocation subject to the network, communication, and environment constraints. A Deep Reinforcement Learning (DRL) technique based on a continuous action space approach, namely Deep Deterministic Policy Gradient (DDPG) is utilized. By exploiting DDPG, we propose an optimization strategy to obtain an optimal offloading policy in the presence of UAV dynamics, which is not considered in earlier studies. The proposed strategy can be classified into three cases namely; training through an ideal scenario, training through error dynamics, and training through extreme values. We compared the performance of these individual cases based on cost percentage and concluded that case II (training through error dynamics) achieves minimum cost i.e., 37.75 %, whereas case I and case III settles at 67.25% and 67.50% respectively. Numerical simulations are performed, and extensive results are obtained which shows that the advanced DDPG based algorithm along with error dynamic protocol is able to converge to near optimum. To validate the efficacy of the proposed algorithm, a comparison with state-of-the-art Deep Q-Network (DQN) is carried out, which shows that our algorithm has significant improvements.

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Section: A Related Workmentioning

confidence: 99%

A DRL Strategy for Optimal Resource Allocation Along With 3D Trajectory Dynamics in UAV-MEC Network

et al. 2023

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“…The optimization problem is aimed at finding the minimum mission time for a complete round of data collection, which is formulated as a coordinated traveling salesman problem with battery constraints and is solved by utilizing a heuristic algorithm. The authors of [21] study the UAV placement problem for critical nodes in emergency networks. Two different optimization problems are formulated based on the nature of node criticality, namely, capacity maximization and age of information minimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Data Collection in UAV‐Assisted Cluster‐Based Wireless Sensor Networks for 3D Environment: Optimization Study

et al. 2023

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Unmanned aerial vehicles (UAVs) have been recently employed in combination with wireless sensor networks (WSNs) to collect data efficiently and improve surveillance effectiveness. This integration enhances the WSN infrastructure where UAVs are used as aerial base stations from which to access wireless sensors in hard-to-reach places within surveillance area. Consequently, the UAVs have become a promising solution to maintain reliability for the communication between wireless sensors and base station particularly in cases where infrastructure becomes unavailable such as hilly terrains and emergencies. However, UAVs encounter many challenges which mainly focus on their lifespan and efficient placement that improves the coverage and data collection. In this paper, a novel optimization study is presented to improve the lifespan of UAV-assisted cluster-based WSNs deployed in 3D environment. This optimization study is based on two algorithms: (1) Particle Swarm Optimization (PSO) which is employed to address the clustering problem in the WSN and (2) Genetic Algorithm (GA) which is employed to locate an efficient UAV placement to maximize the lifetime. The UAV-WSN system is evaluated by considering two metrics: lifetime and throughput. The simulation results show that varying UAV altitude has significant impact on both lifetime and throughput especially in the presence of different terrain. With increasing altitude, lifetime and throughput decrease as this loss can be as high as 94%. However, the proposed optimization plays a major role in combating these losses by redirecting the UAV to efficient placement corresponding to the new altitude level to maintain maximum lifetime and throughput. Moreover, the system lifetime concerning efficient UAV placement outperforms the one concerning centered placement at lower altitude, while the difference between two cases becomes less at higher altitude. Thereby, these outcomes may provide interesting measures for designing such integrated systems to achieve efficient data collection.

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“…However, during the investigation and performance analysis, the authors did not consider the significance of the 3GPP Release-10 and Release-11 ICIC techniques for the AG-HetNet deployments. Similar extensive studies to evaluate UAV-based ad hoc PSC have been conducted in [21][22][23][24]. The wireless network model under investigation focused on optimizing limited aspects such as coverage, interference analysis, resource optimization, and user scheduling using suitable heuristic approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Improvement Using ICIC for UAV-Assisted Public Safety Networks with Clustered Users during Emergency

Kumbhar

2023

Telecom

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The application of drones, also known as unmanned aerial vehicles deployed as unmanned aerial base stations (UABSs), has received extensive interest for public safety communications (PSC) to fill the coverage gaps and establish ubiquitous connectivity. In this article, we design a PSC LTE-Advanced air–ground-based HetNet (AG-HetNet) that is a scenario representation of a geographical area during and after a disaster. As part of the AG-HetNet infrastructure, we have UABSs and ground user equipment (GUE) flocking together in clusters at safe places or evacuation shelters. AG-HetNet uses cell range expansion (CRE), intercell interference coordination (ICIC), and 3D beamforming techniques to ensure ubiquitous connectivity. Through system-level simulations and using a brute-force technique, we evaluate the performance of the AG-HetNet in terms of fifth-percentile spectral efficiency (5pSE) and coverage probability. We compare system-wide 5pSE and coverage probability when UABSs are deployed on a hexagonal grid and for different clustering distributions of GUEs. The results show that reduced power subframes (FeICIC) defined in 3GPP Release-11 can provide practical gains in 5pSE and coverage probability than the 3GPP Release-10 with almost blank subframes (eICIC).

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On Coverage of Critical Nodes in UAV-Assisted Emergency Networks

Cited by 10 publications

References 32 publications

A DRL Strategy for Optimal Resource Allocation Along With 3D Trajectory Dynamics in UAV-MEC Network

A DRL Strategy for Optimal Resource Allocation Along With 3D Trajectory Dynamics in UAV-MEC Network

Efficient Data Collection in UAV‐Assisted Cluster‐Based Wireless Sensor Networks for 3D Environment: Optimization Study

Performance Improvement Using ICIC for UAV-Assisted Public Safety Networks with Clustered Users during Emergency

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