Maximizing the Lifetime of Wireless Devices in Millimeter Wave UAV Networks

Shakhatreh, Hazim; Malkawi, Waed

doi:10.13189/ujeee.2020.070402

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…By this, we have relaxed our goal to find the optimum 2D location of the UAV in order to maximize the lifetime of wireless devices. After all of these, we found that we can represent the constraint sets (6.b-6.d) by an intersection of half spheres and this intersection forms a convex set in terms of (Xu, Yu), so, we can write our problem in a form of a two-variable (Xu, Yu) optimization problem (as proved in Shakhatreh and Malkawi (2020)) and we note the resulted objective function is concave if the coverage angle θ i for all ground users in a cluster is greater than 60 o (as proved in Shakhatreh and Malkawi (2020)). Now, in case the objective function is concave, we can find the optimal location of a UAV using the gradient projection algorithm (Bertsekas and Tsitsiklis, 1989) which has a computational complexity O(M) (Jiang et al, 2014).…”

Section: Proposed Algorithmmentioning

confidence: 98%

“…The pseudo-code of the GP algorithm is shown in Fig. 3a (Shakhatreh and Malkawi, 2020) as algorithm 1. Fig.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Shakhatreh et al (2019) expanded the field of research to include multiple UAVs. Now, in Khreishah, (2018), andShakhatreh et al (2019), mm-waves have not been considered, while Shakhatreh and Malkawi (2020) found the optimal placement of a UAV, and the aim is to allocate the location for one UAV that achieves maximum sum of uplink transmissions time durations on the basis that the frequencies used are mm-waves. In this paper, we will expand the field of research to include multiple UAVs considering the same conditions in Shakhatreh and Malkawi (2020).…”

Section: Introductionmentioning

confidence: 99%

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3D placements of drones in a millimeter-wave network to maximize the lifetime of wireless devices

al.¹

2021

Int. j. adv. appl. sci.

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In the last few years, the use of drones is increasing day by day in wireless networks and the applications of them are rapidly increased on different sides. Now, we can use the drone as an aerial base station (BS) to support cellular networks in emergency cases and in natural disasters. To take the advantage of both drones and fifth-generation (5G) and link between their features, we study an aerial BS considering millimeter waves (mm-waves). In this paper, we optimize the 3D placements for multiple unmanned aerial vehicles (UAVs) in an mm-wave network to achieve maximum time durations of the uplink transmission. First, we present a formulation for the placement problem, where we aim to allocate 3D locations for multiple UAVs to achieve the maximum sum of time durations of uplink transmissions. We propose an efficient algorithm to find the placements of UAVs. We propose an algorithm that starts by grouping the wireless devices into a number of clusters, and each cluster is served by a single UAV. After the clustering process, it applies the gradient projection-based algorithm (GP) or particle swarm optimization (PSO) in each cluster. In the results section, our proposed approach and the center projection algorithm will be compared to prove the efficiency of our approach.

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Section: Proposed Algorithmmentioning

confidence: 98%

“…The pseudo-code of the GP algorithm is shown in Fig. 3a (Shakhatreh and Malkawi, 2020) as algorithm 1. Fig.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D placements of drones in a millimeter-wave network to maximize the lifetime of wireless devices

al.¹

2021

Int. j. adv. appl. sci.

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“…They present two effective methods for reducing the number of UAVs required to serve wireless devices. In [23,24], the authors investigate the problem of efficient 3D placements for a set of UAVs in a mmWave network. The objective function of the optimization problem is aimed at finding the most effective UAV deployments that maximize the total uplink transmissions' time duration of ground wireless devices in a mmWave network.…”

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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Maximizing the Lifetime of Wireless Devices in Millimeter Wave UAV Networks

Cited by 2 publications

References 14 publications

3D placements of drones in a millimeter-wave network to maximize the lifetime of wireless devices

3D placements of drones in a millimeter-wave network to maximize the lifetime of wireless devices

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

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