Power-Efficient Wireless Coverage Using Minimum Number of UAVs

Sawalmeh, Ahmad; Othman, Noor Shamsiah; Liu, Guanxiong; Khreishah, Abdallah; Alenezi, Ali; Alanazi, Abdulaziz

doi:10.3390/s22010223

Cited by 12 publications

(19 citation statements)

References 40 publications

(107 reference statements)

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“…The PSO algorithm uses a clustering approach where each particle in the swarm moves towards its best previous position, called Lbest, and the global best position called Gbest. This movement is repeated until the maximum number of iterations is reached, which is the termination condition [46]. Algorithm 1 presents the pseudocode for this approach.…”

Section: Clustering Approach Using Particle Swarmmentioning

confidence: 99%

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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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Section: Clustering Approach Using Particle Swarmmentioning

confidence: 99%

“…For constant T the algorithm complexity is OðnpÞ≃Oðn 2 Þ. Moreover, the GA and PSO algorithms' complexity has been discussed in details in [46,47].…”

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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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“…Results confirmed the superiority of the proposed algorithms in terms of fitness values compared to WWO, HS, SA, and GA methods. Sawalmeh et al [32] suggested GA-based and PSO-based clustering algorithms for solving the UAV placement issue. Both algorithms were assessed using a various number of drones and ground users.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

et al. 2023

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The concept of smart cities is to enhance the life quality of residents and provide efficient services by integrating advanced information and communication technologies, autonomous robots, Internet of Things (IoT) devices, etc. Unmanned Aerial Vehicles (UAVs) are a class of autonomous flying mobile robots that bring a lot of benefits to smart cities due to their mobility, accessibility, autonomy, and many other advantages. Their integration allows for accomplishing hard and complex tasks that humans or other entities are not able to complete. In most applications, multiple connected UAVs are required to build a network under which missions are completed and tasks are shared. This network can be established by finding the optimal UAV placement that meets some requirements such as user coverage, UAV connectivity, energy, and load distribution. In this paper, we present a hybrid algorithm, called IMRFO-TS, based on the hybridization of Improved Manta Ray Foraging Optimization (IMRFO) with the Tabu Search (TS) algorithm for solving the UAV placement problem in a smart city. First, the tangential control strategy is incorporated into the original MRFO algorithm to enhance its convergence speed and explore the search space effectively. Second, The TS algorithm is hybridized with the IMRFO algorithm to increase the exploitation capability of IMRFO and improve the best solution (placement quality) obtained after each iteration. The performance of the proposed IMRFO-TS algorithm is validated using 52 benchmarks considering the fitness value, coverage, connectivity, energy consumption, and load distribution parameters. Compared to eight well-known optimization meta-heuristics such as the original MRFO, TS algorithm, Bat Algorithm (BA), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Reptile Search Algorithm (RSA), the results of the experiments revealed the significant superiority of the proposed IMRFO-TS algorithm by obtaining promising solutions (optimal positions of UAVs) in the majority of the cases.

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“…Ahmad et al proposed the clustering algorithm and 3D UAV deployment algorithm that can be iteratively called to achieve multi-UAV deployment in the target area. The results show that this algorithm can effectively serve both outdoor and indoor UEs [21]. Jisang et al obtained the minimum height of UAV deployment according to the elliptical characteristics generated by the inclined antenna and transformed…”

Section: Introductionmentioning

confidence: 98%

Research on UAV access deployment algorithm based on improved virtual force model

2022

KSII TIIS

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In this paper, a unmanned aerial vehicle (UAV) access deployment algorithm is proposed, which is based on an improved virtual force model to solve the poor coverage quality of UAVs caused by limited number of UAVs and random mobility of users in the deployment process of UAV base station. First, the UAV-adapted Harris Hawks optimization (U-AHHO) algorithm is proposed to maximize the coverage of users in a given hotspot. Then, a virtual force improvement model based on user perception (UP-VFIM) is constructed to sense the mobile trend of mobile users. Finally, a UAV motion algorithm based on multi-virtual force sharing (U-MVFS) is proposed to improve the ability of UAVs to perceive the moving trend of user equipments (UEs). The UAV independently controls its movement and provides follow-up services for mobile UEs in the hotspot by computing the virtual force it receives over a specific period. Simulation results show that compared with the greedy-grid algorithm with different spacing, the average service rate of UEs of the U-AHHO algorithm is increased by 2.6% to 35.3% on average. Compared with the baseline scheme, using UP-VFIM and U-MVFS algorithms at the same time increases the average of 34.5% to 67.9% and 9.82% to 43.62% under different UE numbers and moving speeds, respectively.

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Power-Efficient Wireless Coverage Using Minimum Number of UAVs

Cited by 12 publications

References 40 publications

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

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

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

Research on UAV access deployment algorithm based on improved virtual force model

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