Efficient Deployment of Small Cell Base Stations Mounted on Unmanned Aerial Vehicles for the Internet of Things Infrastructure

Sobouti, Mohammad Javad; Rahimi, Zahra; Mohajerzadeh, Amir Hossein; Seno, Seyed Amin Hosseini; Ghanbari, Reza; Marquez-Barja, Johann M.; Ahmadi, Hamed

doi:10.1109/jsen.2020.2973320

Cited by 35 publications

(24 citation statements)

References 27 publications

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“…To compare the model presented in this paper with our previous work [7], we need to generalize the method presented in [7] to 3D positioning. For this purpose, we must provide the set of candidate points for the model presented in [7] from 3D space. We use a 3D expansion of the candidate points obtained from one of the methods mentioned in Section V as the set of candidate points.…”

Section: D P-medianmentioning

confidence: 99%

“…Since the proper deployment of UAVs increases the reliability of air-to-ground links and offers better coverage for users, their optimal placement has been studied in a number of works. In [7], for instance, we proposed an efficient 2D positioning algorithm for multiple UAVs covering many Internet of Things (IoT) nodes. We determined the minimum number of required UAVs by using a bisection algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…As the mathematical model required candidate points to determine the proper position of the UAVs, we proposed a smart mesh approach, which yielded better results than a simple mesh or on-user approach. The literature in 2D positioning is further discussed in [7], [8].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient 3-D Positioning Approach to Minimize Required UAVs for IoT Network Coverage

Rahimi

Sobouti

Ghanbari

et al. 2022

IEEE Internet Things J.

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Using Unmanned Aerial Vehicles (UAVs) to cover users in wireless networks has increased in recent years. Deploying UAVs in appropriate positions is important to cover users and nodes properly. In this paper, we propose an efficient approach to determine the minimum number of required UAVs and their optimal positions. To this end, we use an iterative algorithm that updates the number of required UAVs at each iteration. To determine the optimal position for the UAVs, we present a mathematical model and solve it accurately after linearizing. One of the inputs of the mathematical model is a set of candidate points for UAV deployments in 2D space. The mathematical model selects a set of points among candidate points and determines the altitude of each UAV. To provide a suitable set of candidate points, we also propose a candidate point selection method: the MergeCells method. The simulation results show that the proposed approach performs better than the 3D P-median approach introduced in the literature. We also compare different candidate point selection approaches, and we show that the MergeCells method outperforms other methods in terms of the number of UAVs, user data rates, and simulation time.

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Section: D P-medianmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient 3-D Positioning Approach to Minimize Required UAVs for IoT Network Coverage

Rahimi

Sobouti

Ghanbari

et al. 2022

IEEE Internet Things J.

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“…Unlike traditional relays that are fixed at specific locations, UAVs can change their positions in order to provide a large coverage region [3]. Moreover, using UAVs has recently emerged as an idea to respond to high localized traffic demands in next generation cellular networks [4].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Energy Harvesting UAV Selection

Ho-Van

Do-Dac

2021

Wireless Communications and Mobile Computing

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In an UAV- (unmanned aerial vehicle-) aided relaying system, the transmitted signal is exposed to free space in two transmission hops which may be overheard by eavesdroppers. Accordingly, physical layer security should be exploited to improve information security. This paper analyzes both (security and reliability) performance aspects of such a system where only one UAV among multiple UAVs, all capable of harvesting energy from radio frequency signals, is adopted. Towards this end, the tight approximated and exact closed-form expressions of the outage probability at the legitimate destination and the intercept probability at the eavesdropper are first derived. Then, Monte-Carlo simulations are conducted to verify the derived expressions. Based on these expressions, the protected zone of the selected UAV is also proposed through an exhaustive search. Finally, various results are provided to illustrate the impact of key operation parameters on the system performance and the efficacy of the UAV selection.

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“…Internet of things (IoT) is a research hotspot under the trend of smart living in the future, and it is also an important part of the Internet in the future. At present, the Internet of things has been successfully applied in many fields such as transportation logistics, medical care, intelligent environment [1]. As the market for IoT applications becomes more and more expansive, the number of devices will connect to the network in the future will increase by tens of billions over the next five to ten years [2] [3].…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence Application to UAV Aided IoT Data Acquisition Deployment Optimization

Chen

Mohamed

et al. 2020

IEEE Access

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It is feasible and safe to use unmanned aerial vehicle (UAV) as the data collection platform of the Internet of things (IoT). In order to save the energy loss of the platform and make the UAV perform the collection work effectively, it is necessary to optimize the deployment of UAV. The objective problem is to minimize the sum of the lost energy of UAV and the loss of data transmission of Internet of things devices. The key to solving the problem is to calculate the location of the docking points and the number of docking points when the UAV is working to collect data. This paper proposes a coding scheme based on swarm intelligence optimization, which encapsulates the docking position of UAV into a dimension, so the number of docking points to be calculated is the dimension number of optimization objective. This problem is considered as a dynamic dimension optimization problem. Each individual in swarm intelligence algorithm is a solution. When adjusting the dimension, the best individual is added or deleted to achieve dynamic search in the evolutionary process. Collaborative search among multiple individuals can improve the local optimal limit of search to a certain extent. Finally, the validity of the swarm intelligence-based coding approach is verified by simulation experiments under seven different IoT device distribution scenarios. INDEX TERMS UAV deployment optimization, Internet of things data collection, Swarm intelligence, Dynamic dimension optimization.

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Efficient Deployment of Small Cell Base Stations Mounted on Unmanned Aerial Vehicles for the Internet of Things Infrastructure

Cited by 35 publications

References 27 publications

An Efficient 3-D Positioning Approach to Minimize Required UAVs for IoT Network Coverage

An Efficient 3-D Positioning Approach to Minimize Required UAVs for IoT Network Coverage

Performance Analysis of Energy Harvesting UAV Selection

Swarm Intelligence Application to UAV Aided IoT Data Acquisition Deployment Optimization

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