Drone Charging Stations Deployment in Rural Areas for Better Wireless Coverage: Challenges and Solutions

Qin, Yujie; Kishk, Mustafa A.; Alouini, Mohamed‐Slim

doi:10.1109/iotm.001.2100083

Cited by 25 publications

(14 citation statements)

References 14 publications

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“…In this study, the authors formulated the problem of jointly optimizing UAV Trajectories and Locations of Battery swap Stations as an optimization problem and developed an optimization framework to solve the problem. Especially since finding the shortest trajectory for a single UAV visiting each region of interest and returning to the base station without even considering charging stations is essentially the traveling salesperson problem (TSP) which is NP-Hard, a heuristic solution called UBAT based on Ant Colony Optimization (ACO) is proposed to solve the problem In [28], the authors consider the approach of deploying charging stations and collaborating with public transportation vehicles. The optimal deployment problem is formulated to minimize the average delivery time for the customers, which is a reflection of customer satisfaction.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…Yujie Qin [26] [27] used tools from queuing theory and stochastic geometry to study the influence of each of the charging station's limited capacity and spatial density on the performance of a drone-enabled wireless network. Yujie Qin [28] investigated the possibility and performance enhancement of the deployment of renewable energy (RE) charging stations. The authors used simulation results to demonstrate that RE charging stations can be a possible solution to address the limited onboard battery of UAVs in rural areas.…”

Section: Introductionmentioning

confidence: 99%

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Charging Stations Distribution Optimization using Drones Fleet for Disaster Prone Areas

Hassan

Shah

Rana

2022

IJIST

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A disaster is an unforeseen calamity that causes damage to property or brings about a loss of human life. Quick response and rapid distribution of vital relief items into the affected region could save precious lives. In this regard, disaster management comes into play, which is highly dependent on the topography of the disaster-hit area. If the disaster-hit area has little or no road connectivity, the use of drones in such areas becomes essential for the delivery of health packages. Since the battery capacity of the drone is limited, there is a need of charging stations that should be transported using road infrastructure and pre-installed in disaster-prone areas, as access to these areas may be denied once the disaster hits. In this article, a simulation model was used to optimize the number and location of drone charging stations for deployment in a disaster-prone area in the pre-disaster scenario, aiming at the distribution of relief items to disaster-hit areas in the post-disaster scenario. We consider the relative priority of locations where a preference is given to the locations that have higher priority levels. An optimal number of charging stations and optimal routes have also been determined by using our optimization model. To illustrate the use of our model, numerical examples have been simulated for different sizes of the disaster-hit area and the number of targets. In our numerical simulation, it was observed that the drone's maximum distance capacity is the key factor in determining the optimal grid size, which directly correlates to the number of charging stations.

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Section: Simulations and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Charging Stations Distribution Optimization using Drones Fleet for Disaster Prone Areas

Hassan

Shah

Rana

2022

IJIST

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“…Indeed, wired communications require high expenses that cannot be justified by the limited economical revenues in rural regions. Several technologies and approaches are proposed for wireless connectivity in rural areas as: high-altitude platform systems (HAPS), satellite communications, unmanned aerial vehicle base station (UAV-BS) and super cells (SCs) (see [1]- [14] and references therein). Among them, the technology of SCs, i.e., cells with a high tower and large coverage area can be considered as the most cost-effective [1], [13], [15].…”

Section: B Wireless Connectivity and Super Cellsmentioning

confidence: 99%

Enhancement of Rural Connectivity by Recycling TV Towers with Massive MIMO Techniques

Falou¹,

Alouini²

2022

Preprint

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Nowadays, the digital divide is one of the major issues facing the global community. Around 3 billion people worldwide are still not-connected or underconnected. In this article, we investigate the use of TV towers with multi user (MU) massive multiple input multiple output (mMIMO) techniques to offer connectivity in rural areas. Specifically, the coverage range is assessed for a MU mMIMO base station (BS) mounted on a high tower as a TV tower, and compared with a legacy mMIMO BS. The obtained results show that one high tower BS can cover an area at least 25 times larger than the area covered by a legacy BS. This is of high interest as recycling TV towers can enhance the rural connectivity with low expenditures. We apply the proposed solution to a realistic case study in an Ethiopian rural area, based on population densities and locations of current BS and TV towers. Our study shows that a high number of people can be covered by existing TV towers. Additional possible solutions to enhance rural connectivity are discussed in the last section.

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“…They are expected to play an essential rule in next-generation wireless networks [5], [6]. UAVs can be very useful in both rural and urban areas [7], such as improving the quality of service [8], providing service to remote Internet of Things users [9], Yujie Qin and Mohamed-Slim Alouini are with Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia Arabia. Mustafa Kishk was with CEMSE, KAUST during this work.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Charging Infrastructure Sharing in UAV and EV-Involved Networks

Qin

Kishk

Alouini

2023

IEEE Trans. Veh. Technol.

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Electric vehicles (EVs) and unmanned aerial vehicles (UAVs) show great potential in modern transportation and communication networks, respectively. However, with growing demands for such technologies, the limited energy infrastructure becomes the bottleneck for their future growth. It might be of high cost and low energy efficiency for all the operators to each have their own dedicated energy infrastructure, such as charging stations. In this paper, we analyze a wireless charging infrastructure sharing strategy in UAV and EV-involved networks. We consider a scenario where UAVs can charge in EV charging stations and pay for the sharing fee. On the EVs' side, sharing infrastructure can earn extra profit but their service quality, such as waiting time, might slightly reduce. On the UAVs' side, if renting EV charging stations can achieve an acceptable system performance, say high coverage probability, while considering the cost, they may not need to build their dedicated charging stations. In this case, we use tools from stochastic geometry to model the locations and propose an optimization problem that captures the aforementioned trade-offs between cost or profit and quality of service. Our numerical results show that sharing infrastructure slightly increases the waiting time of EVs, say within 5 min, but dramatically decreases the waiting time of drones, say more than 50 min, and deploying more charging stations do achieve better performances, but all these better performances are expected to cost more.

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Drone Charging Stations Deployment in Rural Areas for Better Wireless Coverage: Challenges and Solutions

Cited by 25 publications

References 14 publications

Charging Stations Distribution Optimization using Drones Fleet for Disaster Prone Areas

Charging Stations Distribution Optimization using Drones Fleet for Disaster Prone Areas

Enhancement of Rural Connectivity by Recycling TV Towers with Massive MIMO Techniques

Performance Analysis of Charging Infrastructure Sharing in UAV and EV-Involved Networks

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