Optimal drone placement and cost-efficient target coverage

Zorbas, Dimitrios; Pugliese, Luigi Di Puglia; Razafindralambo, Tahiry; Guerriero, Francesca

doi:10.1016/j.jnca.2016.08.009

Cited by 124 publications

(90 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, the articles surveyed in Sections 4–6 propose various solution approaches to optimize operations of drones. In addition to presenting centralized optimization methods, some articles design agent‐based, or decentralized, algorithms . Such algorithms are scalable to new drones entering the system, are robust, since drones may not be connected to the control station at all times, and naturally enable parallelized computation with each drone optimizing its own local subproblem.…”

Section: Planning Drone Operationsmentioning

confidence: 99%

“…Environmental monitoring, monitoring of transportation networks or emergency response activities may require allocation of several heterogeneous drones to stationary observational positions, that is, monitoring positions where drones hover or loiter for a long time. Pugliese et al and Zorbas et al set up optimization models in which a given set of objects should be covered by the sensor range of at least one drone. Each object should be monitored for a certain amount of time, so that if the drone's energy is insufficient to cover the required time span, a new drone jumps into the surveillance position and the drone with depleted energy returns to the depot.…”

Section: Planning Drone Operationsmentioning

confidence: 99%

See 1 more Smart Citation

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult‐to‐access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

show abstract

Section: Planning Drone Operationsmentioning

confidence: 99%

Section: Planning Drone Operationsmentioning

confidence: 99%

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

et al. 2018

View full text Add to dashboard Cite

show abstract

“…For example, in target coverage, there is a tradeoff between energy consumption and coverage radius. Higher altitude means higher observation radius but higher energy consumption [108]- [110]. Optimizing the flight radius and speed to improve energy efficiency is also addressed in [111].…”

Section: Reducing Mechanical Energymentioning

confidence: 99%

Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges

Fotouhi

Qiang

Ding

et al. 2019

IEEE Commun. Surv. Tutorials

779

485

View full text Add to dashboard Cite

The rapid growth of consumer Unmanned Aerial Vehicles (UAVs) is creating promising new business opportunities for cellular operators. On the one hand, UAVs can be connected to cellular networks as new types of user equipment, therefore generating significant revenues for the operators that can guarantee their stringent service requirements. On the other hand, UAVs offer the unprecedented opportunity to realize UAVmounted flying base stations that can dynamically reposition themselves to boost coverage, spectral efficiency, and user quality of experience. Indeed, the standardization bodies are currently exploring possibilities for serving commercial UAVs with cellular networks. Industries are beginning to trial early prototypes of flying base stations or user equipments, while academia is in full swing researching mathematical and algorithmic solutions to address interesting new problems arising from flying nodes in cellular networks. In this article, we provide a comprehensive survey of all of these developments promoting smooth integration of UAVs into cellular networks. Specifically, we survey (i) the types of consumer UAVs currently available off-the-shelf, (ii) the interference issues and potential solutions addressed by standardization bodies for serving aerial users with the existing terrestrial base stations, (iii) the challenges and opportunities for assisting cellular communications with UAV-based flying relays and base stations, (iv) the ongoing prototyping and test bed activities, (v) the new regulations being developed to manage the commercial use of UAVs, and (vi) the cyber-physical security of UAV-assisted cellular communications.

show abstract

“…where α is a motor speed multiplier, β is the minimum power needed to hover just over the ground (when altitude is almost zero), z means the height at time step t, and s is the speed of drone and P max is the maximum power of motor to flight. Therefore, the term P max ( z s ) refers to the power consumption needed to lift to height h with speed v [37], [38]. In this experiment, we set the values of α to 5.5, β to 15, z to 1m, and P max to 45.…”

Section: B Overall Auction Mechanismmentioning

confidence: 99%

Auction-Based Charging Scheduling With Deep Learning Framework for Multi-Drone Networks

Shin

Kim

Levorato

2019

IEEE Trans. Veh. Technol.

116

View full text Add to dashboard Cite

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auctionbased mechanism to control the charging schedule in multidrone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-priceauction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep learningbased approach provides effective battery charging control in multi-drone scenarios.

show abstract

Optimal drone placement and cost-efficient target coverage

Cited by 124 publications

References 40 publications

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges

Auction-Based Charging Scheduling With Deep Learning Framework for Multi-Drone Networks

Contact Info

Product

Resources

About