2016
DOI: 10.1016/j.icte.2016.08.005
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Flying path optimization in UAV-assisted IoT sensor networks

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Cited by 44 publications
(24 citation statements)
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“…Algorithms that consider the battery and energy consumption of UAVs as a constraint have also been studied in the literature. For instance, some works consider a limited availability of energy [112]- [114], and a limited flight time [115] in developing path planning algorithms.…”
Section: Reducing Mechanical Energymentioning
confidence: 99%
“…Algorithms that consider the battery and energy consumption of UAVs as a constraint have also been studied in the literature. For instance, some works consider a limited availability of energy [112]- [114], and a limited flight time [115] in developing path planning algorithms.…”
Section: Reducing Mechanical Energymentioning
confidence: 99%
“…When a security guard, for example, notices suspicious behavior, he/she commands the UAV to take a video of the involved people and applies face recognition to verify if someone has a criminal record. The work in [31] found an optimal flying path for UAVs equipped with IoT sensors by using a location-aware multi-layer information map and utility functions based on sensor density, flight time, energy, and risk. Genetic algorithms were used to maximize these utility functions.…”
Section: Uav and Iotmentioning
confidence: 99%
“…They also consider data aggregation to route information in an energy-efficient way. [27] proposes an optimal flying path scheme for UAV-aided IoT sensor networks. They use a location aware information map with multiple layers and introduce different utility functions based on the sensor density, energy consumption, duration on fly and risk level.…”
Section: Energy Efficiencymentioning
confidence: 99%
“…There are also some studies in the literature that apply GA for aerial systems in order to effectively provide a solution against some objectives. [27] studies an optimal flying scheme in UAV assisted sensor networks and they implement GA approach to solve their multi-objective utility function against provided constraints. [32] deploys GA with ACO approach to provide a collaborative solution against their combinational optimization problem for multi-UAV flight planning with maximum surveillance.…”
Section: Energy Efficiencymentioning
confidence: 99%