Energy-Efficient 3D Navigation of a Solar-Powered UAV for Secure Communication in the Presence of Eavesdroppers and No-Fly Zones

Huang, Hailong; Savkin, Andrey V.; Ni, Wei

doi:10.3390/en13061445

Cited by 24 publications

(30 citation statements)

References 30 publications

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“…Moreover, new path and mission planning strategies for solar powered UAVs are being continuously developed, intended to (1) improve their trajectory by deriving the most energy-efficient flight patterns [ 25 , 26 , 27 , 28 , 29 ], to (2) determine the optimal path for improving operational efficiency in missions meant for communications [ 30 , 31 , 32 ], or (3) to track different types of targets [ 33 ]. The planning strategies exploited in these works are based on different types of optimization approaches, ranging from nonlinear optimization strategies [ 26 , 28 , 29 , 30 , 31 ] to rapidly-exploring random trees [ 32 ], the grasshopper optimization algorithm [ 33 ] and particle swarm optimization [ 27 ]. Nevertheless, the planners presented in these works either (1) optimize the trajectories without considering any aspect that is relevant to the mission or (2) tackle missions which are significantly different from the one proposed in this paper (and hence they consider a different set of requirements and constraints).…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Kiam

Besada-Portas

Schulte

2021

Sensors

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Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.

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Section: Related Workmentioning

confidence: 99%

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Kiam

Besada-Portas

Schulte

2021

Sensors

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“…Therefore, to show the superiority of the proposed method, it was compared with four methods SAUAV, BSUM-base, HVCR, CS-AVN and CST-UAS under the criteria of DR, FN, FP, PDR, and RE. n Iteration PDR: The number of packets successfully received by the destination UAV is divided by the number of packets sent by the source UAV [21][22][23][24]. This criterion is shown in Eq.…”

Section: Performance Metricsmentioning

confidence: 99%

Secure communication between UAVs using a method based on smart agents in unmanned aerial vehicles

Faraji-Biregani¹,

Fotohi

2020

J Supercomput

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Unmanned Aerial Vehicles (UAVs) can be deployed to monitor very large areas without the need for network infrastructure. UAVs communicate with each other during flight and exchange information with each other. However, such communication poses security challenges due to its dynamic topology. To solve these challenges, the proposed method uses two phases to counter malicious UAV attacks. In the first phase, we applied a number of rules and principles to detect malicious UAVs. In this phase, we try to identify and remove malicious UAVs according to the behavior of UAVs in the network in order to prevent sending fake information to the investigating UAVs. In the second phase, a mobile agent based on a three-step negotiation process is used to eliminate malicious UAVs. In this way, we use mobile agents to inform our normal neighbor UAVs so that they do not listen to the data generated by the malicious UAVs. Therefore, the mobile agent of each UAV uses reliable neighbors through a three-step negotiation process so that they do not listen to the traffic generated by the malicious UAVs. The NS-3 simulator was used to demonstrate the efficiency of the SAUAV method. The proposed method is more efficient than CST-UAS, CS-AVN, HVCR, and BSUM-based methods in detection rate, false positive rate, false negative rate, packet delivery rate, and residual energy.

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“…In the node-based algorithms, the workspace is conceptualized as a graph or a grid, and the feasible path is obtained using approaches similar to the well-known Dijkstra algorithm [23]. Within this category, the work in [24] describes an improved variant of the A-Star algorithm that allows the online path-planning for UAVs.…”

Section: Introductionmentioning

confidence: 99%

Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

et al. 2020

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This work presents a novel path-planning approach for Unmanned Aerial Vehicles (UAVs) in continuous 3D environments. This proposal aims to minimize the path length while avoiding collisions through the suitable adjusting of control points (the points that take the UAV from a start position to a target location). The above is stated as a constrained global optimization problem. This problem considers the overall length of the path as the single objective function. Regarding the problem constraints, they are related to the collision of the obstacles with the 3D shape of a path. The assignment of the path shape is also proposed in this work to streamline the planning process. Due to the optimization problem features (high nonlinearity, multimodality, non-differentiability, and the lack of an initial guess solution), a constraintshandling mechanism is used in meta-heuristics to find suitable optimized paths. Also, an enhanced pathsearch mechanism is included in these algorithms to deal with complex planning scenarios. The enhanced mechanism incorporates a path computed by a variant of the A-Star method (the Pruned A-Star) in the first set of candidate solutions of the meta-heuristics. The proposed approach is tested through six complex scenarios. Moreover, the performance of three well-known meta-heuristics, Differential Evolution (DE), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA), is studied to find a potential candidate to solve the path-planning problem. In this way, the paths found by DE show outstanding performance. The paths obtained by the Pruned A-Star technique are adopted as a point of comparison to determine the advantages and drawbacks of the proposal. INDEX TERMS Path-planning, optimization problem, aerial vehicles, meta-heuristics, continuous spaces.

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Energy-Efficient 3D Navigation of a Solar-Powered UAV for Secure Communication in the Presence of Eavesdroppers and No-Fly Zones

Cited by 24 publications

References 30 publications

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Secure communication between UAVs using a method based on smart agents in unmanned aerial vehicles

Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

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