A distributed PSO-based exploration algorithm for a UAV network assisting a disaster scenario

Sánchez-García, J.; Reina, Daniel Gutiérrez; Toral, S. L.

doi:10.1016/j.future.2018.07.048

Cited by 139 publications

(57 citation statements)

References 45 publications

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“…are defined in a similar way. As final result, the original fractional problem (10) has been reduced to two optimization subproblems, one for each sub-time slot, which can be studied separately. Henceforth,, since the two sub-problems are in the same form, without loss of generality we will refer to the first sub-time slot problem.…”

Section: A Power Allocation Proceduresmentioning

confidence: 99%

Energy-Efficient and Throughput Fair Resource Allocation for TS-NOMA UAV-Assisted Communications

Masaracchia

Nguyen

Duong

et al. 2020

IEEE Trans. Commun.

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This paper proposes an optimization framework for power and time resource allocation during time sharing nonorthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial vehicle (UAV) in the context of a largescale scenario. The objective of the proposed UAV-TS-NOMA system and optimization framework is to jointly maximize the energy efficiency (EE) and the downlink throughput fairness among users within the UAV communication range. The idea behind is to propose a communication system that: i) merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm and ii) maximizes the EE and the downlink fairness among users. The resulting model finds applicability in performing energy efficient and throughput fair transmissions into power-constrained communication scenarios. Performance investigations regarding the proposed framework in finding the optimal set of resources which maximizes jointly the above mentioned network metrics, have shown the advantage of the proposed two-step optimization framework in finding the optimal configuration of both power and time resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. In addition, it is shown how under particular conditions the proposed framework jointly optimizes the aforementioned network metrics in only one step.

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Section: A Power Allocation Proceduresmentioning

confidence: 99%

Energy-Efficient and Throughput Fair Resource Allocation for TS-NOMA UAV-Assisted Communications

Masaracchia

Nguyen

Duong

et al. 2020

IEEE Trans. Commun.

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“…The PSO-C algorithm was compared with LEACH (Low Energy Adaptive ClusteringHierarchy) and LEACH-C (Low Energy Adaptive Clustering Hierarchy-cluster based protocol). J. Sanchez-Garcia et al [24] proposed a novel dynamic Particle Swarm Optimization for UAV networks (dPSO-U). This dPSO-U was for a path optimization in the rescue operations of the given disaster situation.…”

Section: Literature Surveymentioning

confidence: 99%

BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

Ganesan

Raajini

Nayyar

et al. 2020

Sensors

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Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.

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“…Swarms of mobile robots enable the execution of many tasks faster and more effectively than a lone robot, for example, in field exploration [21,22], search for a target of surveillance [23,24] or rescue [25,26]. This is possible because of their number as well as their group intelligence, which allows distribution of tasks between robots in the swarm.…”

Section: Related Workmentioning

confidence: 99%

Toward a robot swarm protecting a group of migrants

Vaidis

Otis

2020

Intel Serv Robotics

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Different geopolitical conflicts of recent years have led to mass migration of several civilian populations. These migrations take place in militarized zones, indicating real danger contexts for the populations. Indeed, civilians are increasingly targeted during military assaults. Defense and security needs have increased; therefore, there is a need to prioritize the protection of migrants. Very few or no arrangements are available to manage the scale of displacement and the protection of civilians during migration. In order to increase their security during mass migration in an inhospitable territory, this article proposes an assistive system using a team of mobile robots, labeled a rover swarm that is able to provide safety area around the migrants. We suggest a coordination algorithm including CNN and fuzzy logic that allows the swarm to synchronize their movements and provide better sensor coverage of the environment. Implementation is carried out using on a reduced scale rover to enable evaluation of the functionalities of the suggested software architecture and algorithms. Results bring new perspectives to helping and protecting migrants with a swarm that evolves in a complex and dynamic environment.

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A distributed PSO-based exploration algorithm for a UAV network assisting a disaster scenario

Cited by 139 publications

References 45 publications

Energy-Efficient and Throughput Fair Resource Allocation for TS-NOMA UAV-Assisted Communications

Energy-Efficient and Throughput Fair Resource Allocation for TS-NOMA UAV-Assisted Communications

BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

Toward a robot swarm protecting a group of migrants

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