A distributed PSO-based exploration algorithm for a UAV network assisting a disaster scenario
Introduction
According to the Annual Disaster Statistical Review [1], in 2014 a total number of 324 natural disasters occurred worldwide, claiming over 7.823 victims and affecting to 140 millions of people. Disaster scenarios usually leave the communication infrastructure affected and not able to provide normal communication services. The communication between rescuers, also known as first responders, is vital as it may be the key to a successful rescue mission. In this situation, first responders face one of the most important tasks: to deploy an ad hoc network on the disaster area [2].
Mobile Ad hoc Networks (MANET) provided emergency response teams with the ability to deploy multihop and tailored communication networks rapidly [[2], [3], [4]]. However, in recent years, the breakthrough in the development of small unmanned aerial vehicles (UAVs), also known as drones, paved the way for developing new communication solutions for emergency response teams and rescue missions [[5], [6], [7], [8]]. UAVs are able to move to specific locations in a short time frame while avoiding almost any kind of obstacle. Furthermore, multiple UAVs can be used as a nodes swarm forming a flying network. These are commonly known as Flying Ad hoc Networks (FANET) [9] or Aerial Ad hoc Networks (AANET) [6]. One of the main capabilities of UAV networks is the enormous mobility of the nodes, which allows adapting the network topology to the scenario conditions, in order to optimize the desired objectives [9].
Our main purpose is to propose a distributed solution for generating high-level trajectories for a UAV network with the mission of exploring a large-scale disaster scenario. The secondary purpose is to make the UAVs converge to several zones in which victims are gathered, i.e. victims’ clusters, in order to provide assistance in the form of communication services. Two main considerations to highlight are: (i) the UAV network follows the approach of a Delay Tolerant Network (DTN) [2], in which nodes have short communication ranges and only exchange information in the case of an encounter, and (ii) although victims are organized in clusters, victims may move and thus the cluster shape may evolve over time.
We propose a solution for efficiently generating trajectories for a UAV network in a search mission, in a disaster scenario. Our solution is based on an adaptation of the well-known swarm algorithm called Particle Swarm Optimization (PSO). Due to this, we call the proposed algorithm Distributed and dynamic PSO for UAV networks (dPSO-U). Although other works previously applied PSO-based solutions for similar applications none of them performed a characterization of the algorithms proposed. We provide extensive simulation results with different value sets of the main dPSO-U parameters. Finally, we compare dPSO-U with an optimal trajectory planning algorithm which is able to sweep the entire scenario area, and thus finding all victims most of the times. Due to the fact that disasters are not predictable and have large-scale consequences, the best approach for validating new proposals is through modeling and simulation. We follow the approach of using modeling and simulation techniques for validating our proposal, in a similar manner to other works such as [[6], [10], [11], [12]], among others.
We believe that a UAV network with the capabilities described above would clearly benefit the work of emergency response teams, as the UAVs could be exploring the scenario even before the first responders enter the disaster scenario area. This would be useful for victims, who could receive first-aid information from the UAV network, as well as for first responders who would start the rescue mission with an already-deployed communication network. The main contributions of this paper are:
A proposed PSO-based algorithm, called dPSO-U, which generates trajectories for a UAV network performing exploration tasks in a disaster scenario. A secondary objective, of making the UAVs converge towards several victims groups, is also achieved by the dPSO-U algorithm.
The characterization of the proposed algorithm for different sets of values of the inertia, local best and neighbor best. This allows identifying the best-performing combination of PSO parameters for the specific objectives of this paper.
A comparison of the dPSO-U algorithm against an optimal trajectory planning algorithm, which previous to entering in the disaster scenario, calculates the trajectories for the UAV network to sweep the entire disaster scenario area.
To the knowledge of the authors, there are no other works that have addressed the contributions listed above. This paper is organized as follows; Section 2 presents the related works. Section 3 describes the environment that has been considered in the simulations, i.e. the scenario and UAV models. The proposed algorithm is described in Section 4. The simulation results are described in Section 5. Finally, Section 6 concludes the paper.
Section snippets
Related work
The design of autonomous systems able to generate trajectories for each UAV in a flying network and meeting the mission requirements is one of the main challenges for the research community [13]. Control approaches with this purpose can be organized in the following categories: (i) centralized, (ii) decentralized and (iii) distributed [14]. In centralized approaches, the control algorithm resides entirely in one central node, either a UAV or a ground station, which is responsible for most of
Environment model
A description of the simulated environment in which the proposed solution has been tested is described in this section. We organize the section in two main subsections. The first one describes the assumptions made for building a disaster scenario model. The second subsection presents the description of the UAV network model in which the proposed PSO-based algorithm has been implemented.
PSO-based exploration algorithm
In this section we first present the PSO algorithm in one the most common and well-known formulation. Later on we describe the PSO-based algorithm that we propose for a UAV network performing a search mission.
Simulation results
In the first part of this section we present the settings that we used for running the simulations of the proposed dPSO-U algorithm. Later in the section we present the simulation results obtained from the characterization of the proposed algorithm. Secondly, we present the algorithm behavior on scenarios with a different number of clusters, ranging from 1 up to 10 clusters. Finally, we present a comparison between one of the best-performing cases of the dPSO-U algorithm against a trajectory
Conclusions
We propose a PSO-based algorithm, called dPSO-U, for exploring a disaster scenario area, where the PSO particles are UAVs following a DTN networking approach. We have run extensive simulations for performing a characterization of the algorithm with different value sets of the main parameters, namely: the inertia, the local best and the neighbor best. Thanks to the characterization we can evaluate the best value combination for efficiently exploring the scenario and finding several victims
Jesús Sánchez-García was born in Seville, Spain, in 1984. Since 2012, he holds a M.S. degree in telecommunications engineering from the University of Seville, Seville, Spain. From 2010 to 2013 he worked as an engineer in the R&D department of a privately owned engineering company. In 2014 he joined the University of Seville as a scientific personnel and he is pursuing his Ph.D in electronic engineering in the field of wireless ad hoc networks and its applications. Currently he works at Galgus,
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Jesús Sánchez-García was born in Seville, Spain, in 1984. Since 2012, he holds a M.S. degree in telecommunications engineering from the University of Seville, Seville, Spain. From 2010 to 2013 he worked as an engineer in the R&D department of a privately owned engineering company. In 2014 he joined the University of Seville as a scientific personnel and he is pursuing his Ph.D in electronic engineering in the field of wireless ad hoc networks and its applications. Currently he works at Galgus, an engineering company devoted to maximize the performance of WiFi technology.
D.G. Reina was born in Seville, Spain, in 1983. He received the B.E. degree in electronic engineering and M.S. degree in electronics and telecommunications from the University of Seville, Seville, Spain, in 2009 and 2011 respectively. He obtained the Ph.D. degree in electronic engineering with honors in 2015 by the University of Seville, Seville. He is currently working as an assistant professor at the Engineering department of Loyola Andalucía University. His current research interests include multi-hop wireless networks such as ad hoc networks, vehicular ad hoc networks, delay tolerant networks and flying ad hoc networks.
Sergio Toral was born in Rabat, Morocco, in 1972. He received the M.S. and Ph.D. degrees in electrical and electronic engineering from the University of Seville, Spain, in 1995 and 1999, respectively. He is currently a full Professor with the Department of Electronic Engineering, US. His main research interests include ad hoc networks and their routing protocols, deployment of wireless sensor networks, flying ad hoc networks, real-time and distributed systems, intelligent transportation systems, and embedded operating systems. He is actually an author or coauthor of 73 papers in major international peer-reviewed journals (with JCR impact factor) and of over 100 papers in well-established international conferences and workshops.