Deploying a Reliable UAV-Aided Communication Service in Disaster Areas

Abstract

When telecommunication infrastructure is damaged by natural disasters, creating a network that can handle voice channels can be vital for search and rescue missions. Unmanned Aerial Vehicles (UAV) equipped with WiFi access points could be rapidly deployed to provide wireless coverage to ground users. This WiFi access network can in turn be used to provide a reliable communication service to be used in search and rescue missions. We formulate a new problem for UAVs optimal deployment which considers not only WiFi coverage but also the mac sublayer (i.e., quality of service). Our goal is to dispatch the minimum number of UAVs for provisioning a WiFi network that enables reliable VoIP communications in disaster scenarios. Among valid solutions, we choose the one that minimizes energy expenditure at the user's WiFi interface card in order to extend ground user's smartphone battery life as much as possible. Solutions are found using well-known heuristics such as K-means clusterization and genetic algorithms. Via numerical results, we show that the IEEE 802.11 standard revision has a decisive impact on the number of UAVs required to cover large areas, and that the user's average energy expenditure (attributable to communications) can be reduced by limiting the maximum altitude for drones or by increasing the VoIP speech quality.

Communication and networking technologies for UAVs: A survey

“…Mayor et al [124] presented a UAV communication strategy for disaster management, which integrates a WiFi network with the UAV network to enable VoIP communication for affected people. The authors have used well-known machine learning algorithms such as K-means clustering and genetic algorithms to improve the performance of the network.…”

Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service

“…Unfortunately, the authors did not model the energy consumption, stating that “intuitively, less power is needed if the Gateway is closer to the users”. In a context similar to ours, in [ 12 ], we proposed a 3D placement algorithm that minimized the number of aerial APs deployed to provide VoWiFi to ground users. To the best of our knowledge, this is the only related work in which speech quality has been considered a constraint.…”

“…In this work, we deal with the challenge of optimal multi-UAV 3D placement in the context of creating a provisional WLAN (i.e., UAVs are used as aerial Access Points—AP) to provide ground users with VoIP over WiFi (VoWiFi) service. A distinctive characteristic of this service is that 3D placement has to consider not only coverage (e.g., [ 10 , 11 ]) but also VoIP speech quality [ 12 ] since the mac-sublayer of the communication protocol limits the number of simultaneous VoIP flows that APs can handle with quality of experience (for example, an AP performing IEEE 802.11 b can handle a maximum of 15 simultaneous calls [ 13 ], even though the number of stations associated to such AP could be multiple times greater). This limit is known as VoIP capacity [ 14 , 15 , 16 ].…”

“…In this context, the following considerations should be taken into account: Besides coverage, the multi-UAV 3D optimal placement has to consider VoWiFi speech quality for which the mac-sublayer of the communication protocol is crucial. To the best of our knowledge, the only work that has considered this factor [ 12 ] sets the focus only in minimizing the number of UAVs deployed (among solutions with a similar number of drones, the one that extended the battery life of served users was chosen) disregarding energy efficiency in UAVs. The number of UAVs required for the mission depends not only on the UAVs initially deployed but also on the number of UAVs launched to replace those in battery exhaustion.…”

“…Besides coverage, the multi-UAV 3D optimal placement has to consider VoWiFi speech quality for which the mac-sublayer of the communication protocol is crucial. To the best of our knowledge, the only work that has considered this factor [ 12 ] sets the focus only in minimizing the number of UAVs deployed (among solutions with a similar number of drones, the one that extended the battery life of served users was chosen) disregarding energy efficiency in UAVs.…”