Unmanned aerial vehicle‐enabled layered architecture based solution for disaster management

Abstract

Unmanned aerial vehicle (UAV) has become a priority in the industrial and academic research community for the last few years. With the fast development of wireless networking technologies, it is widely used in many applications such as surveillance, search and rescue (SAR) operation, disaster management, remote monitoring, and so forth. Emergency communication for victims and first responders is one of the critical requirements during natural and human-made disasters. Airborne Base Stations (BS) or UAV-mounted BS has been used to provide high-speed internet services and rescue responses in natural disasters and emergencies in recent years. Most of the previous research on UAV-mounted BS (UAVmBS) only considers outdoor users. It assumes their coordinate position in a two-dimensional (2D) plane, taking only Euclidean pathloss-based distance into account.Though, in man-made or urban natural disaster situations such as floods, cyclones, building fires, and so forth, users are stuck in single or multistory high-rise buildings situated in a three-dimensional (3D) plane. In such cases, it is possible to interact and rescue reliably if pathloss features are known a priori, both for UAVmBS to users (downlink) and vice versa (uplink). As pathloss in the above situations is a mixture of outdoor and indoor elements, adopting a compatible model can be challenging for communications. This article proposes a layered architecture and studies the path loss models best suited for the disaster management system (DMS), which considers both indoor and outdoor parameters. Further, the main goal is to effectively implement a layered architecture employing UAVmBS in urban disasters for better capacity and coverage improvement. Simulation outputs display the progress in transmission delays, throughput, and effective packet delivery for disaster management.

Altitude and density optimization over UAV‐enabled massive IoT wireless communications

“…However, some significant challenges must be addressed when using UAVs, such as air-to-ground (A2G) propagation channel modeling, 3D localization, optimizing UAV power consumption, 8 secure communications, 9 and disaster management. 10,11 Recent research on the fundamental techniques for such networks has primarily focused on three approaches:…”

“…Furthermore, in (10), the term log 2 (1 + 𝛽) > 0 for 𝛽 > 0 is independent of 𝜆 d and can be ignored.…”

“…As integral over a non-negative function is always greater than zero, hence, A (𝜆 d , h d , 𝛽) > 0. It should be noted that in (10)…”

“…Because of these benefits, incorporating UAVs into traditional terrestrial networks is a promising solution for enabling mIoT services, improving coverage, and increasing connectivity. However, some significant challenges must be addressed when using UAVs, such as air‐to‐ground (A2G) propagation channel modeling, 3D localization, optimizing UAV power consumption, 8 secure communications, 9 and disaster management 10,11 …”

Quick cooperative spectrum sensing in cognitive unmanned aerial vehicles networks

“…Unmanned aerial vehicles (UAVs) are an increasingly growing technology that offers a wide variety of productive solutions in different smart applications in the real world, 1,2 which do not require human operator, or where human intervention is risky, dangerous, impossible or expensive. 3 A smart alliance of UAVs is inclusively used in the range of applications like in the public safety, military, and commercial use such as intelligence surveillance, inland-defense, border protection live video remote communication and setting up guidance units, 4,5 which operate in these bands leading to increased utilization of these bands, consequently, UAVs also face the problem of spectrum scarcity.…”