Energy Minimization UAV Trajectory Design for Delay-Tolerant Emergency Communication

Abstract

The increasing cases of wireless communication networks being partly (or even fully) destroyed after the occurrence of natural disasters has made researchers focus on the use of Unmanned Aerial Vehicles (UAVs) to provide quick and efficient backup communication in post-disaster scenarios. However, the performance of UAVs in the provisioning of wireless coverage is known to be constrained by their battery life, which limits their flight times. In this paper, we explore the use of a single UAV to provide backhaul connectivity to truck-mounted Base Stations (BSs) that have been deployed within a disaster zone to provide network coverage to users based on the principle of delay-tolerant communications. We propose a trajectory design that uses genetic algorithm to find the trajectory with the least energy requirement for the UAV to visit all the BSs and return to a central node that acts as a gateway to the core network. Our trajectory design takes into account both the straight-andlevel flight and banked-level turns of the UAV in computing the energy requirement. Simulation results show that our proposed design outperforms two approaches in the literature by up to 14% and 40%.

Softwarization of UAV Networks: A Survey of Applications and Future Trends

“…The crucial challenge of this assistance is to provide ubiquitous backhaul connectivity to UAV-BSs. This could be carried out through the routing of the traffic to/from the UAV-BSs from/to the existing cellular networks [98]. The backhauling concept is considered as a cost-effective solution allowing operators to get end-to-end control of their network.…”

Coarse Trajectory Design for Energy Minimization in UAV-Enabled

“…Due to the limited endurance and on-board energy of UAVs, the problem of UAV energy minimization has attracted much attention [18]- [22]. The work [18] applies the genetic algorithm to design the trajectory with the least energy consumption to visit all BSs and return to the UAV station.…”

“…Due to the limited endurance and on-board energy of UAVs, the problem of UAV energy minimization has attracted much attention [18]- [22]. The work [18] applies the genetic algorithm to design the trajectory with the least energy consumption to visit all BSs and return to the UAV station. Reference [19] minimizes the completion time and energy consumption problems for a fixed-wing UAV-enabled multicasting system via jointly optimizing the flying speed, UAV altitude, and antenna beamwidth.…”

“…The aforementioned works have addressed the various new challenges in UAV-enabled communications, such as completion time minimization [9], energy minimization [18]- [22], and throughput maximization [17]. Moreover, efficient methods have been devised to deal with complicated optimization problems, e.g., time discretization method [16], [20], path discretization method [21], block coordinate descent (BCD) in combination with the successive convex approximation (SCA) method [22], and efficient trajectory design [9].…”

Energy Optimisation through Path Selection for Underwater Wireless Sensor Networks

“…Since the AUV must visit all cluster heads and return to the floating platform, we model the problem as a variant of the travelling salesperson problem (TSP), where an underwater robot must select a path that minimises its travel time or energy expenditure [3]. There are different ways to address this problem, including using genetic algorithm [4], ant colony optimisation [5], [6], using reinforcement learning [7], using nearest neighbour optimisation [8], [9], etc. However, unlike classical shortest path problems, we treat this as a stochastic shortest path problem [10] since the AUV must consider the energy cost of a path in addition to the path length.…”