Due to the flying nature of unmanned aerial vehicles (UAVs), it is very attractive to deploy UAVs as aerial base stations and construct airborne networks to provide service for on-ground users at temporary events (such as disaster relief, military operation, and so on). In the constructing of UAV airborne networks, a challenging problem is how to deploy multiple UAVs for on-demand coverage while at the same time maintaining the connectivity among UAVs. To solve this problem, we propose two algorithms: a centralized deployment algorithm and a distributed motion control algorithm. The first algorithm requires the positions of user equipments (UEs) on the ground and provides the optimal deployment result (i.e., the minimal number of UAVs and their respective positions) after a global computation. This algorithm is applicable to the scenario that requires a minimum number of UAVs to provide desirable service for already known on-ground UEs. Differently, the second algorithm requires no global information or computation, instead, it enables each UAV to autonomously control its motion, find the UEs and converge to on-demand coverage. This distributed algorithm is applicable to the scenario where using a given number of UAVs to cover UEs without UEs’ specific position information. In both algorithms, the connectivity of the UAV network is maintained. Extensive simulations validate our proposed algorithms.

Deployment Algorithms for UAV Airborne Networks Toward On-Demand Coverage

Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due to their agility and versatility, which have attracted significant attentions from both the academia and industry in recent years. As an integration of embedded systems with communication devices, computation capabilities and control modules, the UAV network could build a closed loop from data perceiving, information exchanging, decision making to the final execution, which tightly integrates cyber processes into physical devices. Therefore, the UAV network could be considered as a cyber physical system (CPS). Revealing coupling effects among the three interacted CPS components, i.e., communication, computation and control, is envisioned as the key to properly utilize all available resources and hence improve the performance of the UAV network. In this paper, we present a comprehensive survey on UAV networks from a CPS perspective. Firstly, we review the basics and advances of the three CPS components in UAV networks. Then we look inside to investigate how these components contribute to the system performance by classifying UAV networks into three hierarchies, i.e., cell level, system level, and system of system level. Furthermore, the coupling effects among these CPS components are explicitly illustrated, which could be enlightening to deal with the challenges in each individual aspect. New research directions and open issues are discussed at the end of this survey. With this intensive literature review, we intend to provide a novel insight into the state of the art in UAV networks.

Survey on Unmanned Aerial Vehicle Networks: A Cyber Physical System Perspective

Due to the flexibility in 3-D space and high probability of line-of-sight (LoS) in air-to-ground communications, unmanned aerial vehicles (UAVs) have been considered as means to support energy-efficient data collection. However, in emergency applications, the mission completion time should be main concerns. In this article, we propose a UAV-aided data collection design to gather data from a number of ground users (GUs). The objective is to optimize the UAV’s trajectory, altitude, velocity, and data links with GUs to minimize the total mission time. However, the difficulty lies in that the formulated time minimization problem has mutual effect with trajectory variables. To tackle this issue, we first transform the original problem equivalently to the trajectory length problem and then decompose the problem into three subproblems: 1) altitude optimization; 2) trajectory optimization; and 3) velocity and link scheduling optimization. In the altitude optimization, the aim is to maximize the transmission region of GUs which can benefit trajectory designing; then, in the trajectory optimization, we propose a segment-based trajectory optimization algorithm (STOA) to avoid repeat travel; besides, we also propose a group-based trajectory optimization algorithm (GTOA) in large-scale high-density GU deployment to relieve massive computation introduced by STOA. Then, the velocity and link scheduling optimization is modeled as a mixed-integer nonlinear programming (MINLP) and block coordinate descent (BCD) is employed to solve it. Simulations show that both STOA and GTOA achieve shorter trajectory compared with the existing algorithm and GTOA has less computational complexity; besides, the proposed time minimization design is valid by comparing to the benchmark scheme.

Joint Optimization on Trajectory, Altitude, Velocity, and Link Scheduling for Minimum Mission Time in UAV-Aided Data Collection

Exploiting unmanned aerial vehicles (UAVs) as flying base stations (BSs) to assist the terrestrial cellular networks is promising in 5G and beyond. Despite the inherent potentials, one challenging problem is how to optimally deploy multiple UAVs to achieve on-demand coverage for ground user equipment (UE). In this article, we model the deployment problem as minimizing the number of UAVs and maximizing the load balance among them, which is subject to two main constraints, i.e., UAVs should form a robust backbone network and they should keep connected with the fixed BSs. To solve this optimization problem with low complexity, we decompose the problem into two subproblems and propose a hybrid algorithm to solve them stepwise. First, a centralized greedy search algorithm is used to heuristically obtain the minimum number of UAVs and their suboptimal positions in a discontinuous space. Then, a distributed motion algorithm is adopted which enables each UAV to autonomously control its motion toward the optimal position in a continuous space. The proposed algorithm is applicable to various scenarios where UAVs are deployed alone or with fixed BSs regardless of the UE distribution. Extensive simulations validate the proposed algorithm.

Deployment Algorithms of Flying Base Stations: 5G and Beyond With UAVs

The distributed mobile robotic network consists of a group of mobile nodes, such as mobile sensors, unmanned vehicles, unmanned submarines, unmanned air vehicles, or mobile robots. The mobile robotic network keeping a regular topology can utilize efficient network protocols and is also promising in many application scenarios. We propose a distributed algorithm that controls multiple distributed robotic nodes to form regular topology, including straight line, ring, triangular lattice, and square lattice. Our algorithm generates artificial forces, including the attractive force towards a reference point to gather the distributed nodes, the repulsive force from neighboring nodes to keep the desirable distance among them, the formation force to form a specific shape, and the obstacle avoidance force to avoid possible obstacles, such that each node simply follows the resultant force to move. The algorithm works in a fully distributed manner, converges fast, and is easy to deploy, requiring only one-hop local network geometry information. And, it is effective under both 2D and 3D scenarios. A computer demo is developed to demonstrate the effectiveness of the algorithm for large numbers of robotic nodes.

Lingchu Mao

Papers

Coverage on demand: A simple motion control algorithm for autonomous robotic sensor networks