# Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
## 5 Radio Surveillance by UAV-BSs
Their simulation results verified the power savings of the proposed algorithms. The paper [19] proposed an optimal placement algorithm for multiple UAV-BSs that maximizes the number of covered users using the minimum transmit power. To simplify the UAV-BSs' deployment problem, the authors in [19] decoupled the problem in the vertical and horizontal dimensions. They modelled the UAV-BS deployment in the horizontal dimension as a circle placement problem and the smallest enclosing circle problem. The simulations in [19] verified the transmission power savings and increased in the number of covered users as the user heterogeneity increases by applying the proposed method. To investigate the 3D deployment of a UAV-BS to maximize the number of covered users with different quality-of-service (QoS) requirements, the authors of [19] modelled the deployment problem as a multiple circles placement problem and proposed an exhaustive search (ES) algorithm over a 1D parameter in a closed region. In addition to the ES algorithm, the authors of [19] also proposed a maximal weighted area (MWA) algorithm to solve the placement problem, which is computationally efficient. To investigate the UAV-BS deployment for minimum-delay communications, the authors of [112] formulated a minimum-delay UAV-BS placement problem, subject to practical constraints imposed on the UAV-BS' battery life and velocity. The authors in [112] transformed the primal problem to the corresponding constrained Markov decision process (CMDP) and provided a reinforcement learning-aided solution to the problems formulated under various assumptions concerning the wireless teletraffic dynamics. Reference [111] studied the placement optimization of multiple UAV-BSs, subject to minimizing the number of UAV-BSs to cover every user in the considered area. To this end, the authors in [111] proposed a polynomial-time algorithm with successive UAV-BSs placement, where the UAV-BSs are placed sequentially starting on the area perimeter of the uncovered users along a spiral path toward the centre, until all users are covered. Previous studies recognized the key role played by the backhaul limitations while designing and deploying UAV-BSs [108; 109; 110]. The authors in [110] proposed a framework that utilizes the 3D mobility of the UAV-BSs as the major degree of freedom to find the optimal precoder design for the backhaul links, UAV-BSs' 3D hovering locations, power allocations, and user-BS association by exhaustive search. Reference [108] studied the optimal 3D deployment of a UAV-BS over an urban environment with users that have various rate requirements, considering both the bandwidth of a UAV-BS and the wireless backhaul peak rate as the limiting factors. In particular, the authors of [108] adopted the exhaustive search and branch-and-bound method in a step size of 100 m to search for UAV-BS's 3D location to maximize the total number of served users and sum-rates. Such as solution, however, has the same problem as in [110] in that the solutions are both inefficient and cannot guarantee accurately optimal results. In addition, these two methods simply assumed that the UAV-BSs are connected to remote ground-BSs, which is not practical for ignoring the backhaul links' communication range constraint. The authors in [102] introduced the use of a robust extended Kalman filter to estimate users' positions with the received signal strength indication (RSSI) and proposed a decentralized algorithm to find a locally optimal result to maximize UAV-BSs' coverage while considering both the backhaul limitations and collision avoidance of UAV-BSs. However, the results may not be globally optimal. Reference [113] studied a joint placement, resource allocation, and user association problem for multiple UAV-BSs with constrained backhaul links. The authors first determined the best resource allocation schemes based on various fronthaul and backhaul conditions and then developed an efficient iterative algorithm to optimize user association and UAV-BSs placement simultaneously.