## 2 UAV-Enabled ITS Framework
### First Layer: Technical Specifications
#### 2.1.2 Architectural View

Table 1: Research summary on UAV deployment and trajectory optimization. for monitoring and tracking, and gathering traffic information. However, it also comes with several theoretical and functional challenges. Common UAV deployment problems include finding optimal UAV locations, optimizing UAV trajectories, deployment optimization for speed, coverage, and energy efficiency, emergency response, network topology, communication for the Internet of Things (IoT), and data acquisition. Several optimization algorithms have been developed lately for static and dynamic deployment to address those challenges. [PERSON] et al. [38] addressed the static deployment problem, considering the optimal placement of UAVs for maximum coverage. Some works considered other constraints, including average throughput [39] or speed, time, and communication [40]. Moreover, dynamic deployment has also been studied in different scenarios. The dynamic deployment problem must be considered for UAV-enabled ITSs due to the ever-changing variation in the coverage area, on-road vehicle location, speed, time, and on-time responsive communications. [PERSON] et al. [26] employed quantization theory to optimize UAV deployment and trajectories across multiple ground terminal (GT) scenarios. They developed models for static and dynamic cases: the static case aimed to minimize average GT transmission power, while the dynamic case focused on reducing time-averaged power consumption. The deployment of UAV-mounted base stations has become an emerging solution for communicating with GTs. [PERSON] et al. [28] modeled the deployment problem of UAVs considering the constraints on transmitting power, number of UAVs, caching, and locations. The proposed model aims to provide the required quality of communication services to GTs while maximizing the benefit of caching. In ITS application, one of the main goals of the UAV deployment optimization problem is establishing an efficient traffic monitoring scheme. While a single UAV with a fixed trajectory could provide coverage of a specific region of the transportation networks, the use of UAV swarms would be more adaptive to trajectories and beneficial for improved detection and tracking performance. Figure 3 shows an example of the UAV swarm structure during different flight phases. UAV swarm architecture involves coordinated communication for mission planning (pre-flight), real-time data exchange and adaptive behavior (during flight), and comprehensive data analysis and system diagnostics (post-flight). The implementation of UAV swarms and the associated challenges have been investigated for various purposes. [PERSON] et al. [41] explored an optimized vision-directed deployment strategy for rapid traffic monitoring. The optimization problem is formulated to find the lowest number of UAV hovering locations subject to the data acquisition, avoiding building heights caused by Field-of-View (FoV) obstructions. The proposed problem solution includes a deployment map that can capture road traffic footage to help assess  
Figure 3: UAV swarm architecture during different flight phases. congestion levels. [PERSON] [42] explores the optimization of swarms of fully automated UAVs (FAUAVs) to utilize the benefits of autonomous UAV swarms for smart cities. One of the most significant challenges of deploying UAV swarms is maintaining continuous inter-UAV communications. [PERSON] and [PERSON] [29] developed a networked swarm model to manage UAV movements in complex environments. Their approach addresses the challenge of inter-communication among UAVs amid obstacles like trees and infrastructure. For high-quality forest mapping, they employed a leader election algorithm that collects data from the swarm and directs it to the target location. Moreover, deploying a UAV network aims to provide the optimal coverage of ground nodes. [PERSON] et al. [43] modeled this problem as a multi-objective coverage problem considered a nondeterministic polynomial time (NP)-hard problem.