# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 1 Introduction
Simulation results show that the performance of CWG is best in terms of throughput and energy. Figure 1: Disaster-affected region. Another important clustering technique is called the K mean clustering, which is mostly used in Wireless Sensor Networks (WSNs). In [17], K means and LEACH-C are combined to prolong the lifetime of the network. At the start, the K mean algorithm is used to form clusters, then LEACH-C is applied to each cluster. The method reduces the overhead and increases the packet success rate. In [18], the authors have used the K means L layer algorithm, which results in a decreased number of clusters enhancing network lifetime. In [19], an energy-efficient K mean clustering protocol is proposed to optimize packet size based on the channel conditions. This approach reduces energy consumption and increases the overall network lifetime. In [20], an optimum value of K is obtained using the elbow method and afterwards, clustering will be applied using the K mean algorithm. Simulations show that after running the elbow method on different points, there comes a point where gain gained by increasing K will drop. This is the optimum value of K.  
In the absence of a cellular network, relaying critical data from the devices to the CC is another important concern. Several routing protocols/algorithms exist in the literature for multi-hop/multi-user networks. An emergency routing technique based on body-to-body networks, known as an Optimized Routing Approach for Critical and Emergency Networks (ORACE-NET) is proposed in [21]. It establishes temporary network connectivity for relief works in disaster-affected areas. The results show that ORACE-NET performs better in terms of energy consumption and throughput. Considering network size, different routing schemes perform differently. Interference Aware Routing (IAR), Shortest Path Routing (SPR), and Broadcast Routing (BR) are tested in [22] to find the shortest emergency route in a disaster scenario. The simulation results show that the BR has the highest packet success ratio for small networks, while IAR performs better for large networks. In [23], the authors applied Simultaneous Wireless Information and Power Transfer (SWIPT) on CHs to achieve better performance in a disaster-affected region. Once the devices are clustered, the UAV position and trajectory plays a crucial role in determining the Energy Efficiency (EE) of the network. Mainly, in the disaster situation the UAV acts as a relay node for on-scene devices [24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. For UAV deployment in PSN, the authors in [27] discovered the optimal altitude for a UAV that maximizes coverage. In [29], authors proposed a UAV-assisted solution to establish energy-efficient connectivity in a disaster-affected region in the presence of Critical Nodes (CNs). In [30], authors proposed a D2D-based solution using UAVs that can reduce the response time significantly. In [31], authors used Reinforcement Learning (RL) technique to deploy UAVs in a disaster scenario to maximize total user coverage. In [32], the intelligent placement of UAVs as temporary aerial base stations is discussed for public safety communications. In [34], authors proposed a UAV-assisted vehicular communication framework using Software Defined Networking (SDN) to reduce the processing cost of vehicles. UAV will act as a flying relay and helps in forwarding data to a Mobile Edge Computing (MEC) server. This algorithm reduces the average system cost by half. In [35], the authors proposed a new cellular network for UAVs to support a high data rate. Three transmission modes of UAV with a network, UAVs, and devices i.e., U2N, U2U, and U2D are studied. Authors in [36] proposed a multi agent deep RL-based UAV framework assisted by MEC in which UAVs will assist users on the ground. Results showed considerable gain in terms of fairness and energy consumption. In this paper [37], the authors used UAVs in a disaster environment to extract information from its one-hop devices using wireless power transfer technique.