# Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)
## 2 Related Works
The authors of [21] developed an effective communication model for the UAV networks through hybrid automatic repeat request protocol to control the congestion in the network. However, the communication is ineffective due to the lack of security. The authors of [22] proposed a reinforcement learning-based trajectory control for UAVs that increase the stability of the network by protecting the network from the jamming, but it fails to reduce the energy consumption. The authors of [23] proposed a bio-inspired UVDTN network to improve the quality of service (QoS) of the urban VANETs. The algorithms which are used in this process are social spider optimization (SSO) and store carry forward (SCF) process. The results show that this method achieves low delay and overhead but fails to achieve high efficiency. The authors of [24] proposed a NOMA-based resource allocation in vehicular communication using game theory. The simulation results achieve moderate efficiency. The authors of [25] developed an authentication and key agreement (AKA) scheme to improve the privacy of the drones-based network. Through this method, the communication and computational cost are reduced. However, it fails to concentrate on other parameters such as efficiency, overhead, throughput, etc. The authors of [26] introduced a method to avoid the congestion called fog-assisted smooth packet distribution. This method improves the packet delivery ratio and reduces the overhead but fails to improve the security of the network. The authors of [27] developed a trust-and-priority-based drone-assisted IoV. Through this method, the overhead and delay are reduced, packet delivery ratio and efficiency are high, but the throughput achieved by this network is moderate. The advantages and disadvantages of these earlier studies are shown in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Refs.** & **Technique Used** & **Advantages** & **Disadvantages** \\ \hline
[9] & Data scheduling through game theory & Low delay, high throughput & Routing Overhead is high \\ \hline
[10] & Collaborative network coverage enhancement scheme (CONEC) using PSO & Packet delivery ratio and throughput is high & Routing Overhead is high \\ \hline
[11] & Bio-inspired and traffic-aware mix-zone placement model & Throughput and securitye high & Overhead is high and efficiency is low \\ \hline
[12] & Secure and lightweight drone access protocol & Run time cost and communication cost is reduced & Energy Consumption is high and energy efficiency is poor \\ \hline
[13] & Collaborative communication scheme & Packet delivery ratio and throughput is high & Routing Overhead is high \\ \hline
[14] & Geographic node speed drone-based routing path optimization & Efficiency is high and delay is low & Communication cost is high \\ \hline
[15] & Improved particle swarm optimization (IPSO) & High coverage and quality are achieved & Routing Overhead is high, security is low \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Earlier Research Analysis. By analyzing all these earlier studies, it is understood that the major drawbacks in the drone-based VANETs are congestion, high overhead, and security. In [25], AKA authentication is performed, which fails to achieve lower congestion and overhead. Hence, the major focus is security, but the huge number of vehicles with dynamic mobility pattern leads to high delay, congestion, and packet loss. The authors of [26] focus on the congestion control of the vehicles during the process of communication which provides a way to some of the misbehaviors that is reflected in the increases of packet loss, overhead, and energy consumption. In [27], the trust and priority of the network is the focus, but there is no optimal path between the source and the destination during communication, which leads to ineffective communication. These are major drawbacks from the earlier research works.