# Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)
## 4 Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based SIoDTs (BDTC-SIoDTs)
### Congestion-Aware Community  
A congestion-aware community is designed in terms of analyzing the road traffic in the densely populated area and discovering a suitable path for data transmission [32]. The network's similar functions are analyzed, and the dynamic trust-based clustered community is created. Each cluster is represented as a separate community, and it consists of basic cluster quality as well as irregular vehicle allotment. The structure of the congestion-aware cluster community is shown in Figure 5. The real-time congestion is analyzed using certain parameters, such as vehicle information, and momentum variation inside the cluster where the vehicle which is located at the shorter distance to the CH directly transmits the data, while the vehicle which has a high density to the CH transfers the data to the CH with the help of the intermediate vehicles. In a real road condition, vehicle distribution in a densely populated area turns out to be highly uneven. For this reason, even an aggregated network creates traffic congestion. Congestion-aware clustered community creation results in congestion avoidance in the densely populated area. The initial stage of this message is to transmit a warning message when high variation is present in momentum and there is density among the vehicles, which are measured in terms of time variation. The congestion inside each cluster community is captured, and the warning message is transmitted to the CH. Secondly, using the bio-inspired optimization method, the optimal path is found to transfer the data from the vehicles to the CH. The process of finding the optimal path is elaborated in the next section.  
### Congestion-Aware Community  
For the process of optimal path selection for this network, a bio-inspired hybrid optimization model is utilized, which is the combination of ant colony optimization (ACO) and grey wolf optimization (GWO). The ACO algorithm is one the most familiar biological ant-based models where ants search for food using pheromone deposition during the movement from one place to another. According to the pheromone quality, the shortest path from the source to the destination is decided. In the GWO algorithm, the social characteristics of the grey wolf are considered, and certain parameter analysis with random values of vehicle count, coverage area, and density of the grid followed by cluster formation and cluster head (CH) creation is performed. In this network, the CH selection is based on the dynamic trust-based clustering approach, which is already shown in Section 4.1. The CH is chosen, and it manages the whole cluster and also provides a way for vehicles to newly join the cluster and leave the cluster; this is because the vehicles are dynamic in nature and one vehicle cannot be a member of two clusters. These are the general processes of the ACO and GWO algorithms. To find an optimal path at high speed, dynamically varying network hybrid optimization is essential to achieving better performance. Here, at the initial stage, the ACO algorithm is used to find the shortest path, followed by the GWO algorithm which provides an optimal path according to the calculation of vehicles leaving and joining the cluster; moreover, it maintains the minimum vehicle count inside the cluster.  
Figure 5: Congestion-Aware Cluster Community.