# 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
#### 4.3.1 Initial Path Finding Using ACO Algorithm  
The principle of ACO is derived from the mutual characteristics of the ants. The food search process of the ant is used to find the optimal path. This initiates the travel from the nest to discover the food, and when it finds the food, it proceeds to transfer the food to the nest [33]. In this travel time, the ant will deposit an evaporative solution called a pheromone. Using this chemical, the other ants follow this path to transfer the rest of the food to the nest. At the initial stage, ants travel in various paths, and at the final stage once the food is found, according to the quantity of the pheromone deposition, the best optimal path is chosen. In our model, the vehicles are represented by ants that fill up the pheromone on the traveling paths. Here, congestion is measured in terms of the quantity of pheromone deposition on each road. Each road has permission to utilize the deposition of up to 80% of its threshold value, and once it has reached that stage automatically for further transmission, the next best path is shown to avoid congestion. The mathematical expression to calculate the pheromone initialization at the starting stage when the time is zero is shown below:  
\[P_{sd}(t=0)=\alpha\times distance\ (E2E_{sd}) \tag{9}\]  
In Equation (9), \(P_{sd}\) represents the amount of pheromone present from the source to the destination according to end-to-end distance, \(\alpha\) is a constant value of (0,1), and it is measured only for analysis purposes. The process is repeated until the best result is found. When the vehicle s chooses the vehicle, the current pheromone deposition at that instant of time is expressed below:  
\[P_{sd}(t=t_{1})=\frac{[P_{sd}(t_{1})]^{c_{1}}\times[\rho_{sd}]^{c_{2}}}{[P_{ sd}(t_{1})]^{c_{1}}-[\rho_{sd}]^{c_{2}}}\times\frac{1}{C_{id}} \tag{10}\]  
where  
\[\rho_{sd}=\frac{1}{distance\ (E2E_{sd})} \tag{11}\]  
In Equation (10), the term \(P_{sd}(t=t_{1})\) is the pheromone deposition between the two ends of the source and the destination, and \(C_{id}\) is the overall cost of the transmitting vehicle and both \(c_{1}\)_and_\(c_{2}\) standard constants, which helps in the measurement of the pheromone deposition. Following this, the calculation of pheromone evaporation proceeded. This process is mainly used to avoid confusion in path findings. The path which consists of a lower quantity of pheromone deposition gets evaporated automatically in some instances. The mathematical expression for the calculation of pheromone evaporation is shown below:  
\[P_{sd}(t_{1})=\frac{P_{sd}(t_{1}-1)}{\beta} \tag{12}\]  
In Equation (12), \(\beta\) is represented as a constant value of [0, 1] for analysis purposes. After this process, the current status of the pheromone is updated immediately. The mathematical expression for the calculation of pheromone updates is shown below:  
\[P_{sd}(t_{1})=\frac{P_{sd}(t_{1}-1)}{\beta}+\gamma*(\Delta P_{sd}) \tag{13}\]  
where  
\[\Delta P_{sd}=\frac{1}{N_{sd}} \tag{14}\]  
In Equation (13), \(N_{sd}\) is total ant count which is needed to transfer the data from the source to the destination, \(\gamma\) is represented by a constant value of [0, 1] for analysis purposes. The entire process of this optimization method is illustrated using Algorithm 2.  
Using this ant-based optimization technique, an optimal path is chosen in the network at the initial stage. After reaching a certain degree of vehicles joining and leaving the cluster simultaneously at each instant of time, the selection of the optimal path gets updated using the grey wolf optimization algorithm. The process of the grey wolf optimization algorithm is elaborated in the following section.