# Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)
## 6 Conclusions  
This research proposed a novel Bio-inspired Dynamic Trusted Congestion-Aware Zone-based Internet of Drone Things (BDTC-SIOVAs) to provide trust estimation and congestion control. To secure the data transmission from the network threads, a triple-layer trust model is performed through the dynamic trust estimation method. To improve the network mobility, the congestion-aware community is constructed which predicts the congestion, and to reduce the routing overhead, hybrid optimization is performed. Using these methods, the network is protected from security issues and traffic-oriented issues. The performance analysis is performed using two scenarios, such as (i) the performance analysis with varying speed from 10 m.s\({}^{-1}\) to 35 m.s\({}^{-1}\) and with varying attacks. The performance of the proposed BDTC-SIOVAs is high in terms of parameters such as energy efficiency, packet delivery ratio, and throughput for the other attacks. However, the overall performance is comparatively low for the position falsification attack, which is the major limitation of this research. It needs some more attention in the future. In the second scenario (ii), the performance is calculated in terms of vehicles, and it is compared with the earlier works. The energy efficiency of the proposed BDTC-SIOVAs is 7% to 17% higher, packet delivery ratio is 5% to 14% better, routing overhead is \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **No of** & **LAKA-** & **NCAS-** & **TPDA-** & **BDTC-** \\  & **Vehicle** & **IOD** & **IOD** & **IOV** & **SIODTs** \\ \hline \multirow{8}{*}{E2E\_Delay (m.s\({}^{-1}\))} & 25 & 75.347 & 65.314 & 45.179 & 22.475 \\  & 50 & 112.768 & 99.117 & 62.175 & 35.467 \\  & 75 & 153.668 & 124.871 & 93.412 & 59.476 \\  & 100 & 186.276 & 154.331 & 129.115 & 78.204 \\  & 125 & 201.647 & 189.164 & 154.712 & 91.764 \\  & 150 & 254.317 & 201.433 & 186.346 & 105.706 \\  & 175 & 296.124 & 234.178 & 214.765 & 134.756 \\  & 200 & 346.176 & 264.709 & 243.126 & 153.146 \\ \hline \multirow{8}{*}{Packet Loss (PACs)} & 25 & 134 & 102 & 83 & 31 \\  & 50 & 184 & 163 & 127 & 62 \\  & 75 & 221 & 196 & 164 & 91 \\  & 100 & 286 & 238 & 208 & 143 \\  & 125 & 328 & 263 & 243 & 186 \\  & 150 & 371 & 291 & 275 & 201 \\  & 175 & 406 & 328 & 305 & 251 \\  & 200 & 457 & 361 & 346 & 282 \\ \hline \multirow{8}{*}{Throughput (Kbps)} & 25 & 41 & 127 & 163 & 258 \\  & 50 & 65 & 186 & 227 & 275 \\ \cline{1-1}  & 75 & 128 & 224 & 266 & 329 \\ \cline{1-1}  & 100 & 186 & 286 & 328 & 438 \\ \cline{1-1}  & 125 & 227 & 355 & 386 & 528 \\ \cline{1-1}  & 150 & 251 & 427 & 467 & 637 \\ \cline{1-1}  & 175 & 305 & 465 & 486 & 705 \\ \cline{1-1}  & 200 & 354 & 503 & 542 & 782 \\ \hline \end{tabular}
\end{table} 
Table 7: The values of End-to-End Delay, Packet Loss, and Throughput Concerning vehicles. 1300 packets to 3000 packets lower, end-to-end delay is 90 ms to 200 ms lower, packet loss is around 140 packets to 270 packets lower, and throughput is 240 Kbps to 400 Kbps higher than the earlier methods, such as LAKA-IOD, NCAS-IOD, and TPDA-IOV. The proposed BDTC-SIOVTs is more feasible because of the presence of drones in it; however, the consumption of energy needs to be concentrated further, though it achieved some further enhancement in performance. In future research, the proposed BDTC-SIOVTs will be applied in satellite-assisted drone-enabled VANETs. Formal analysis, Y.S.J. ; investigation, M.J.M.J. ; resources, H.M.A.G. and A.D.A. ; methodology, N.F.S. ; software, W.E.-S.; resources, S.R.M.Z. ; writing--original draft preparation, A.A. and A.S.A. ; validation, N.F.A. ; supervision, writing--review and editing, A.H.A. ; visualization, H.M.H. ; project administration, F.H.A. All authors have read and agreed to the published version of the manuscript. This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Not applicable.