## 5 Experimental Results and Discussion  
The proposed ACO-PT algorithm was simulated using the 3D VANET ns-3.37 simulator. The simulation was conducted on various congested scenarios. The performance of the algorithm was monitored while vehicles were transferred from one unit to another. To effectively manage the mobility of vehicles, the use of wireless-sensor networks that are mobility-aware was necessary. Realistic congested scenarios were employed to accurately assess the impact of the proposed ACO-PT algorithm. VANET is a network without any infrastructure if we consider the network that is produced by automobiles. There is no need for a physical channel for vehicle-to-vehicle communication. No centralized regulatory body is necessary. VANET does not require switches or hubs since hop-to-hop communication is possible. Even though they are situated along the sides of the road, RSUs and TAs are only basic resources. The performance of the proposed ACO-PT algorithm was compared to existing protocols such as ACO-FL, RACO, and AODV-ACO. It was observed that the performance of the proposed ACO-PT algorithm, as well as the competing algorithms, may be affected by the level of mobility and the number of hops involved in the network. WSNs experience several challenges and limitations related to mobility and scalability. To tackle these issues, the energy-efficient border-node medium-access-control (BN-MAC) protocol was employed [32]. The main goal of using BN-MAC was to reduce energy consumption while maintaining high levels of mobility, scalability, and collision avoidance at the second layer. Wireless-sensor networks rely on bidirectional end-to-end dependability to ensure smooth data flow and efficient use of bandwidth. The lack of bidirectional dependability can affect the accuracy of event detection and data collection. To achieve end-to-end dependability, a bidirectional consistent transport system that uses a brief preamble acknowledgment/non-acknowledgment (ACK/NACK) control packet between vehicles was employed. To conserve energy, vehicles communicated with each other using short-range, one-hop communication instead of long-range communication. Multihop communication does not offer significant advantages over single-hop communication and can lead to increased delay as each node stores and sends packets. The simulation scenario included a maximum of 400 vehicles with a transmission range of 45 m placed randomly but in a consistent pattern within a 900 \(\times\) 900 square-meter space. Several testing scenarios were constructed, starting with 50 vehicles. There was no congestion with the utilization of 50 vehicles. Consequently, the number of vehicles was increased in each testing scenario, and congestion was detected with 300 vehicles. Thus, the suggested network could support a maximum of 400 vehicles, demonstrating a realistic situation. The dynamic link-origin-destination-matrix estimate (DLODME) characterizes vehicle traffic in a specific area to analyze transportation demand. DLODME supports modern technologies such as Bluetooth and the Global Positioning System (GPS) and allows for trajectory retrieval. Furthermore, it lowers the number of variables linearly rather than quadratically depending on network size. It also employs vehicle counts perceived by traffic-count sensors; it does not require past origin-destination trip data or any anticipated flow distribution for an estimate. Simulation of Urban Mobility (SUMO) was employed to generate a random Manhattan-grid road network with random traffic flows and traffic signals. The SUMO traffic-simulation power was effectively integrated with NS3 network-simulation capabilities. The Online Vehicular Network Integrated Simulation (OVNIS) [33] platform was used to connect SUMO with NS3 such that the SUMO scenario was included in the NS3 mobility model. The NS3 module affected traffic simulation by rerouting vehicles in the mobility model. OVANIS addressed this issue by creating a submodule in NS3 utilizing the TraCI interface.