## 3 Proposed Heterogeneous Algorithm for Efficient-Path Detection
### Deployment of Pheromone-Termite Algorithm for Vehicle Management  
Routing in urban areas is highly critical because it increases latency and congestion. Consequently, the throughput performance of the network decreases. In this paper, a scalable mobility-aware pheromone-termite analytical model is used to provide robust and faster routing for improved throughput and minimum latency [30]. PT also provides support for network scalability and node mobility. In general, the pheromone-termite algorithm consists of using the process of real termites in search of food.  
Pheromones are chemical compounds produced and released by organisms and are used for communication and coordination within a species. In the case of termites, pheromones are used to communicate information about food sources, potential mating partners, and colony defense. Termites use pheromones to coordinate the behavior of the colony and to maintain the social structure of the colony. For instance, when a termite finds a food source, it will lay down a pheromone trail that other termites can follow to the food source. This operation in termite colonies is a highly sophisticated and coordinated behavior that is essential for the survival and prosperity of the colony.  
Termites use complex pheromone-communication processes. They produce pheromones in specialized glands and release them into the environment. Pheromones are dispersed through the colony and surrounding area through air or by being carried on termites. Other termites detect pheromones through receptors on their antennae, interpreting the pheromone trail as a signal to follow and take appropriate action.  
In our case, the PT algorithm is used for initial distance estimation to generate the path between the source vehicle at the city entrance and the destination at the city exit. PT plays an important role because it assigns a value to each vehicle \(V_{iv}\), which is denoted as:  
\[V_{iv}=E_{(e)}+T_{(V)} \tag{1}\]  
Figure 2: General framework of the proposed algorithm.  
where \(E_{(e)}\) is the value of the trail between the source vehicle and the destination vehicle, and \(T_{(V)}\) is the empirical estimation value of the most efficient path from the source vehicle.  
The PT algorithm determines the most efficient path to reach the destination (vehicle). When the empirical estimation of the ideal path is successful, then the estimated value is properly confirmed.  
The layout design of the vehicle scenario depicted in Figure 3 allows the PT algorithm to initialize \(E_{(e)}\), which shows the estimated value of a given path from the entrance point to the exit point. A priority queue is performed to analyze the frequent choices for the vehicles, from the lowest value to the largest. The priority queue is considered the open set. Vehicles at all steps with lower values than \(V_{iv}\) are unselected from the priority queue and then assigned values of \(V\) and \(T\), and neighboring vehicles are chosen and computed accordingly. As mentioned, the neighboring vehicles are part of the priority queue. Thus, the PT algorithm continues searching until it reaches the vehicle that possesses the smallest value in the priority queue that is less than \(V\).