## 3 Proposed Model
### Model Phases
#### 4.1.6 Impact of User Population on Group Dynamics

Increasing the number of users may lead to changes in group dynamics, such as higher pedestrian traffic in urban areas or increased tram frequency. Additional groups may be introduced to represent new users or communities joining the network, affecting encounter probabilities and routing decisions. The simulation parameters, including group sizes and mobility characteristics, may be adjusted to reflect changes in the user population and its impact on network behavior. #### 4.1.7 Selection of Destination Node

The destination node for each message may be selected based on various factors, including the intended recipient, geographic proximity, or specific routing criteria. In the DakNet scenario, destination nodes could represent end-users, service points, or central hubs within each city. The routing algorithm may consider factors such as encounter history, message content, and network conditions when selecting the destination node for message delivery. #### 4.1.8 Simulation Parameter Selection

Simulation parameters are selected based on the characteristics of the DakNet scenario and the objectives of the study. Parameters such as node mobility, message generation rate, message size, and simulation duration are chosen to replicate real-world conditions and evaluate routing performance effectively. Selection criteria may include relevance to rural communication scenarios, scalability, and the ability to capture key network dynamics and metrics of interest. #### 4.1.9 Latency

Latency is defined as the time taken for a message to reach its destination from the time it is generated or sent. In the context of the simulation, latency includes transmission delays, propagation delays, queuing delays, and processing delays. The latency metric provides



### Varying the number of users. As illustrated in Figure 7, the proposed algorithm maintains the highest delivery probability as the number of users increases. For 245 users, the proposed algorithm with number of copies set to 0 has 0.66 delivery probability. It is shown that increasing the number of copies to sixincreases the delivery probability to 0.738. Please note that we chose the values of 125, 185, and 245 users to represent a range of user populations that are relevant to the DakNet scenario and cover a reasonable spectrum of network sizes. These values were selected based on considerations such as the capacity of the simulation environment, computational resources, and the desire to capture significant changes in performance metrics with relatively few data points. Figure 8 shows that the overhead ratio is low and challenging with respect to that of other algorithms. Figure 9 shows that the latency is acceptable with respect to other that of algorithms. The demonstration of a low and challenging overhead ratio for the proposed algorithm relative to that of other algorithms in Figure 8 signifies its efficiency in

Figure 5: Latency with respect to number of messages. Figure 6: Overhead ratio with respect to number of messages. resource utilization and management. Overhead ratio reflects the amount of additional data or processing required by the algorithm to facilitate message delivery, including control messages, routing updates, and protocol overhead. A low overhead ratio is desirable in network protocols as it minimizes resource consumption, reduces network congestion, and enhances overall system scalability and efficiency. The superior performance of the proposed algorithm in maintaining a low overhead ratio suggests its effectiveness in optimizing resource utilization and minimizing unnecessary network overhead, contributing to improved network performance and sustainability. The indication of acceptable latency for the proposed algorithm relative to that of other algorithms in Figure 9 highlights its ability to maintain an efficient message delivery speed and network responsiveness.