## 3 Proposed Model
### Model Phases
### Simulation Environment

For the simulation of our proposed model, we opted for the DakNet scenario, which replicates a network connecting three cities. Within these simulations, our attention is directed towards evaluating three key parameters: delivery probability, overhead ratio, and latency. To benchmark the efficacy of our proposed algorithm, weconducted comparative analyses against several prominent DTN routing algorithms, all implemented within the ONE simulator. These algorithms include Spray and Wait, Prophet, Epidemic, and Fresh [2; 8; 29]. In this simulation setup, the Ping Pong application plays a pivotal role. It offers configurable options, allowing for the transmission of pings at fixed intervals or solely responding to received pings. When the application receives a ping, it promptly sends a corresponding pong message in return, thereby facilitating communication and interaction within the simulated network environment. The scenario simulates three rural cities lacking communication infrastructure, relying solely on opportunistic networking. Nodes communicate using an Bluetooth interface at a data rate of 250 KB/s with a radio range of 10 m. The decision to use Bluetooth as the physical layer in the described model is motivated by its alignment with the proximity-based, energy-efficient, and human-centric nature of opportunistic networks, as well as its suitability for deployment in dynamic and crowded environments. While other access schemes like 802.11 (Wi-Fi) offer longer communication ranges, Bluetooth's advantages in terms of energy efficiency, interference mitigation, deployment simplicity, and security make it a compelling choice for opportunistic network scenarios. To ensure the validity and relevance of our simulation setup, we adopted several configuration parameters from established research in the field. Specifically, the number of nodes, buffer sizes, transmission ranges, and mobility models were aligned with those used in previous studies to maintain consistency and comparability. More precisely, the number of nodes, buffer sizes, and transmission ranges were set based on the guidelines provided by [30; 31]. The selection of mobility models was learnt by [32]. The message generation process, including message size and inter-arrival time, followed the methodologies outlined by [32]. #### 4.1.1 Group Configuration

Group 1, 2, and 3: Pedestrians with random speeds between 0.5 and 1.5 m/s and pause times between 0 and 120 s. Each group corresponds to one city, with 40 hosts per city. Group 4: Trams moving at speeds of 3-5 m/s with pause times between 10 and 30 s. Group 4 consists of fivenodes. Groups 1, 2, and 3: Up to 5 MB of free buffer space; Group 4: Nodes have 50 MB of free space for relaying messages. #### 4.1.2 Message Generation

A new message is generated on average every 25 to 35 s.

Message size varies between 50 KB and 150 KB, with a time to live set to 300 min. #### 4.1.3 Simulation Duration

Each simulation runs for 43,200 s (12 h). To expand more about the simulation parameters, the following aspects are defined. #### 4.1.4 Movement of Groups

The movement of pedestrian groups (Groups 1, 2, and 3) is characterized by random speeds between 0.5 and 1.5 m/s and pause times between 0 and 120 s. These movements simulate the activities of individuals within each city, such as walking or stopping at various locations. The randomness in speed and pause times reflects the unpredictable nature of human mobility patterns, adding realism to the simulation. #### 4.1.5 Movement of Trams

Trams (Group 4) move at relatively higher speeds of 3-5 m/s with shorter pause times between 10 and 30 s. Their movement typically follows predefined routes within each city, with periodic stops at tram stations. Trams serve as mobile relay nodes in the network, providing additional opportunities for message exchange and routing.