## 2 Literature Review  
In this section, we will provide a comprehensive review of the contribution of existing work in designing various schemes to transfer data using the conventional infrastructure and device-to-device (D2D) communication mechanism incorporating human mobility information.  
In today's digitally revolutionized world, the number of smartphone users is increasing exponentially, resulting in a significant rise in mobile traffic [14]. Mobile devices form a wireless mobile ad hoc network (MANET), which offers various advantages in terms of scalability and flexibility. For instance, users can join or leave the network without any prior notification [15]. These mobile device users travel autonomously in different directions, allowing them to connect dynamically. This user mobility renders the MANET environment unpredictable, particularly for real-time data transfer applications [16; 17]. It is important to note that opportunistic data transfer can be a potential characteristic of MANETs. However, the reverse may not always hold true, as not all MANETs rely on opportunistic behavior. MANETs can leverage opportunistic techniques for data transfer in delay-tolerant networks (DTNs), given that they primarily operate based on D2D communication. In [18], the author introduced the use of encounter history and transitivity for the prophet probabilistic routing protocol, based on the assumption of repeated rather than random movement. This approach results in quicker and more effective message delivery [19; 20].  
The probabilistic metric predicts an individual's known destination by formally calculating each node before sending any message [15]. The probabilistic metric can be defined as \(P[a,b]\) in the range of \([0,\ 1]\) of two nodes, such as node A and node B. These nodes typically exchange probabilistic information with each other in a vector form upon each encounter [16]. Data are transferred to a neighbor node based on the high probability of them reaching the distinction; otherwise, the data are stored and transfer is refrained. The data caching in the ProPhet algorithm is implemented by using the FIFO queuing mechanism, which may result in the consistent dropping of the packets [17]. Another area for improvement in the protocols mentioned above is their slow and inefficient response to the dynamic trends in node movement. The main advantage of the protocol is that it uses the node's delivery predictability information to choose the best carrier for message delivery [18].  
ProPHET+ utilizes parameters such as buffer capacity, location, power, and popularity to select the optimal next hop. Normalized weights are assigned to these parameters using values from the range \([0,\ 1]\)[21]. These normalized weights and parameters are embedded in a metric to determine the next hop. Another routing protocol, based on historical information, was proposed by [PERSON] et al. [22] for Opp-Nets. This protocol uses the current node context to select the ultimate path, which primarily consists of the identity table (IT) and history table (HT) to store the local context upon each encounter; additionally, the current context (CC) is updated. The collected information is monitored using continuity probability (Pc), redundancy (R), and heterogeneity counters. While HBOp stores all possible information, it faces memory capacity issues due to the requirements of the aforementioned counters.  
[PERSON] et al. [23] proposed an extended epidemic routing protocol known as the spray-and-wait protocol. This protocol primarily addresses congestion control caused by data flooding in the network. The proposed protocol operates in two phases: spray and wait. During the spray phase, the spread of the message is controlled by limiting its dissemination to specific nodes. In the wait phase, the relay node holds a copy of the message until it can directly transmit it to the destination.