## 2 Related Work  
[PERSON] et al. [13] introduce k-Means clustering-based routing protocol for opportunistic networks (kROp), a routing protocol for DTNs that leverages k-means clustering. It utilizes network features like contact duration, frequency, buffer occupancy, and delivery predictability for next-hop selection, and whereas kROp demonstrates improvements over other protocols in terms of dropped packets, overhead, and average hop count, its effectiveness might be limited in scenarios with low node mobility due to its assumptions about cluster creation and maintenance. Location-Based Clustering Approach for Next-Hop Selection in Opportunistic Networks (LBC), presented in [14] by [PERSON] et al., identifies cluster points based on human interaction patterns, achieving lower latency compared to existing protocols. However, LBC may incur higher energy consumption due to frequent location updates and cluster computations. [PERSON] et al. [15] introduce Metaheuristic-Based Optimized Opportunistic Routing Protocol (MOORP), a routing protocol designed for Wireless Sensor Networks (WSNs) that utilizes a metaheuristic approach combined with opportunistic routing strategies. MOORP focuses on optimizing data transmission efficiency and reducing energy consumption by selecting optimal forwarder nodes and routes, and whereas it shows potential for DTNs, its complexity and scalability limitations require careful consideration during implementation in resource-constrained DTN environments. The study by [PERSON] et al. [16] presents a novel routing protocol for wireless mobile networks that employs a cluster-based approach with innovative techniques to improve coverage delay performance. It utilizes a sophisticated algorithm considering factors like residual energy, node mobility, and distance to optimize cluster head selection. Additionally, a fuzzy logic system adjusts node transmission power based on network conditions. However, the protocol lacks clear explanations on how the DTN technique integrates with the clustering algorithm, and it also lacks validation through real-world experiments or testbeds, limiting its applicability to DTNs. [PERSON] et al. [17] propose a cluster-based, QoS-optimized OLSR protocol for urban VANETs. The protocol incorporates cluster formation, cluster head election, and QoS-aware routing, prioritizing metrics like bandwidth, delay, and packet loss for path selection. However, it lacks an analytical model or theoretical proof for the protocol's effectiveness, hindering its wider adoption in DTNs. [PERSON] et al. [18] introduce a novel model for clustering and routing VANETs in urban environments. This model comprises two key components: a connectivity prediction method (CP) and a dynamic clustering model (DC). CP forecasts connectivity based on vehicle features, whereas DC forms clusters and selects core nodes as cluster heads, adapting to network changes. This method also includes a routing approach based on DC. However, this approach may introduce overhead and complexity due to prediction and clustering processes, potentially impacting its suitability for resource-constrained DTNs. A clustering-based routing method for vehicular networks called CRLLR is introduced in [19] by [PERSON] et al. Utilizing Ant Colony Optimization (ACO), CRLLR aims to determine optimal routes between communicating vehicles, emphasizing four quality of service (QoS) metrics: reliability, end-to-end latency, throughput, and energy consumption. Although showing potential in delivering dependable and low-latency routing, further research is needed to evaluate its performance in real-world DTN scenarios and compare it with other advanced routing schemes. It is important to assess how CRLLR's reliance on ACO translates to the more intermittent connectivity patterns of DTNs compared to VANETs. [PERSON] et al. [20] introduced the AMRBC, a MAC protocol for VANETs, focusing on stable clustering and communication range to improve network stability.