## 2 Related Works
LDORRO [25] refines LORA by dynamically segmenting the candidate area and implementing predefined routing rules, yet its static duty cycle configuration lacks adaptability to network topology changes. TORAS [26] improves reliability byincorporating a trust model for forwarder selection but requires extensive global network information, resulting in high computational overhead. Recently, [PERSON] et al. [52] proposed a dynamic OR protocol leveraging an analytic hierarchy process (AHP) and fuzzy inference system (FIS) to adjust duty cycles based on residual energy, traffic conditions, and link quality. While this approach enhances network longevity, its high computational complexity limits feasibility in resource-constrained WSN environments. Furthermore, scalability issues remain unaddressed, raising concerns about its applicability in large-scale deployments. Most existing OR protocols adopt either centralized or distributed approaches to compute routing metrics, both of which incur high overhead costs. To address these limitations, our proposed protocol introduces a lightweight, local computation-based method that eliminates the need for explicit neighbor probing or additional control packet exchanges. By embedding routing metric recalculations within transmitted packets, our approach enables dynamic adaptation without global state awareness. Moreover, by employing soft clustering techniques, forwarder selection is optimized to prioritize low-density areas, thereby reducing redundant transmissions, minimizing collisions, and improving energy efficiency in opportunistic duty-cycled WSNs. Although fuzzy clustering has been used in WSNs before for topology control and data aggregation, our work stands out because it only uses local FCM clustering for dynamic forwarder selection. On-demand reactivity without centralized control is made possible by this clustering, which is localized to one-hop neighborhoods rather than global ones. Additionally, unlike protocols like LORA [23] or MORR [9], we do not rely on centralized re-clustering or heavy computation. Comparing FCM-OR to more varied classes of existing protocols, like LORA [23], LDORRO [25], and LEOR [24], that employ geometric or trust-based forwarder selection strategies would help to shed light on the novelty of our method. Table 1 presents a concise summary of previous related works within the scope of our protocol research area and provides a brief comparison with the proposed protocol. For now, in this study, we have implemented and compared our proposed protocol with two well-known protocols in the opportunistic routing field, ORW [3] and ORR [7]. ORW and ORR have been used by other protocols such as ORD [8], MOR [51], and MORR [9], and our work will be empirically compared to those models in future iterations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Routing Metric** & **Addressed Issues** & **Advantages** & **Drawbacks** \\ \hline \multirow{3}{*}{ExOR [1]} & \multirow{3}{*}{ETX} & \multirow{3}{*}{Packet loss rate} & Increased throughput, reduced unnecessary retransmissions & Did not consider energy efficiency of nodes \\  & & & & Introduced EDC, unique forwarder selection, improved throughput and energy, handles link dynamics & Recursive calculation, ignores load balancing, high overhead, recalculation only on topology changes \\ \hline \multirow{3}{*}{ORD [8]} & \multirow{3}{*}{EDC + Residual energy + Hop-count} & Energy usage, latency, sender wait time, transmission count & Supports in-network aggregation, fewer transmissions, longer network lifetime, tradeoff between delay and energy \\  & & & & \\ \hline \multirow{3}{*}{ORR [7]} & \multirow{3}{*}{FS + Residual energy} & Link quality, multiple receivers, load balancing, sender wait time & Balances energy among forwarders, reduces duplication, improves lifetime and delay & Periodic FS computation by sink, high control overhead \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of previous related works in the area of opportunistic routing in WSNs.