## 2 Incorporating OSI Layers in WSN Clustering  
The cross-layer protocol design has become a key focus in recent network research, especially in WSNs, as it plays a crucial role in developing new protocols that address the challenge of energy saving within the constraints of WSNs. By leveraging the cross-layer principle, this approach significantly increases energy efficiency, thereby enhancing overall network performance. [PERSON] et al. (2018) proposed the EQPD-MAC protocol, designed to address energy efficiency and QoS challenges in WSNs. EQPD-MAC integrates prioritized data handling with multi-hop routing to ensure the timely delivery of high-priority packets while conserving energy. The protocol uses an adaptive active/sleep time mechanism to minimize idle listening and reduce energy consumption. By implementing a cross-layer communication strategy with the AODV routing protocol, EQPD-MAC supports four levels of packet priority, dynamically adjusting active times based on network load. This ensures prompt delivery of high-priority data packets, enhancing network QoS. Tested with the Castalia Simulator, EQPD-MAC showed significant improvements, reducing sensor node energy consumption by up to 30.3%, per-bit energy consumption by up to 29.6%, and sink node energy consumption by up to 27.4%. It also increased throughput by up to 23.3% compared with other protocols. Key advantages include superior energy efficiency, effective multi-priority packet handling, enhanced throughput, and reduced latency. [PERSON] et al. (2018) proposed a hybrid clustering and routing protocol for WSNs that combines Fuzzy Logic with a Quantum Annealing algorithm (FQA) to enhance network stability and minimize energy consumption. The Fuzzy Inference System (FIS) selects CHs \begin{table}
\begin{tabular}{c c} \hline
**Acronym** & **Description** \\ \hline EEUC & Energy-Efficient Unequal Clustering \\ EOAMRCL & Energy Optimization Approach based on MAC/Routing Cross-Layer \\ EQPD-MAC & Multi-hop routing \\ FL & Fuzzy Logic \\ FIS & Fuzzy Inference System \\ FND, HND, LND & First Node Dead, Half of Nodes Dead, Last Node Dead \\ FQA & Fuzzy Logic with a Quantum Annealing Algorithm \\ GCRAD & Cross-layer Routing for Disaster \\ CCWGC & Greedy Coverage Weighted Communication \\ GWO & Grey Wolf Optimization \\ HC & Hill Climbing \\ HEED & Hybrid Energy-Efficient Distributed Clustering \\ IoT & Internet of Things \\ IP & Internet Protocol \\ LEACH & Low-Energy Adaptive Clustering Hierarchy \\ MAC & Medium Access Control \\ NAV & Network Allocation Vector \\ NS2 & Network Simulator version 2 \\ OCCH & Optimized Connected Coverage Heuristic \\ OSI & Open Systems Interconnection \\ OSTS & One Service a Time Scheme \\ OTTC & Overlapping Target and Connected Coverage \\ PHY & Physical Layer \\ PNC & Physical Layer Network Coding \\ QoS & Quality of Service \\ RSSI & Received Signal Strength Indication \\ SA & Simulated Annealing \\ TCP & Transmission Control Protocol \\ TDMA & Time-Division Multiple Access \\ TSLC & Topological Structure by Layered Configurations \\ WSN & Wireless Sensor Network \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._based on residual energy, number of neighbors, distance to the BS, and node centrality. Quantum annealing is used for optimal routing from CHs to the BS. An on-demand re-clustering mechanism reduces computation time and overhead. An energy threshold filters candidate CHs, optimizing the process further. The FQA protocol outperforms peer protocols in energy consumption, network lifetime, number of alive nodes, and throughput. Simulations show that FQA significantly improves energy efficiency and network performance by leveraging fuzzy logic for CH selection and quantum annealing for routing. [PERSON] et al. [24] employed Simulated Annealing (SA) to tackle energy loss and node placement in WSNs, focusing on optimizing topology control, power management, and packet transmission.