## 2 Incorporating OSI Layers in WSN Clustering
's research highlights the importance of interlayer strategies in WSNs, demonstrating that CCBE's approach effectively addresses energy dissipation, collision prevention, and balanced energy consumption, thereby extending the lifespan of sensor networks. [PERSON] et al. [34] tackled optimal sensor placement in WSNs to enhance sensing coverage, considering sensor limitations like energy, communication distance, and sensing range. They used a variant of the Ant Colony Optimization (ACO) algorithm, known as the Binary Ant Colony Algorithm (BACA), and integrated it with Hill Climbing (HC) and Simulated Annealing (SA). BACA makes binary path decisions (0 or 1) to optimize sensor configuration and coverage. The algorithm updates solutions based on previous solution quality, with better paths having their pheromone trails strengthened. To improve performance in large, complex search spaces, BACA is integrated with HC, which iteratively compares adjacent solutions, and SA, which reduces the probability of accepting worse solutions over time. This combined approach enhances solution exploration and helps achieve a global optimum, optimizing sensor placement for better coverage. [PERSON] et al. (2019) presented an energy-efficient clustering and routing mechanism for WSNs using a hybrid approach combining Chaotic Genetic Algorithm (CGA) and GWO, named CGA-GWO. This method aims to minimize overall energy consumption, which is crucial for extending the network's operational lifespan. CGA-GWO leverages the strengths of CGA and GWO to select energy-aware cluster heads and establish optimal routing paths. Extensive simulations compared CGA-GWO to other systems, evaluating metrics such as the number of live nodes, average remaining energy, packet delivery ratio, and overhead. Results showed the CGA-GWO outperforms other systems in energy efficiency and network lifetime. It maintains a higher number of live nodes, better average remaining energy levels, improved packet delivery ratio, and reduced overhead in cluster formation and routing, demonstrating its effectiveness and efficiency in WSNs. The collection of research works reviewed (Table 2) underscores the importance of cross-layer optimization and the strategic integration of multiple OSI layers to enhance the performance and energy efficiency of WSNs. These studies reveal common features and techniques that collectively contribute to the advancement of WSN technology. A recurring theme across these works is the emphasis on energy efficiency, which is paramount given the limited power resources of WSN nodes. By leveraging cross-layer designs, protocols can optimize energy usage by coordinating functions across the physical, data link, and network layers. This holistic approach allows for more informed and effective decision-making, as it considers the interactions and dependencies between different layers of the network stack. One of the key techniques highlighted is the use of adaptive duty cycles and sleep schedules managed by the MAC layer. Protocols such as EQPD-MAC and CL-MAC incorporate dynamic scheduling to ensure nodes spend minimal time in active states unless necessary. This technique significantly reduces idle power consumption and extends the overall network lifespan. The integration of these schedules with routing decisions ensures that energy savings do not compromise data transmission reliability or latency. Another common technique is the strategic selection of CHs and relay nodes based on multiple criteria, including residual energy and distance to the BS. The protocols CCBE and GCRAD exemplify this approach by evaluating nodes' energy levels and proximity to optimize data routing paths and reduce energy expenditure. This method ensures balanced energy consumption across the network, preventing early depletion of nodes and maintaining network functionality over a longer period. The importance of robust and reliable data transmission is also a focal point in these studies.