## 2 Materials and Methods
Figure 8 depicts how machine learning determines node sleep durations. Using data and network information, the ML-DSS approach adjusts sleep cycles in real time. The system may be able to save energy while being responsive. FLEMA is a novel energy operations system management approach that combines fuzzy logic-controlled energy management with DSS. A real-time data-reactive energy management system may consider node power use and traffic volume, allowing it to function optimally. This method enables flexible energy control and ensures network operation under all circumstances. ML-DSS uses machine learning and a dynamic sleep scheduling algorithm to plan network node sleep patterns. \[\text{Input\_Features}=\text{Historical\_Data}+\text{Current\_Network\_Condtions} \tag{50}\]  
This equation combines historical data and current network conditions to determine the optimal sleep timing for network nodes. The regression model for predicting sleep duration is  
\[\text{Regression: Output}=\text{Sleep Duration} \tag{51}\]  
This equation indicates that the regression model's output is the predicted sleep duration for a network node. This information optimizes energy consumption by adjusting the node's active and sleep states. Efficient Energy Metric for Routing (ERM):  
Reinforcement Method for Energy Use and Data Transmission Learning Model for Clusters  
The Algorithm to Optimize Energy Use  
Algorithm 4 explains unified WSN optimization for consumer electronics. These equations and algorithms constitute the heart of the proposed technique, carefully intended to enhance energy efficiency inside e-commerce-focused wireless sensor networks via dynamic adaptation, resource allocation, and intelligent decision making, ensuring continuous network performance. The mutually beneficial interaction of the recommended  
Figure 3.0 Intelligent sleep scheduling for energy efficiency. strategy's components makes it appealing. The Path Selection Algorithm is responsible for generating the best possible data routing; ACRL is accountable for cluster formation and energy consumption optimization; GARA is responsible for resource management; FLEMA is responsible for dynamic energy consumption regulation; and ML-DSS is accountable for node activity cycles. In consumer electronics, particularly for e-commerce applications, the proposed technique integrates various advanced algorithms and approaches to maximize network efficiency while minimizing power consumption. This method is highly relevant in today's consumer electronics, where devices and networks are increasingly interconnected and intelligent. GARA utilizes genetic algorithms for adaptive resource allocation based on energy needs. GARA manages WSN resources by exploring the network for available resources and distributing them using different techniques. FLEMA controls network power consumption. It regulates power use and makes real-time choices based on erroneous and changing data to ensure network efficiency. FLEMA tailors its energy usage to the demands of each network to save money and enhance efficiency. ML-DSS uses machine learning to plan node sleep cycles. This strategy lowers power usage while maintaining system responsiveness. It modifies sleep cycles depending on network conditions and data. An efficient energy metric for routing models may improve energy consumption and data transmission efficiency. It improves cluster data processing and routing in response to network changes, increasing productivity and reducing energy consumption. This is an example of an inheritance algorithm. Fitness is critical to the energy efficiency of consumer electronics networks. Based on the statistics, this function minimizes power consumption. An algorithm in a fuzzy logic-based power management system determines the best power use under heavy network traffic to meet demand. For better rest period scheduling, the model recommends that each node sleep for a certain number of hours daily to achieve optimum energy savings and network performance.