## 2 Materials and Methods  
An innovative technique for optimizing WSNs, critical to the consumer electronics sector, mainly e-commerce, blends AI, IRM (Inverse Rank Mean), and MSN (Maximum Signal-to-noise Ratio). This technique saves electricity and improves network efficiency, making it vital. This method is incredibly complicated, from designing unique network settings for each node to thoroughly evaluating the route's usefulness, including energy consumption, data transmission speed, path stability, and other factors. Our method relies on the Path Selection Algorithm [19]. This dynamic algorithm varies with network history. It updates the routing database and optimizes routes for speed and efficiency. Controlling WSN energy and data utilization requires adaptive clustering. This process builds and optimizes node clusters for network architecture and data flow. Depending on data speed and energy use, dynamically adding and removing clusters increases network flexibility. A genetic algorithm for resource allocation will boost system capacity. Evolutionary techniques for adaptive resource allocation boost network efficiency and reduce power use. Effective resource allocation may improve network performance and sustainability [19; 20]. GARA's resource identification, allocation, and real-time network data-driven modification guarantee this. FLEMA dynamically controls WSN energy usage. Combining FLEMA and GARA provides optimal results. This algorithm excels at responding swiftly to changing network traffic, making real-time network dynamics choices, and minimizing energy usage to balance efficiency and conservation. Energy management flexibility is essential for network efficiency in the fast-changing consumer electronics industry [21]. Furthermore, machine learning deep sleep scheduling (ML-DSS) is a novel wireless sensor network energy management method. ML-DSS adjusts network node sleep times using machine learning to preserve energy and maintain responsiveness. To implement this technique, energy resource management must be proactive. It controls sleep cycles by monitoring network performance and data. This reduces power usage by only activating nodes when required. The suggested approach uses cutting-edge algorithms and methodologies due to the increasing complexity and interconnection of digital devices and networks. Featuring consumer electronics technology is a credit to the industry. It redefines e-commerce network WSN optimization and delivers continuous performance [22]. It uses resource allocation, dynamic flexibility, and intelligent decision making. The proposed technique improves key WSN capabilities over prior attempts to optimize them for consumer devices, such as for online shopping applications. It highlights a device's capacity to operate in complex networks beyond its present technological use. This tech innovation is better than others because multidimensional WSN technology solves WSN problems in the fast-paced e-commerce business [23]. The system includes resource allocation, adaptive clustering, sleep scheduling, and efficient routing. ML supports these statements. This comprehensive approach makes consumer electronics part of complex and intelligent networks. The real-time optimization of these networks signals a watershed moment in using AI in consumer electronics and e-commerce. The three key elements driving the proposed technique for decreasing power consumption and improving network performance are artificial intelligence, intelligent resource management, and dynamic adaptability. The algorithm continues, as depicted in Figure 2, to arrive at and compute a route efficiency measure that considers energy consumption, data transmission speed, and path stability. This measure evaluates the practicality of each alternative route to facilitate real-time information flow throughout the network while accounting for the nodes' energy restrictions. This comprehensive and rigorous WSN routing solution benefits consumer electronics online companies by regulating energy costs and network performance.