## 3 Results  
Estimating WSN energy consumption entails calculating node power requirements based on data volume, power consumption rate, and other variables. Data clustering utilizing similarity indices and resource allocation is critical for network smoothness. The dynamic adaptation mechanism must adapt to new requirements to keep the network running in the face of unforeseen changes. Path Selection Algorithms, ACRL, GARA, FLEMA, and ML-DSS are examples of complex technological interactions in modern consumer electronics. This occurs while mixing algorithms. In addition to the device's capabilities, this entails managing complex computer networks. This involvement extends beyond the functioning of the item. In the next section, we discuss how e-commerce platforms have altered corporate procedures and customer purchasing behaviors. This circumstance necessitates using WSNs for reliable data transmission and network administration. The usefulness of AI algorithms in optimizing energy consumption depends mainly on the type and relevance of the datasets employed. Datasets are used that are typical of those found in e-commerce applications; these datasets include things like traffic and energy statistics. These datasets mimic the complex dynamics of an online retail system by including real-time transaction information, a wide variety of user interactions, and traffic surges during peak hours. Figures 9-13 were generated based on the performance of the algorithms in optimizing the energy consumption using the aforementioned datasets. In Table 2, a comparison between various network methods for optimizing wireless sensor networks in e-commerce is presented. The methods evaluated are scored on four categories--Network Stability Score (measured from 1-5, with 1 as the lowest), Energy Efficiency Ratio (0-1, with 0 as the lowest and 1 representing 100% energy efficiency), Operational Robustness (scored from 0-100, with 0 the lowest and 100 the highest), and Adaptability Index (scored 1-5, with 1 the lowest and 5 the highest)--based on which the overall performance of the method is scored from 1-10, with 1 as the lowest and 10 as the highest. The methods tested are commonly used methods for WSN optimization, including AI-Optimized Routing, the Legacy Centralized System, Hierarchical Clustering, the Reactive Protocol, Grid-Based Topology, and the Load-Balancing Algorithm. Based on the analysis, the AI-powered decision support system outperforms other methods in different metrics. It achieves a network stability score of 4.8, an energy efficiency ratio of 0.95, an operational robustness of 95, and an adaptability index of 4.9, resulting in an overall performance rating of 9.7. These findings highlight the superiority of the proposed methodology over previous centralized systems, hierarchical clustering, and other methods, indicatingits potential to revolutionize consumer electronics in e-commerce by efficiently optimizing wireless sensor networks. Optimizing wireless sensor networks would reduce both the time needed to maintain the system and the direct costs of the network for the business. Network lifespan refers to the length of time for which the network is able to maintain full functionality [26]. According to Figure 9 of the network lifespan study, the proposed AI-based solution achieves the highest value of around 88 out of all the techniques that were evaluated. Thus, the suggested approach greatly improves energy efficiency, increasing the network's lifetime. With a 35% performance gain and a decrease of 40% in energy usage, these outcomes stand out when compared to previous approaches. Based on these results, fuzzy logic-controlled energy management, adaptive clustering, and genetic algorithms for resource allocation might transform e-commerce wireless sensor networks, which would improve consumer electronics platforms. Overall, the suggested approach improves the network's performance and efficiency, as demonstrated by the higher network life cycle value.