## 4 Discussion  
In consumer electronics, improved artificial intelligence algorithms for WSN management in e-commerce applications are a significant advancement. The results of this study support the findings of other studies, such as [27; 28; 29], which suggest that an artificial intelligence algorithm has the potential to increase the efficacy of wireless sensor networks. This technique improves WSN effectiveness, adaptability, and performance for consumer products, focusing on e-commerce applications. The network dynamically transfers data and regulates energy use using AI algorithms, enabling data mobility and energy management. Dynamic data loads in consumer electronics need simple node mobility, improving network efficiency and flexibility. ACRL uses less power and processes data better than static clustering systems. Data and energy management are necessary for ACRL, a practical suggestion for consumer electronics. ACRL finds its applications in \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Network Stability Score** & **Energy Efficiency** & **Operational** & **Adaptability Index** & **Overall Performance** \\  & **(1-5)** & **Ratio** & **Robustness (1-100)** & **(1-5)** & **(1-10)** \\ \hline Proposed Method & 4.8 & 0.95 & 98 & 4.9 & 9.7 \\ \hline AI-Optimized Routing & 4.2 & 0.85 & 88 & 4.5 & 8.5 \\ \hline Legacy Centralized System & 3.5 & 0.70 & 75 & 3.2 & 6.5 \\ \hline Hierarchical Clustering & 2.8 & 0.65 & 60 & 2.5 & 4.5 \\ \hline Reactive Protocol & 3.0 & 0.70 & 70 & 3.0 & 5.5 \\ \hline Grid-Based Topology & 2.9 & 0.65 & 65 & 3.1 & 5.0 \\ \hline Load-Balancing Algorithm & 3.1 & 0.70 & 72 & 3.0 & 6.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative performance analysis of different network methods across multiple metrics. routing--specifically, in identifying the ideal paths for communications between a node and the sink node it is connected to [27]. GARA dynamically assigning resources to maintain network performance is an improvement over earlier methods. Consumer electronics devices and network performance depend on resource management. Genetic algorithms are useful to fine-tune a sensor network, allowing the network to maintain the required properties for the purpose it is designed for while reducing the energy costs of the sensor network, thus extending the lifetime of the network [28]. Additionally, Genetic Algorithms can be used to maintain the minimum energy costs of the system as the size of the system is scaled up, particularly in the case of cloud computing [30]. FLEMA increases WSN energy management flexibility [5]. It makes real-time judgments using fuzzy logic. This strategy may enhance home equipment energy efficiency compared to rule-based alternatives. Additionally, algorithms based on fuzzy logic also improve the overall performance of the individual network nodes [29]. This makes fuzzy logic extremely useful in event-detection problems where a simple rule-based scenario may not be as useful, such as in the detection of fires in mines based on environmental data [31; 32]. DSS adjusts network node sleep time based on data trends, eliminating reactive sleep scheduling. Thus, reactive sleep scheduling is unnecessary. The power economy makes power consumption reduction a priority for consumer gadgets [22]. DSS allows this without affecting network responsiveness. As one of the biggest limitations of WSNs is their limited battery time, the usage of the Deep Sleep Scheduling algorithm can extend the operational lifetime of wireless sensor nodes, thus reducing the need for replacement of the nodes over time, as well as allowing for reduced general maintenance of the network. As a result, the overall efficiency of the network can be greatly increased through adoption of this algorithm [27]. Other models can predict and alter in real time. The suggested technique differs from consumer electronics items in real-time adaptation and predictive modeling. This is a significant advance over manual operations and rudimentary network management.