## 1 Introduction
Consumer technology advancements have fueled this trend by making online markets more accessible. These advances have had significant results. Wireless Sensor Networks (WSNs), which enable real-time data transmission and resource management, are critical to this integration. Wireless sensor networks (WSNs) are most suitable for this purpose and can potentially improve the efficacy of e-commerce systems [7]. These networks differ from others in that they can gather, analyze, and disseminate data across several nodes. It is difficult to achieve energy efficiency with wireless sensor networks, as well as performance. The performance of the sensor network is based on its data transmission: both the quality of the data transmitted and the quality of the connections by which the data is transferred. However, continuous data transmission can result in increased energy consumption. As a result, sensors require replacement with greater frequency. In order to reduce the energy consumption of the network, WSNs will often switch off data transmission during periods of low activity, enabling them to preserve battery power. However, disruptions to the sensor network can result in the quality of the transmission being affected, as previous paths to the cluster head are no longer available. Wireless sensor networks have been negatively affected by an inability to balance these two conflicting goals [9]. One method to balance the two conflicting goals is through the usage of Machine Learning (ML)-based algorithms. There have been applications of such algorithms in similarly complex systems, such as fault detection in gearboxes [10]. By allowing the network to adjust which sensors are switched off and which routes are selected, such an algorithm could enable the reconciliation of these seemingly conflicting goals. AI and machine learning have provided a new perspective on optimizing wireless sensor networks (WSNs). AI-powered systems may improve wireless sensor networks (WSNs) by alerting management to changing network conditions, calculating energy usage, and reallocating resources [4]. Resource management is critical because consumer gadgets are significant in e-commerce, characterized by frequent and quick trading. Advanced WSN optimization approaches use various AI techniques, resulting in low-power, self-regulating intelligent networks. This course focuses on fuzzy logic, genetic algorithms, and reinforcement learning. Algorithms such as GARA (Genetic Algorithm for Resource Allocation) and ACRL (Adaptive Clustering for Reinforcement Learning) have significantly increased network lifespan and data transmission efficiency. These findings have been reported. Fuzzy logic-controlled energy management (FLEMA) and deep sleep scheduling (DSS) have improved WSN energy management. Modern consumer gadgets optimize these technologies to improve energy efficiency while maintaining functionality. This study's AI-based decision support system, which employs sophisticated machine learning models and optimization methodologies, can potentially enhance wireless sensor networks (WSNs), notably in e-commerce consumer electronics. We achieve this by using an optimization approach. This technology improves energy efficiency and wireless sensor network performance by combining cutting-edge computational algorithms with thorough data analysis. As consumer gadgets become more reliable and efficient, growing e-friendly e-commerce will be possible. This innovative approach addresses WSN energy management challenges using AI to govern network resources. In the authors' opinion, this is ideal for internet and consumer electronics purchases. This technology for consumer electronics-linked WSNs uses the Path Selection Algorithm for efficient routing. The network parameters for each node are specified, and a route effectiveness statistic is created [11]. This and the following iterations continue until the operation is almost complete. We can select the option that transfers data effectively by comparing each option according to this statistic.