## 1 Introduction  
The rapid growth of e-commerce has fundamentally altered consumer behaviors and business operations, creating a massive demand for reliable data transmission and processing. This shift has highlighted the role of consumer electronics not just as devices for personal use but as integral components in more extensive, interconnected systems like online shopping platforms. Wireless sensor networks (WSNs) can be described as a wirelessly connected network of sensors gathering information to be used by a central controller. These networks feature many sensor nodes and consumer electronics, and WSNs must balance power and utility [1]. Online stores rely on Wireless sensor networks to collect and distribute data over a wireless connection, which is essential for processing orders. Wireless sensor networkswith AI integration are intriguing and more viable than simple WSNs [2]. Using artificial intelligence (AI), WSNs can optimize energy usage by learning, adapting, and making decisions. As a result of this upgrade, both network speed and data transmission efficiency will improve. Consumer electronics have progressed from simple devices to complex systems with self-monitoring and self-control capabilities. Due to this integration, procedures have become faster and more efficient [3]. The research mentioned offers a significant addition to online shopping in the context of electronic commerce by establishing algorithms specially adapted to the needs of WSNs. These algorithms are based on artificial intelligence concepts. These algorithms seek to reduce power consumption while maintaining data transmission speed. These protocols are crucial for e-commerce applications involving consumer devices as they handle challenges like balancing energy saving with operational efficiency, which is critical for the effective operation of certain programs [3]. This study seeks to not only enhance current technology but also reconsider the function of WSNs in the context of electronic commerce. The objective is to considerably reduce energy consumption while ensuring error-free and efficient data transfer. The AI-powered strategy aims to accomplish energy utilization that is well-suited to the shifting needs of online shopping by distributing resources in WSNs as efficiently as feasible [4]. These solutions are centered on developing and implementing sophisticated frameworks for regulating power consumption in WSNs linked to consumer devices. Techniques like machine learning, optimization, and predictive modeling are employed to create dynamic protocols that can adjust to the ever-evolving needs of the online shopping industry [5]. WSNs are in high demand in the ever-changing consumer electronics sector, notably e-commerce. This project will propose a novel technique using AI, Intelligent Resource Management (IRM), and dynamic adaptation to enhance consumer electronics WSN performance and energy efficiency. This study considers each node's energy restrictions to determine the optimum network path for real-time data. WSNs can be applied to various tasks in the field of agriculture. WSNs allow farmers to monitor and control their farms from anywhere in the world through the usage of the zigbee wireless sensor network, reducing the risks faced by the farmers and putting them much more at ease [6]. WSNs provide essential services to e-commerce retailers, especially in the field of logistics. Energy-efficient WSNs can ensure quick and efficient delivery service even in the face of obstacles such as traffic congestion [7]. Additionally, they also provide essential services in the field of cold food chains in the food industry. Wireless sensor networks can be used to identify Critical Control Points (CCPs) based on the temperature data, allowing the companies to more effectively chose between frozen and cold storage, enabling them to obtain a greater degree of efficiency in their operations [8]. Internet shopping has expanded tremendously throughout the digital revolution and is essential to any economy.