## 1 Introduction
This method ensures that the network operates efficiently while consuming little power by adjusting the routing table in response to changes in the network via dynamic optimization. Consumer electronics researchers will uncover fresh information in the study's conclusions, especially about online shopping. AI-driven strategies may help internet service providers (ISPs) boost network speed and control energy use. WSNs require resource management and adaptive clustering as consumer electronics grow more complex [12]. Ultrafast e-commerce requires quick responses to energy and traffic fluctuations. Due to these variables, the suggested technique reduces power consumption and improves data transfer. WSNs in consumer electronics must balance data transmission speeds and power consumption. The proposed solution uses FLEMA and GARA to maximize energy utilization and allocation. These algorithms prioritize energy efficiency and resource allocation. Machine Learning Deep Sleep Scheduling (ML-DSS) may adjust node sleep schedules to network conditions to improve energy efficiency. This theoretical article on AI in wireless sensor networks advances past studies. According to this paper, AI may enhance network efficiency and flexibility. It guides consumer electronics makers in implementing AI-driven approaches in WSNs, which might boost energy efficiency and network performance [13]. The report provides more significant research on AI in consumer electronics, particularly online purchasing. This allows for deeper study into AI algorithms' practical applications, which might lead to greener and more efficient consumer gadgets. E-commerce has advanced using an AI-optimized consumer electronics WSN [4]. AI, IRM, and dynamic adaptation may improve WSNs and reduce energy usage. As a digital product, WSNs will enhance consumer electronics. Consumer electronics use this technology to convey data effectively while utilizing little power. ACRL uses reinforcement learning to prolong the lifespan of consumer electronics networks and minimize energy consumption. ACRL modifies cluster design dynamically for efficient data flow. Nodes may learn new talents and behave better [14]. SIEO controls consumer electronics WSN power utilization via swarm intelligence algorithms. Like natural swarming, this technology utilizes less energy while maintaining network performance, showing how biological notions may be applied to consumer electronics to allow decentralized decision making and self-organization. GARA optimizes WSN resource allocation using evolutionary methods. Consumer gadgets must adapt since they use the network [15]. GARA simulates natural selection to allocate resources and extend network life. FLEMA improves consumer electronics by using fuzzy logic control for adaptive energy management in WSNs. The severe but balanced approach to increasing efficiency and lowering emissions by limiting power usage addresses changeable network circumstances. The Particle Swarm Optimization Protocol (PSOP) improves power consumption in WSNs used in consumer electronics [10; 16]. Imitating the cooperative tendencies of swarming particles improves data flow and resource allocation while decreasing the network power burden. Deep sleep scheduling uses machine learning to enhance the sleep schedules of WSN nodes in consumer electronics. Automatic sleep cycles have been established for historical and present network data structures [17]. These provide for power savings without interfering with node activation. These systems use algorithms to enhance consumer electronics by lowering power consumption or increasing network dependability. Consumer electronics are assisting e-commerce operations in becoming more effective and sustainable by incorporating cutting-edge technologies into WSNs [18]. Table 1 shows a comparative analysis of the different methods of producing energy-efficient WSNs in e-commerce.