Revolutionizing E-Commerce With Consumer-Driven Energy-Efficient WSNs: A Multi-Characteristics Approach

Abstract

In the context of the Internet of Things (IoT), wireless sensor networks (WSNs) play a significant role by collecting and analyzing real-time data on consumer behavior, product availability, and other environmental factors, thus enhancing e-commerce operations. Researchers in the domain of consumer electronics are working actively to increase the sustainability of WSNs and decrease their ecological footprint by developing energy-efficient algorithms, data compression techniques, and sensor designs. These advancements are directed toward optimizing energy consumption and minimizing environmental consequences while ensuring the smooth functionality of IoT applications. Organizations are emphasizing the need for collaboration between designers, business organizations, developers, and regulatory authorities to implement eco-friendly industrial laws to enhance the effectiveness of the e-commerce industry. Decision-making in the e-commerce sector is complicated by information overload, subjectivity of preferences, trust issues, product variety, lack of personalized assistance, return and exchange concerns, dynamic pricing and discounts, limited sensory experience, and complex decision-making processes. To address these challenges, an entropy-based multicriteria decision-making (MCDM) approach is proposed to assist personalization in e-commerce and improve user interfaces, recommendation systems, product information transparency, and consumer trust. The utilization of the MCDM technique facilitates the customization of electronic commerce platforms, whereby affordable products can be identified and retrieved according to individual user preferences. The study investigates the impact of electronic gadgets and energy-efficient WSNs on inventory management, customer preferences, and online shopping sustainability, highlighting the intricate relationship between data privacy, energy efficiency, and governance. The proposed method is compared to other state-of-the-art methodologies and shown to be effective under various criteria.

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