Artificial Intelligence-Driven Sensor Network Approach for Optimizing Halted Product Delivery in E-Commerce Platforms

Abstract

Sensor network architectures incorporated with artificial intelligence are exploited for process automation in e-commerce platforms. Specifically, product management is automated through sale-based tracking, enabled by the rapid exchange of information within the sensor network. A Paused Product-based Data Management Scheme is proposed in this article to track and improve the delivery of halted products through e-commerce platforms. The sensor network architecture performs individual identification and tracking of halted/delayed products at any hub through synchronized data updates. The synchronization of product information, delay time, and its associated paused information is performed by identifying the product delivery time. For this purpose, a deep neural network is employed to compute the difference between actual and paused delivery intervals. The higher the difference, the synchronization and delivery re-initialization processes are through the sensor network’s interconnected data exchange. Besides, the network is trained until the delay time is reduced with the re-scheduled time as the base. This infers precise product tracking under fewer missing order complaints in an e-commerce platform.