Distributed face-based tracking with prediction trees in internet of things

Large-scale mobile target tracking in the Internet of Things (IoT) faces substantial challenges due to its centralized architecture, high communication delays, energy limitations, and inaccurate path predictions. This paper presents a novel distributed tracking approach, the IoT-based Face-Based Routing Protocol (IFBRP), which employs prefix trees for path prediction. IFBRP enhances coordination through regionalization, path forecasting via prefix trees, and dynamic sensing radius adjustment driven by learning agents. In contrast to existing centralized methods, IFBRP selects high-energy regional leaders to ensure stable routing. It predicts the locations of mobile targets using historical movement patterns stored in efficient prefix trees, activates nodes only in predicted regions, and adaptively adjusts the sensing range to optimize energy efficiency. Extensive OPNET simulations demonstrate that IFBRP outperforms the advanced FTCCOA protocol, achieving a 64.13% reduction in energy consumption, a 54.61% decrease in end-to-end delay, a 20.77% increase in throughput, a 54.88% reduction in bit error rate, and an 13.41% improvement in signal-to-noise ratio (SNR). These findings make IFBRP highly suitable for tactical aerial surveillance applications that require reliable and energy-efficient tracking of high-speed targets.