TRACE: Transformer-based continuous tracking framework using IoT and MCS

Abstract

Target tracking, a critical application in the Internet of Things (IoT) and Mobile Crowd Sensing (MCS) domains, is a complex task that involves the continuous estimation of the positions of an object by using efficient and accurate algorithms. Some potential applications of target tracking include surveillance systems, asset tracking, wildlife monitoring, and cross-border security. The existing target tracking solutions are either energy-inefficient or are only effective for fixed-length trajectories, making them impractical for real-world applications. For robust predictive tracking, with irregular trajectory lengths, energy efficiency and accuracy are vital to ensure system’s longevity and reliability. In this work, using a combination of trajectory prediction and path correction techniques, a novel approach, TRACE, is proposed for continuously tracking a target in an environment. TRACE uses locations offered by IoT/MCS localization systems to make predictions about the target’s future movement. A transformer neural network is implemented to learn mobility patterns to predict the target’s future trajectory. To ensure accurate predictions, a path correction mechanism is devised, by updating the predicted trajectory using polynomial regression. Experiments are conducted using a real-world GeoLife dataset to evaluate the performance of the proposed approach. The results demonstrate that TRACE performs better than existing tracking techniques with an improvement in accuracy of about 50% while using 85% less energy, supporting the potential of the proposed approach for enhancing target tracking in IoT/MCS applications.