Taxonomy of Cluster-Based Target Tracking System in Wireless Sensor Networks

Abstract

Background: In the last decade, with the advancement of big data technology and the internet of things, Wireless Sensor Networks (WSN) have become fundamental for the success of a different range of applications especially those demanding massive data processing. Objective: This paper investigates several tracking methods to introduce a novel cluster-based target tracking analysis model. Methods: Some crucial factors of the cluster-based routing protocols are demonstrated, and a comparison among these different methods is conducted according to our taxonomy, such as cluster formation, predicate/proactive, target speed, single or multi-object tracking, boundary problem, scalability, energy efficiency, and communication cost. This can help the community of researchers by providing clear information for further study. Results and Conclusion: The proposed paper compares the differences and similarities between the available approaches across different categories in terms of the cluster construction, clustering method, object speed, number of objects, boundary problem, and scalability. Finally, we can recognize some open issues that have so far gained little attention or are unexplored yet.