Optimum Bi-level Hierarchical Clustering for Wireless Mobile Tracking Systems

TL;DR

This paper introduces a bi-level hierarchical clustering method to optimize energy efficiency and reduce interference in large-scale wireless mobile tracking systems, especially for crowd monitoring at events.

Contribution

It presents a novel ILP-based bi-level clustering technique that improves energy efficiency and tracking accuracy in large-scale mobile crowd monitoring systems.

Findings

The proposed method achieves near-optimal clustering performance.

Simulation results show significant energy savings.

The technique effectively tracks large crowds in real-time.

Abstract

A novel technique is proposed to optimize energy efficiency for wireless networks based on hierarchical mobile clustering. The new bi-level clustering technique minimizes mutual interference and energy consumption in large-scale tracking systems used in large public gatherings such as festivals and sports events. This technique tracks random movements of a large number people in a bounded area by using a combination of smart-phone Bluetooth and Wi-Fi connections. It can be effectively used for monitoring health conditions of crowd members and providing their locations and movement directions. An integer linear programming (ILP) model of the problem is formulated to optimize the formation of clusters in a two-level hierarchical structure. In order to evaluate the proposed technique, it is compared to the optimum solutions obtained from the ILP model for both single-level and two-level clustering. Moreover, a Matlab/Simulink simulation model is developed and used to test the technique performance under realistic operating conditions. The results demonstrate a very good performance of the proposed technique.