Opportunistic Protocols for People Counting in Dynamic Networks

Abstract

In the modern world, accurate crowd counting is integral to a multitude of applications, including urban planning, transportation management, and crowd control. The advent of opportunistic communication networks, which enable devices to sporadically exchange data in a decentralized fashion, has introduced a new set of challenges in crowd estimation. This paper delves into two opportunistic people counting protocols: UrbanCount and HeartBeatCount. UrbanCount, while a robust protocol in its own right, comes with certain limitations that hinder its real-world applicability. In response to these limitations, this paper introduces refinements to UrbanCount, making it more practical and effective. Additionally, a novel protocol called HeartBeatCount is presented, which significantly enhances crowd size estimation accuracy, particularly in sparse scenarios. Through an evaluation, we compare the performance of these protocols and conclude that HeartBeatCount offers a more resilient solution for opportunistic people counting in various real-world scenarios.