Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

TL;DR

This paper presents a low-cost, fault-tolerant middleware using Raspberry Pi devices and MapReduce to monitor indoor crowds, track visitors, and assist in evacuation, demonstrating comparable performance to resource-heavy systems.

Contribution

It introduces a novel, distributed middleware architecture employing inexpensive hardware and fault-tolerant algorithms for indoor crowd monitoring and data analysis.

Findings

Middleware performs comparably to resource-intensive methods.

System effectively tracks visitor counts and occupancy.

Tested successfully in a Greek historical building.

Abstract

This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design's innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis' residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.