Crowd tracking and monitoring middleware via Map-Reduce

TL;DR

This paper introduces a fault-tolerant, low-cost Map-Reduce middleware using Raspberry Pi devices for crowd monitoring, emphasizing network connectivity and system availability in a real-world historical building scenario.

Contribution

It presents a novel, low-power, fault-tolerant Map-Reduce algorithm for crowd sensing that does not rely on complex AI or image recognition techniques.

Findings

Achieved high system availability in crowd monitoring

Demonstrated effective operation with simulated data in a real scenario

Provided a low-cost, resource-efficient crowd sensing solution

Abstract

This paper presents the design, implementation, and operation of a novel distributed fault-tolerant middleware. It uses interconnected WSNs that implement the Map-Reduce paradigm, consisting of several low-cost and low-power mini-computers (Raspberry Pi). Specifically, we explain the steps for the development of a novice, fault-tolerant Map-Reduce algorithm which achieves high system availability, focusing on network connectivity. Finally, we showcase the use of the proposed system based on simulated data for crowd monitoring in a real case scenario, i.e., a historical building in Greece (M. Hatzidakis' residence).The technical novelty of this article lies in presenting a viable low-cost and low-power solution for crowd sensing without using complex and resource-intensive AI structures or image and video recognition techniques.