Crowd tracking and monitoring middleware via Map-Reduce

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/video recognition techniques.

GRAPHICAL ABSTRACT

KEYWORDS:

• Map-Reduce
• distributed fault-tolerant algorithm
• wireless sensor networks
• internet of things (IoT)
• middleware
• distributed sensing system

Acknowledgements

This article details our recent work, as part of an MSc course on distributed systems, at the School of Engineering Department of Democritus University of Thrace, Greece to apply the parallel programming to WSNs. Lastly, the authors are grateful to the civil engineer E. Chamalidou for her assistance in the analysis and the presentation of the floor plans of M. Hatzidakis’ residence, as presented in this publication.

Disclosure statement

No potential conflict of interest was reported by the author(s).