Crowd tracking and monitoring middleware via Map-Reduce. Issue 3 (4th May 2022)

• Record Type: Journal Article
• Authors: Gazis, Alexandros; Katsiri, Eleftheria
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 37:Issue 3(2022)
• Page Start: 333
• Page End: 343
• Publication Date: 2022-05-04
• Subjects: Map-Reduce -- distributed fault-tolerant algorithm -- wireless sensor networks -- internet of things (IoT) -- middleware -- distributed sensing system; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2022.2034163
• Languages: English
• ISSNs: 1744-5760

Abstract

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: UF0001