论文标题
人群跟踪和监视中间件通过地图还原
Crowd tracking and monitoring middleware via Map-Reduce
论文作者
论文摘要
本文介绍了一种新颖的分布式耐故障中间件的设计，实现和操作。它使用互连的WSN来实现MAP-REDUCE范式，由几个低成本和低功率迷你计算机（Raspberry PI）组成。具体来说，我们解释了开发新手，容忍故障的地图算法的步骤，该算法可实现高系统可用性，重点是网络连接。最后，我们展示了基于模拟数据的拟议系统的使用，以在实际情况下进行人群监控，即希腊的一栋历史建筑（M. Hatzidakis的住所）。本文的技术新颖性在于，不使用复杂和资源的AI结构和视频识别和视频和视频认识和图像和视频技术来提供可行的低速和低功率解决方案。
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.
