标题： 通过物联网云计算中间件实现智能人群监控 显示英文标题

标题： Streamline Intelligent Crowd Monitoring with IoT Cloud Computing Middleware

摘要： 本文介绍了一种新型中间件，该中间件利用成本低廉、低功耗的计算设备，如Raspberry Pi，来分析无线传感器网络（WSNs）的数据。 它适用于室内环境，如历史建筑和博物馆，用于跟踪访客并识别兴趣点。 通过监控人员数量和评估特定区域、主题或艺术展览的受欢迎程度，该中间件可作为疏散辅助工具。 中间件采用MapReduce算法的基本形式来收集WSN数据，并将其分布在可用的计算机节点上。 由访客徽章上的RFID传感器收集的数据存储在展览室内的微型计算机中，然后在预设的时间段后传输到远程数据库。 利用MapReduce进行数据分析以及领导选举算法实现容错性，该中间件通过指标展示了其可行性，展示了快速原型设计和准确验证结果等应用。 尽管使用了更简单的硬件，其性能与涉及音视频和人工智能技术的资源密集型方法相当。 该设计的创新之处在于其使用经济实惠、低功耗设备而非资源密集型硬件或方法的容错分布式设置。 已在希腊的历史建筑（M. Hatzidakis的住所）成功测试，适用于室内空间。 本文将其算法应用层与其他实现进行了比较，突出了其技术优势和优点。 特别是在新冠疫情之后以及一般性的室内位置监控中间件中，该中间件在跟踪访客数量和整体建筑占用情况方面具有前景。 显示英文摘要

摘要： 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.