Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event-filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion, as it not only offers extra information on the described event, but also enhances trust in the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for Internet of Things (IoT)-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. We have used network simulations with realistic urban traces to prove that the three of them can correctly identify between 73% and 100% of false location claims.

翻译：随着智慧城市致力于构建自我监测与响应系统，其部署依赖于通过大规模城市感知实现精细资源监控。海量数据采集使得事件过滤机制的发展至关重要，该机制能够筛选出相关且可信的信息。由于移动事件产生器的普及，位置信息已成为有价值的过滤标准——它不仅为所描述的事件提供额外信息，还能增强对事件源的信任。因此，实施验证位置信息质量的机制迫在眉睫。云架构中缺乏此类策略迫使基于物联网（IoT）的城市服务采用新型通信方案。为满足城市分布式事件系统（DEBS）的定位验证需求，我们设计了三种结合近场通信与云通信的雾计算架构。通过使用真实城市轨迹数据的网络仿真验证，证明这三种架构能正确识别73%至100%的虚假位置声明。