Mobility Stream Processing on NebulaStream and MEOS

TL;DR

NebulaMEOS integrates a spatiotemporal processing library with a scalable IoT data system to enable real-time analysis of moving object data, demonstrated through train tracking and environmental monitoring.

Contribution

This paper introduces NebulaMEOS, combining MEOS with NebulaStream to facilitate real-time spatiotemporal analytics for IoT streaming data on moving objects.

Findings

Successful real-time processing of train location data

Effective geofencing and event detection demonstrations

Enhanced capabilities for in-situ IoT spatiotemporal analytics

Abstract

The increasing use of Internet-of-Things (IoT) sensors in moving objects has resulted in vast amounts of spatiotemporal streaming data. To analyze this data in situ, real-time spatiotemporal processing is needed. However, current stream processing systems designed for IoT environments often lack spatiotemporal processing capabilities, and existing spatiotemporal libraries primarily focus on analyzing historical data. This gap makes performing real-time spatiotemporal analytics challenging. In this demonstration, we present NebulaMEOS, which combines MEOS (Mobility Engine Open Source), a spatiotemporal processing library, with NebulaStream, a scalable data management system for IoT applications. By integrating MEOS into NebulaStream, NebulaMEOS utilizes spatiotemporal functionalities to process and analyze streaming data in real-time. We demonstrate NebulaMEOS by querying data streamed from edge devices on trains by the Soci\'{e}t\'{e} Nationale des Chemins de fer Belges (SNCB). Visitors can experience demonstrations of geofencing and geospatial complex event processing, visualizing real-time train operations and environmental impacts.