High volume geospatial mapping for internet-of-vehicle solutions with in-memory map-reduce processing

With the flourishing of IoV(Internet of Vehicles) technology, location based services need to handle the positional coordinates streaming in continuously from large numbers of vehicles. Across the hundreds of thousands of kilometers of roads, with tens of millions of vehicles on them, it is a significant performance challenge to determine in real time where vehicles are; how quickly and where they are headed; and when, where, and how much congestion can be expected to build as a result. The high volume of data and the rate at which the mappings must be performed require high computational efficiency and avoidance of storage accesses where possible. This paper introduces a Hadoop based approach for handling such large volumes of information. The paper describes a couple of simple adjustments to methods available in JTS and Java AWT that provide efficient mapping between vehicle positions and road segments, shows the importance of secondary sort in achieving the needed computational throughput, and establishes the significant performance benefit to be achieved from in-memory processing. An evaluation using RAF, a lightweight in-memory computing framework, shows the mapping is 20X+ faster than Hadoop approach to achieve results for real-time operation.