Benchmarking Context-Aware Services for IoT-driven Transportation Systems

In the fast-growing realm of smart cities, integrating Internet of Things (IoT) devices into transportation systems is essential for improving efficiency and safety.Deploying these systems in realworld settings demands access to contextual data, and middleware systems to facilitate the exchange of both contextual and IoT data.Existing IoT-based data exchange systems such as Orion-LD, Stellio and Scorpio in the FIWARE space, support the modeling and representation of both context and IoT systems.This paper introduces a comprehensive testbed and a benchmarking platform designed to evaluate the performance of FIWARE context-aware brokers.The testbed incorporates real data from a real Bus Transportation Service in the city of Ioannina, Greece, as well as synthetic data enabling a realistic assessment of query and ingestion performance.The results show that microservices-based architectures like Stellio and Scorpio scale better than traditional designs like Orion-LD under high loads, but all brokers perform similarly at low loads.Furthermore, temporal queries present challenges for IoT applications due to their high cost across all evaluated brokers.However, writeoptimized data stores offer an advantage by improving ingestion speed.The paper emphasizes the importance of understanding and addressing the operational inefficiencies of context-aware brokers to improve IoT system performance.Overall, this work introduces a novel benchmarking platform for smart transportation systems, featuring a realistic testbed with both real and synthetic IoT datasets, as well as detailed experimental results that identify key performance bottlenecks and offer potential optimization strategies.