10.25673/122849">


Proceedings of International Conference on Applied Innovation in IT
2025/12/22, Volume 13, Issue 5, pp.151-159

Comparative Analysis of Consumer Strategies for Real-Time Traffic Graph Updates in IoT Systems


Andrii Liashenko and Larysa Globa


Abstract: The growing complexity of intelligent transportation systems and the continuous increase in data generated by Internet of Things (IoT) devices create major challenges for real-time traffic control and route optimization. Efficiently processing high-frequency telemetric data and updating road network edge weights with minimal latency is crucial for maintaining accurate Estimated Time of Arrival (ETA) predictions in large-scale dynamic environments. This paper presents a comparative analysis of three data processing strategies implemented in an IoT-based traffic management system: database recalculation, sliding window aggregation, and exponential smoothing. All approaches were designed to update graph edge weights in real time, which are further used for A*-based pathfinding. Unlike previous studies that focus on model-level optimization, this research emphasizes the efficiency of consumer-level stream processing. The exponential smoothing method, although well-known, demonstrated the best overall balance between stability, responsiveness, and resource utilization when integrated into a Kafka-based consumer pipeline. Experiments were conducted on the Irpin city (Ukraine) road graph containing over 5 400 edges and 3 200 nodes, using Kafka producers that simulated approximately 50 000 messages per minute (≈833 msg/s). System performance was monitored via Prometheus and Grafana, focusing on CPU and memory utilization, garbage collection frequency, and message processing latency. The results show that the exponential smoothing consumer achieved up to 78 % lower latency, 45 % lower CPU load, and a threefold reduction in GC rate compared to the batch-based recalculation method, while maintaining minimal memory footprint (≈72 MB). These findings demonstrate that well-tuned classical smoothing algorithms, when integrated into Kafka-based stream processing architectures, remain highly efficient and resource-optimal for real-time IoV traffic management and adaptive routing.

Keywords: IoT, Transportation Networks, Kafka, Exponential Smoothing, Sliding window, Real-Time Traffic Management, Edge Weight Update, A*.

DOI: 10.25673/122849

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