Congestion Control of Large-Scale Elevator Terminal Data Access in Large Metro Stations Based on The Internet of Things

Abstract

Large metro station IoTs used to face congestion while access to terminals was going on a large scale. Due to this, low success rate in access and delay in monitoring critical equipment was observed, which included elevators and escalators. This paper presented a congestion control method for large-scale elevator terminal data access in metro stations using IoT. Business data were categorized based on volume and latency requirements: Slot ALOHA (SA) direct access mode was used for delay-insensitive, small data services, and Access Class Barring (ACB) random access was used for time-sensitive, large data services. ACB control parameters were dynamically adjusted by estimating access requests. Using uniform and Beta distribution models, the method's effectiveness was validated through experiments. With 4000 access requests, the hybrid method achieved a 52.43% success rate and a 76.72 ms average delay under the uniform model, and a 42.07% success rate with an 82.02 ms average delay under the Beta model. These results demonstrated the method's ability to meet Quality of Service (QoS) requirements for high-priority services, ensuring efficient and reliable communication in large-scale IoT environments.