Adaptive Traffic Element-Based Streetlight Control Using Neighbor Discovery Algorithm Based on IoT Events
Yupeng Tan, Sheng Xu, Chengyue Su
TL;DR
The paper tackles energy-efficient adaptive streetlight control by automatically learning neighbor relationships between streetlight nodes from IoT event records instead of relying on GPS. It models the streetlight network as a probabilistic social graph and introduces a multi-objective GA-based clustering that uses a novel Speed Consistency Evaluation (SCE) and a DISIM/DIST framework, including solving a traveling salesman problem with the Christofides algorithm to derive within-cluster neighbor sequences. A six-threshold subgraph strategy together with pKwikCluster-based initialization allows automatic cluster-counting without predefining the number of sectors. Experimental results on simulated datasets show improved neighbor discovery accuracy and effective adaptive sector-based lighting, indicating practical benefits for large-scale streetlight deployments.
Abstract
Intelligent streetlight systems divide the streetlight network into multiple sectors, activating only the streetlights in the corresponding sectors when traffic elements pass by, rather than all streetlights, effectively reducing energy waste. This strategy requires streetlights to understand their neighbor relationships to illuminate only the streetlights in their respective sectors. However, manually configuring the neighbor relationships for a large number of streetlights in complex large-scale road streetlight networks is cumbersome and prone to errors. Due to the crisscrossing nature of roads, it is also difficult to determine the neighbor relationships using GPS or communication positioning. In response to these issues, this article proposes a systematic approach to model the streetlight network as a social network and construct a neighbor relationship probabilistic graph using IoT event records of streetlights detecting traffic elements. Based on this, a multi-objective genetic algorithm based probabilistic graph clustering method is designed to discover the neighbor relationships of streetlights. Considering the characteristic that pedestrians and vehicles usually move at a constant speed on a section of a road, speed consistency is introduced as an optimization objective, which, together with traditional similarity measures, forms a multi-objective function, enhancing the accuracy of neighbor relationship discovery. Extensive experiments on simulation datasets were conducted, comparing the proposed algorithm with other probabilistic graph clustering algorithms. The results demonstrate that the proposed algorithm can more accurately identify the neighbor relationships of streetlights compared to other algorithms, effectively achieving adaptive streetlight control for traffic elements.