Adaptive Traffic Element-Based Streetlight Control Using Neighbor Discovery Algorithm Based on IoT Events

TL;DR

This paper presents a novel IoT-based method for automatically discovering neighbor relationships among streetlights using traffic event data, enabling adaptive control and energy savings in large-scale networks.

Contribution

It introduces a systematic approach combining probabilistic graph modeling and multi-objective genetic algorithms to accurately identify streetlight neighbors without manual configuration.

Findings

Proposed algorithm outperforms existing clustering methods in accuracy.

Enhanced neighbor discovery improves adaptive streetlight control.

Method effectively reduces energy waste in large-scale networks.

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.