Analysis of proximity sensors for smart street lighting solutions

Abstract

The need for effective urban solutions is growing as cities throughout the world become more crowded, especially in sectors like public services, energy management, and transportation. An intelligent city (smart city) concept is the answer to such problems using cuttingedge tools available just to make living in a city better. This transformation is mostly driven by the Internet of Things (IoT), an enabling network that enables numerous times of device to talk in realtime and deliver information rendering environment responsive. Some of the most significant IoT applications in smart cities are illumination. The smart lighting of the ecosystem where real time streetlight brightness adjusted via sensors leads to improvements in safety and drop in energy consumption and cost.

This research aims to design and deploy an Internet of Things smart lighting system tackling municipal problems like energy conservation, public safety etc. The sensors of the system consist of four types (ultrasonic, laser, radar and passive infrared). Every sensor is current to a separate domain with movement detection, proximate sensing and environment sensing, which then calculates the level of streetlight brightness based on data. Ultrasonic sensors for distance measurement, PIR motion sensors and Laser sensors provide high accuracy distance measurements, radar can detect movement even in difficult weathers. These sensors are connected through an ESP32 microcontroller, which takes both data from and controls lighting systems.

In this thesis I will evaluate the given sensors within one united smart lighting system. Through the study of pedestrian vehicle detection and environmental sensors, the research explores how these technological features protect users in public spaces making them safer, energy efficient, and sustain their cities. The system operates to illuminate streets when there are either people or vehicles on them and dims the lights to minimize energy consumption, when streets are empty. Also, the study further investigates the scalability of IoT smart lighting systems to other urban factors i.e. air quality by fitting some more sensors.