Abstract
Intelligent control of public lighting is nowadays one of the most challenging issues in smart city deployment. Lighting optimization entails a compromise between comfort, safety, and power consumption, affecting both vehicles and pedestrians. Smart solutions must estimate their characteristics to trade-off users’ needs and energy requirements. This paper proposes an intelligent street lighting control system and the Receiver Operating Characteristic (ROC) curve method to evaluate the best number of street lamps to achieve a balance between public road user comfort and system power consumption. The control system is based on the detection of users, mainly pedestrians, using presence sensors. From the detection of a pedestrian by two or more consecutive street lamps it is possible to determine their speed. Knowing the pedestrian speed, allows the system to anticipate and adjust the light intensity of the remaining street lamps, and provide a comfortable view of the street. Using the ROC curve, we evaluate the control algorithm in terms of the number of previous street lamps used. We have tested the system and the method in a model of pedestrians walking down a street. The obtained results show that ROC analysis used to control street lighting allows measuring the whole control system’s efficiency by providing a concrete number of previous street lamps.
Highlights
The Sustainable Development Goals (SDGs) are considered part of the basis of the research in Smart Cities [1]
We propose the use of the Receiver Operating Characteristics (ROC) curves [14] to evaluate the performance of control methods applied to develop a distributed smart street lighting system
It is necessary to create the necessary arrays to perform the calculations of consumption, comfort, and the ROC analysis parameters (TP, False Positive (FP), False Negative (FN), and True Negative (TN))
Summary
The Sustainable Development Goals (SDGs) are considered part of the basis of the research in Smart Cities [1]. The challenge of smart cities is in continuous review and transformation [2] since it has to consider different actors, initiatives, goals, and applications [3]. Smart cities include a large number of potential smart sensors such as temperature detectors, ambient sound levels, air quality, and traffic density. With this information, lots of elements interact with the environment to improve the livability of cities. Both city elements and citizens can be considered from an atomic point of view as elements of a system because they share the same environment and interact with them
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