Smart Street Lighting Research Articles

An IoT-Based Smart Street Lighting System to Lower Energy Consumption

Smart Lighting System is an automated and intelligent lighting control system that can be deployed across multiple locations or in one location, utilizing various IoT connection protocols, devices, and sensors. The system is able to intelligently adjust to changes in ambient light conditions, traffic patterns, and pedestrian activity through the integration of Internet of Things (IoT) technologies. IoT-enabled sensors are installed in every street light, creating a network that connects to a central control unit. Using real-time data, this system dynamically modifies street light brightness to maximize energy efficiency and improve safety. It provides a simulation and optimization tool for street light placement, considering factors such as street length, light distance, and the number of lights ahead. This tool aids in visualizing the power consumption implications of different configurations, promoting efficient urban planning. Demonstrates the energy-saving potential of the proposed system. As the street light intervals and the number of lights ahead decreases, the optimal street light power significantly reduces, leading to substantial energy savings. The system's ability to dim or turn off lights during low-traffic or well-lit periods contributes to reduced operational costs and enhanced sustainability.

Application of Smart Streetlights in Urban Road Lighting

Application of Smart Streetlights in Urban Road Lighting

Automated Fault Detection and Location Monitoring of Street Lights in Smart Cities

This work will implement an automated fault detection and monitoring system for streetlights in smart cities. The system will use smart streetlights with sensors, communication modules, and intelligent controllers to create a sensor network across the city. These sensors, like light intensity, motion, and temperature sensors, will collect real-time data on the status of street lights and the environment. The data will be analyzed by a centralized monitoring system that will use algorithms to detect abnormalities in the sensor data that may indicate faults or malfunctions in the street lighting infrastructure. GPS tracking technology will be used to pinpoint the location of each street light. An automated alert system will notify maintenance teams and city officials through a webpage if a fault is detected. The system will also use historical data to anticipate potential issues before they arise.The benefits of this system include improved energy efficiency, cost savings through timely maintenance, quick response times for fault resolution, and data-driven decisionmaking for urban planning. It will provide real-time information on the status of street lights and their environment, which can be used to optimize energy consumption and reduce costs. The system will also enable city officials to make data-driven decisions for urban planning. Ultimately, this system will help to create smarter and more sustainable cities by enhancing public safety, optimizing energy consumption, and ensuring the reliability of street lighting infrastructure. The system will provide a more efficient and costeffective approach to street lighting maintenance, leading to better services for citizens.

Smart and Intelligent Weather Adaptive Street Lighting System Using IoT

In this paper, an Internet of Things (IoT)- based smart and intelligent weather-adaptive street lighting system containing an Arduino UNO, an ESP 8266, an LDR, a voltage sensor, a current sensor, a PIR, a relay, and a mobile or web application is designed and implemented. This project's primary goal is to create an intelligent and adaptable street lighting system that will guarantee constant lighting for rural residents. This project is to minimize the electricity losses, reduce the manpower used in manually switching on-off the street lights and & energy cost. There are three primary parts to the system: An Arduino NANO, ESP 8266 is a Wi-Fi module for signal transfer and a smart phone that can operate web page servers. These days, consumers are searching for smart systems that require less maintenance and use less energy. This intelligent street light only turns on when needed, and it continues to run on its own subsequent evenings even in the absence of sunlight. Furthermore, an Arduino NANO was used to include a PIR sensor, which detects motion and turns on the lights to bright mode by turning on more lights. Taking those factors into account, these smart streetlights are equipped with an Internet of Things (IoT) powered adaptive system that can detect malfunctions and send message to parent system.

Smart Streetlight Energy Saving System Based on mmWave Radar

Streetlights serve as fundamental infrastructure to meet the lighting needs of people on every road. However, their extensive deployment often results in unnecessary energy waste, with many streetlights maintaining high brightness despite minimal usage during the night. This study aims to develop a smart energy-efficient streetlight system that automatically adjusts lighting levels based on the absence of vehicles and pedestrians, detected after a 3-minute countdown. Specifically, the study utilizes mmWave radar to collect point cloud data, which undergoes denoising through Doppler, DBSCAN, and XYZ techniques. Additionally, the mmWave radar assists in training an LSTM model to identify pedestrian pathways. The implementation of the proposed system significantly reduces energy consumption and annual costs by automatically dimming or turning off streetlights in areas with minimal pedestrian activity during nighttime.

Solar Energy Powered Advanced Smart Streetlight For Remote Areas

This project is primarily designed for remote hilly areas, where smart streetlights equipped with fire detection sensors, vibration sensors, noise sensors, and an air quality monitoring module, provide critical safety and environmental monitoring. In the event of abnormal situations, the system instantly notifies government authorities, including police stations, fire stations, and the pollution control board, for swift responses. To ensure reliability in remote hilly regions, the system is powered by solar panels. This innovative solution enhances safety, security, and environmental vigilance in challenging terrains.

Problem-based life situational issues exploration–Taking the learning effectiveness of artificial intelligence in natural sciences

This research focuses on problem-based learning (PBL) teaching methods and designs artificial intelligence (AI) in facial recognition systems, smart streetlights, and drone as teaching materials. To integrate teaching materials of life situation issues into the natural general curriculum and develop a learning perception questionnaire (LPQ) with validity and reliability to evaluate students’ perception of the curriculum. Based on a valid assessment tool, 56 college students were assessed on their learning of emerging technology contextual issues to evaluate their satisfaction, learning situation, and learning effectiveness. The results of the study are, as follows:<br /> (1) construct teaching materials for AI application in face recognition systems, street lights, and drone situations; (2) develop an LPQ with reliability and validity; (3) most students are satisfied with the integration of AI into PBL teaching; (4) most students believe that the integration of cross-domain learning in different subjects can help improve self-learning effectiveness and ensure continuous learning interest; and (5) many students agree that this course can improve learning outcomes. In the future, the focus will be on teaching practice, incorporating easy-to-use AI textbook content, and enhancing the opportunities for interactive learning; in addition, increasing the number of effective samples in the research to improve the depth of the experiment and the breadth of research.

IOT based smart street light fault detection management system

In the contemporary landscape of urbanization and technological progress, efficient resource management, particularly in energy consumption, is paramount. Street lighting stands out as a significant energy consumer in urban environments. This paper proposes a Smart Street Light Fault Detection System utilizing Internet of Things (IoT) technology, a NodeMCU microcontroller, Light Dependent Resistor (LDR), street lights, and the ThingSpeak. The system employs the NodeMCU microcontroller to monitor ambient light conditions through the LDR sensor, enabling automatic control of street lights to optimize energy usage. Furthermore, it integrates fault detection mechanisms to identify abnormalities in street light operations, such as constant light levels amidst changing environmental conditions, indicative of potential faults. Through the ThingSpeak, users can remotely monitor real-time LDR readings, receive notifications about detected faults, and take necessary actions, empowering city authorities or maintenance personnel with timely information for addressing issues and ensuring smooth infrastructure operation. This smart system offers advantages like energy efficiency, automated control, and proactive fault detection, facilitating significant energy savings, reduced maintenance costs, and enhanced sustainability in urban environments, thereby showcasing a practical implementation of IoT technology for smarter and more sustainable cities.

Commodification, datafication and smart cities: An ethical exploration

ABSTRACT Smart cities are a seminal example of data-driven governance that relies on the efficient and networked use of big data in managing urban life to achieve sustainable economic growth. Datafication is considered commodifying various personal information and goods and different aspects of our urban milieu. There is a concern about the commodification-smart city nexus among communication and urban studies scholars concerning its effects on public governance and social segregation, but limited attention is given to it in the ethical literature that targets implications of datafication on privacy. This paper provides an analytical framework to explore ethical considerations for commodification in smart urban systems. The goal is to contribute to incorporating justice-related considerations into the decision-making processes of urban policy development. This paper first revisits the commodification concept and its features. This is followed by a discussion of how datafication can be considered the underlying process of data-driven urban solutions that might exacerbate commodifying processes. Later, different types of commodification are discussed using smart street lights as a generic case. Lastly, different ethical perspectives are presented to discuss the basis of the wrong of commodification in the case of smart city design, along with suggestions stemming from concern over social equality.

Study on Smart Street Light (SSL) using Ultra Narrowband (UNB) Technology: a case study of Malaysia Power Utility

Street light is important to road user and residential area to improve visibility and creating safer road by preventing traffic accidents and also by reducing crime. However, present practice for street lighting management largely remains a challenge. Smart Street Lighting (SSL) system is a system where the street lighting system adds up with communication technology producing a real time monitoring system. Smart Street Lighting (SSL) is known to gain high value creation through maintenance savings and improvement of the environmental impact for Malaysia. Tenaga Nasional Berhad (TNB) through its research arm, TNB Research (TNBR) has recently carried a feasibility study on the compatibility of SSL system using Ultra Narrowband (UNB) technology on the energy efficient and better light intensity of light-emitting diode (LED) lighting technology street light. The SSL systems have been deployed on a small scale, tested and monitored at selected areas. This paper discussed the technical performance such as functionality and compatibility that have been assessed in the study.

BUDGET-BASED SMART CITY IMPLEMENTATION FOR TIER III CITIES

In the present day's world the Internet of Things (IoT) is gaining popularity as a field of research in various engineering applications. IoT includes various elements like communication technologies, communication protocols, identification technologies, sensing, computation, and actuation. Today, almost every home is equipped with smart IoT home appliances. As cities grow and expand, smart and innovative solutions are crucial for improving productivity, increasing operational efficiencies, reducing management costs, and enhancing the lifestyle of human beings. A smart city consists of a number of applications. In this paper, budget-based implementation of smart cities with specific applications like smart parking, smart dustbins, smart streetlights, and smart traffic monitoring and management systems are done using various IoT boards, various sensors, actuators, and various IoT cloud platforms. In the smart city application, several heterogeneous devices are connected through the internet for communication, and huge amounts of data are collected from various applications and stored in databases. Data are classified, analyzed, and visualized on both web and mobile dashboards.

LoRaWAN for Smart Street Lighting Solution in Pangandaran Regency

Smart street lighting is a key application in smart cities, enabling the monitoring and control of street lamps through internet connectivity. LoRa/LoRaWAN, an IoT technology, offers advantages such as low power consumption, cost-effectiveness, and a wide area network. With its extensive coverage of up to 15 kilometers and easy deployment, LoRa has become a favored connectivity option for IoT use cases. This study explores the utilization of LoRaWAN in Pangandaran, a regency in the West Java province of Indonesia. Implementing LoRaWAN in this context has resulted in several benefits, including the ability to monitor and control street lighting in specific areas of Pangandaran and real-time recording of energy consumption. The primary objective of this research is to estimate the number of LoRaWAN gateways required to support smart street lighting in Pangandaran. Two methods are employed: coverage calculation using the free space loss approach and capacity calculation. The coverage calculation suggests a requirement of 34 gateways, whereas the capacity calculation indicates that only two gateways are needed. Based on these findings, it can be inferred that, theoretically, a maximum of 34 gateways would be necessary for smart street lighting in the Pangandaran area. However, further research, including driving tests, is recommended to validate these results for future implementation. This study provides insights into the practical application of LoRaWAN technology in smart street lighting, specifically in Pangandaran. The findings contribute to optimizing infrastructure and resource allocation, ultimately enhancing the efficiency and effectiveness of urban lighting systems.

LoRaWAN for Smart Street Lighting Solution in Pangandaran Regency

Smart street lighting is a key application in smart cities, enabling the monitoring and control of street lamps through internet connectivity. LoRa/LoRaWAN, an IoT technology, offers advantages such as low power consumption, cost-effectiveness, and a wide area network. With its extensive coverage of up to 15 kilometers and easy deployment, LoRa has become a favored connectivity option for IoT use cases. This study explores the utilization of LoRaWAN in Pangandaran, a regency in the West Java province of Indonesia. Implementing LoRaWAN in this context has resulted in several benefits, including the ability to monitor and control street lighting in specific areas of Pangandaran and real-time recording of energy consumption. The primary objective of this research is to estimate the number of LoRaWAN gateways required to support smart street lighting in Pangandaran. Two methods are employed: coverage calculation using the free space loss approach and capacity calculation. The coverage calculation suggests a requirement of 34 gateways, whereas the capacity calculation indicates that only two gateways are needed. Based on these findings, it can be inferred that, theoretically, a maximum of 34 gateways would be necessary for smart street lighting in the Pangandaran area. However, further research, including driving tests, is recommended to validate these results for future implementation. This study provides insights into the practical application of LoRaWAN technology in smart street lighting, specifically in Pangandaran. The findings contribute to optimizing infrastructure and resource allocation, ultimately enhancing the efficiency and effectiveness of urban lighting systems.

LoRaWAN for Smart Street Lighting Solution in Pangandaran Regency

Smart street lighting is a key application in smart cities, enabling the monitoring and control of street lamps through internet connectivity. LoRa/LoRaWAN, an IoT technology, offers advantages such as low power consumption, cost-effectiveness, and a wide area network. With its extensive coverage of up to 15 kilometers and easy deployment, LoRa has become a favored connectivity option for IoT use cases. This study explores the utilization of LoRaWAN in Pangandaran, a regency in the West Java province of Indonesia. Implementing LoRaWAN in this context has resulted in several benefits, including the ability to monitor and control street lighting in specific areas of Pangandaran and real-time recording of energy consumption. The primary objective of this research is to estimate the number of LoRaWAN gateways required to support smart street lighting in Pangandaran. Two methods are employed: coverage calculation using the free space loss approach and capacity calculation. The coverage calculation suggests a requirement of 34 gateways, whereas the capacity calculation indicates that only two gateways are needed. Based on these findings, it can be inferred that, theoretically, a maximum of 34 gateways would be necessary for smart street lighting in the Pangandaran area. However, further research, including driving tests, is recommended to validate these results for future implementation. This study provides insights into the practical application of LoRaWAN technology in smart street lighting, specifically in Pangandaran. The findings contribute to optimizing infrastructure and resource allocation, ultimately enhancing the efficiency and effectiveness of urban lighting systems.

INTEGRASI FRAMEWORK LARAVEL DALAM PENGEMBANGAN PERANGKAT LUNAK SISTEM SMART PJU BERBASIS INTERNET OF THINGS

This study analyzes the use of Laravel structure in improving Internet of Thing IoT-based Smart Street Lighting software. This research applies the Software Development Life Cycle (SDLC) methodology approach with the waterfall method. The IoT Smart Street Lighting system is the focus in an effort to improve the efficiency of public street lighting. This methodology merger applies the Laravel structure in software development with structured steps. The vetting process begins with defining requirements, user interface (UI) plans, and data management. The execution stage involves applying Laravel for programming as per the framework's prerequisites. Thorough testing ensures functionality, security, and optimal execution. This approach enhances future development capabilities and program quality by combining Laravel technicalities and the waterfall approach, making the Intelligent Street Lighting System more responsive, robust, and efficient based on weather data.

Encountering Innovation, Countering Innovation

What could be gained by putting science and technology studies (STS) in conversation with innovation studies (IS)? These distinct fields have shared people over decades, as they build concepts, careers, institutions, and even nations. I review how this collection offers accounts of how STS and IS have been practiced in different times and locations: resisting underdevelopment, Western and middle-class assumptions about progress, or technology-centric policy. I argue, however, that it is critical to clarify the difference between innovation as an analytic and as an emic and ideological category. Neither STS nor IS should take for granted the ways political economy, class relations, racialization and gendering, and even national(ist) ideologies shape what counts as desirable forms of newness, what newness ought to be contained or criminalized, and the hierarchies of socio-technical transformation that emerge out of that. I offer three examples: San Diego’s “smart streetlights” program where “innovation” as an ideology devalues or erases the creativity and knowledge already manifest among residents; Amazon® Mechanical Turk worker advocacy and the limits of doing scholarship with policy relevance when workers do not have organized power; and mid-twentieth-century Iran, where I show what IBM® throwing computers into the ocean can tell us about innovation as a form of enclosure, repression, empire, and waste of collective resources and knowledge.

Design and Implementation of a Real-Time Street Light Dimming System Based on a Hybrid Control Architecture

Smart street LED lighting systems have received much attention driven by the need to save energy and the dramatic advances in the Internet of Things. This work proposes a new smart street lighting system that adaptively changes the street lights’ intensity based on traffic and weather conditions and provides a platform for monitoring road conditions and detecting lamp faults. The system transfers the data using the UDP protocol over NBIoT radio technology. It also maintains two-way communication between the luminaires and the central node. In order to ensure real-time response to traffic and avoid dimming delays, each light is locally controlled by a microcontroller based on the sensed traffic and weather data. The measurements of each luminaire are also sent to the central control node to locate lamp faults, detect emergency situations, and, if needed, broadcast on/off messages to the whole network’s luminaires. The system was implemented in a suburban street in Ras Al Khaimah. Evaluations proved that the system can locate and detect faulty lamps and vary the light intensity in real time based on traffic. It also resulted in energy savings of up to 55% compared to a normal LED street light network.

Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

As urbanization increases, streetlights have become significant consumers of electrical power, making it imperative to develop effective control methods for sustainability. This paper offers a comprehensive review on control methods of smart streetlight systems, setting itself apart by introducing a novel light scheme framework that provides a structured classification of various light control patterns, thus filling an existing gap in the literature. Unlike previous studies, this work dives into the technical specifics of individual research papers and methodologies, ranging from basic to advanced control methods like computer vision and deep learning, while also assessing the energy consumption associated with each approach. Additionally, the paper expands the discussion to explore alternative functionalities for streetlights, such as serving as communication networks, environmental monitors, and electric vehicle charging stations. This multidisciplinary research aims to be a pivotal resource for both academics and industry professionals, laying the groundwork for future innovation and sustainable solutions in urban lighting.

Smart Street Lights: Advancements in Street Lighting Technology

Abstract: This system is primarily designed to efficiently manage LED-based street lights using automatic intensity control. It accomplishes this by harnessing solar power through photovoltaic cells. The primary goal is to ensure that street lights shine brightly during peak hours, when traffic is at its busiest, while gradually reducing their intensity during late-night hours to conserve energy. This automation ensures that the street lights automatically turn on at dusk and turn off at dawn. White Light Emitting Diodes (LEDs) are employed as a replacement for High-Intensity Discharge (HID) lamps, offering the advantage of intensity control through pulse width modulation. This intensity control feature allows for energy conservation during late-night hours when street traffic is minimal.

Adaptive Illumination: Designing a Smart Street Lighting System for Sustainable Urban Environments

In the age of rapid technological advancement and increasing urbanization, the need for sustainable and efficient infrastructure has never been more pronounced. As cities grow and evolve, so do the challenges related to managing their energy consumption, safety, and environmental footprint. One of the critical components of urban infrastructure that has a direct impact on all these areas is street lighting. Traditional street lighting systems, while essential for safety and visibility, often consume excessive amounts of energy, contribute to light pollution, and require significant maintenance. This is where the concept of adaptive illumination comes into play as shown in Figure 1.
 Adaptive illumination refers to the design and implementation of street lighting systems that can adjust their brightness and intensity based on real-time environmental conditions and human activity. Unlike conventional systems that remain uniformly lit throughout the night, adaptive systems use sensors, data analytics, and connectivity to dynamically modify their output. This not only results in significant energy savings but also reduces light pollution and enhances the overall safety and comfort of urban residents.[1]
 The sustainability aspect of adaptive illumination cannot be overstated. As the world grapples with climate change and the pressing need to reduce carbon emissions, every kilowatt-hour saved has a cascading positive effect on the environment. By ensuring that street lights are only as bright as they need to be, cities can dramatically decrease their energy consumption. This is particularly relevant in the context of urban areas, which are often the largest consumers of electricity within a region.