Lighting Management System Research Articles

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

Experimental Study on Automated Controller of Street Light Management System using IoT

In order to ensure public safety and support nighttime activities, street lighting is crucial to the urban landscape, and this study starts by contextualizing its importance. It then goes on to analyze the shortcomings of the current street lighting systems, which are typified by their static control methods and poor environmental response.These shortcomings underscore the urgent need for a paradigm shift towards more intelligent and adaptive solutions. The core proposition of this project lies in the design and implementation of an advanced street light management system empowered by IoT technology. At its heart, the system comprises a sophisticated network of interconnected devices, including sensors, actuators, and a central control unit. These components collaborate synergistically to capture, analyze, and act upon real-time data gleaned from the surrounding environment. Through the application of cutting-edge data analytics and machine learning algorithms, the system autonomously adjusts lighting levels in accordance with fluctuating parameters such as ambient light levels, traffic density, weather conditions, and pedestrian activity. By dynamically optimizing energy usage while ensuring adequate illumination, the system not only enhances operational efficiency but also contributes to cost reduction and environmental sustainability. Moreover, the proposed system incorporates proactive maintenance features, enabling early fault detection and remote diagnostics. This capability minimizes downtime and enhances system reliability, thereby mitigating the operational challenges associated with traditional street lighting infrastructure. The project further explores the conceptual framework of IoT, elucidating its transformative potential in revolutionizing urban infrastructure management. By seamlessly integrating physical devices with digital intelligence, IoT facilitates the creation of interconnected ecosystems wherein data-driven decision-making and automation converge to drive unprecedented efficiency and innovation

Vision based intelligent traffic light management system using Faster R‐CNN

AbstractTransportation systems primarily depend on vehicular flow on roads. Developed countries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Highly Tunable Cellulosic Hydrogels with Dynamic Solar Modulation for Energy-Efficient Windows.

Smart windows that can passively regulate incident solar radiation by dynamically modulating optical transmittance have attracted increasing scientific interest due to their potential economic and environmental savings. However, challenges remain in the global adoption of such systems, given the extreme variability in climatic and economic conditions across different geographical locations. Aiming these issues, a methylcellulose (MC) salt system is synthesized with high tunability for intrinsic optical transmittance (89.3%), which can be applied globally to various locations. Specifically, the MC window exhibits superior heat shielding potential below transition temperatures, becoming opaque at temperatures above the Lower Critical Solution Temperature and reducing the solar heat gain by 55%. This optical tunability is attributable to the particle size change triggered by the temperature-induced reversible coil-to-globular transition. This leads to effective refractive index and scattering modulation, making them prospective solutions for light management systems, an application ahead of intelligent fenestration systems. During the field tests, MC-based windows demonstrated a 9°C temperature decrease compared to double-pane windows on sunny days and a 5°C increase during winters, with simulations predicting an 11% energy savings. The ubiquitous availability of materials, low cost, and ease-of-manufacturing will provide technological equity and foster the ambition toward net-zero buildings.

Intelligent Traffic Signal Control using RF Technology for Emergency Vehicles

The transportation system in our nation has been greatly impacted by the problem of traffic congestion. This leads to a number of complications or problems, especially when there are emergencies in the heavily trafficked lanes at traffic lights. To address these challenges, a traffic light management system is built in. Based on an RFID module, this system was designed to operate when it received a signal from an emergency vehicle. The Programmable Arduino nano micro controller was used to regulate the LEDs used in the traffic signals. The system's use of LEDs makes it easier for emergency vehicles to manoeuvre through traffic. As a result, the project's analysis and implementation of the traffic signal control system for emergency vehicles were successful.

Design and Fabrication of Street Light Management and Control System

In India, most of the streets within the habitations are illuminated from evening until morning, averaging roughly 12 hours per day. Throughout the night, some streets may not have automobiles or even pedestrians travelling through them which results in power loss. In addition, the number of vehicles on the road is increasing every day, and traditional traffic management is still in use, road accidents are one of our country's key issues. Many important lives are lost as a result of these accidents, which occur when emergency assistance is unavailable. So, our proposed solution for this problem is that we are designing a model that first detects the traffic and examine then based on situation it controls the lightening system and send traffic intensity data to internet hour wise. For accident detection we used the SOS button to get help immediately and crewman get the exact location of accident. According to the results obtained from the proposed method, energy usage is greatly reduced, and we can receive a very quick alarm when something unpleasant occurs.

A Data Integrity and Security Approach for Health Care Data in Cloud Environment

Healthcare digitalization has turned out to be a significant paradigm in the advancement of medical sector. Resultant, there is production of huge volume of patient’s digital information. Moreover, storing and safeguarding such information tends to become a critical point of concern. The cloud technology assures to be an effective mechanism towards handling and distributing healthcare information by attending the concerns pertaining to data integrity, security and privacy. The significant benefits and accessibility offered by the cloud technology has immensely raised the storage of healthcare information. It’s highly important that the sensitive healthcare data residing in the cloud is completely secured amidst the healthcare providers and the cloud server. There has been a rising concern associated with secured data access within the cloud environment. That is, the cloud framework must assure a robust authentication control mechanism which allows the healthcare users to confidently share medical information across it. Towards accomplishing the same, a secured and light weight data access management system has been recommended that promises data Integrity and Security. Moreover, the proposed system offers the feature of authority control authorization and authentication mechanism for safeguarding the health-related data residing and circulating within the cloud. A CIAM (Customer identity and access management) helps in secured Data Access Control and OAuth 2.0 aids in providing right privilege with authorisation. Also, by the means of Progressive Attribute Based Encryption approach, sensitive medical information can be encrypted from the cloud storage in a secure manner. The proposed light weight framework yields in efficient, reliable and stable output within the cloud.

A light-management film layer induces dramatically enhanced acetate production in photo-assisted microbial electrosynthesis systems

A light-management system consisting of a Al-doped ZnO (AZO) film layer was combined for the first time with different bio-photocathodes (Serratia marcescens Q1 electrotroph immobilized on g-C3N4, MnFe₂O₄ or MnFe₂O₄/g-C3N4) to significantly enhance acetate production from bicarbonate in photo-assisted microbial electrosynthesis systems (MES). The AZO light-management system exhibiting optical properties independent of the light incident angle mitigated the shielding effect of light by electrotrophs, increasing light trapping and decreasing light reflection, ultimately allowing higher rates of photon absorption and redistributions of photons over the photo-active layers. As a result, more reducing equivalents as H2 produced up to 242% (g-C3N4/AZO-filter) and 543% (g-C3N4/AZO) increase in acetate production at coulombic efficiencies of 70% (g-C3N4/AZO-filter) and 81% (g-C3N4/AZO). The record high solar-to-acetate efficiency obtained with the MnFe₂O₄/g-C3N4/AZO biocathode was 3.20%. The light-management system proposed in this study opens a new promising way to construct efficient bio-photocathodes for inorganic carbon reduction in photo-assisted MES.

Design and optimization of anti-climber device for light rail transit crash energy management system

The study of a new anti-climber structural concept for a light rail transit crash energy management system is intended to improve passive safety performance that reduces the risk of overriding during a car-to-car collision. The anti-climber design and configurations are optimized to achieve the best crashworthiness performances. Several model variations are created, to which design factors are assigned based on the L16 orthogonal array. The optimum configuration is estimated using Taguchi’s Robust Parameter method. The controlled variables’ influence on the performance is analyzed using the Analysis of Variance method. The results show that the optimum configuration for the system’s crash energy absorption and crashbox deformation performances are toothed ends-Al6061-T6-30 mm-lower offset with 1.5 dB gain and toothed ends-Al6061-T6-20 mm-lower offset with 3.3 dB gain, respectively. The most influential parameter is the anti-climber material (29.79%) for the system’s crash energy absorption performance and teeth thickness (50.26%) for the crashbox deforming performance.

Charging Electric Vehicle Batteries: Wired and Wireless Power Transfer: Exploring EV charging technologies

Due to the environmental concerns about CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -caused global warming, policymakers are pushing electrified vehicles (EVs) to reduce these emissions. For passenger vehicles only, the electrified fleet is forecasted to reach 80 million in the U.S. and 500 million globally by 2040. Key enablers for such rapid EV adoption include the improvement of battery energy density, lifespan and safety, and drastic cost reduction of batteries and power electronics. The technology of power electronics nowadays can be found in many aspects of EVs. It alters and transforms the energy from the power source to different forms to feed the loads. For example, the on-board charger (OBC) accepts ac input and converts it to high voltage (HV) dc to charge the propulsion battery. An auxiliary power module (APM) steps down the HV battery voltage to low voltage (LV) to charge the LV battery <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . The HV dc bus voltage is converted to an ac form to drive the propulsion motor, forming the motor drive inverter. Putting the power electronics system and the batteries together, the typical structure of an EV is shown in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> . In addition, power electronics can be found in other onboard auxiliary circuits, such as LED lighting and battery management system (BMS).

A Distributed Algorithm for Computing Groups in IoT Systems

The distributed publication and subscription for the Internet of Things is a model of communication between devices that is simple and powerful. In comparison with other variant problems of ME, the problem considered here is a group mutual exclusion problem. The specificity of an IoT system is that a process can be in more than one group at the same time which is not the case of the algorithms mentioned in the literature where a process request one group in advance for each request. In this paper, we define formally the notion of group. Furthermore, we propose a distributed algorithm for automatic group generation and we will show that this problem is maximal cliques’ problem. This leads us to a new kind of distributed Maximal cliques algorithm to compute the groups suitable for IoT systems. As an application, we propose an IoT-based intersection traffic light management system for vehicles.

Thermal Performance Improvement for Different Strategies of Battery Thermal Management Systems Combined with Jute—A Comparison Study

Jute is a cheap, eco-friendly, widely available material well-known for its cooling properties. In electric vehicles (EVs), dissipating a huge amount of the heat generated from lithium-ion batteries with an efficient, light, and low-power consumption battery thermal management system (BTMS) is required. In our previous study, jute fibers were proposed and investigated as a novel medium to enhance the cooling efficiency of air-based battery thermal management systems. In this paper, as the first attempt, jute was integrated with a phase change material (PCM) passive cooling system, and the thermal performance of a 50 Ah prismatic battery was studied. Temperature evolution, uniformity, and cooling efficiency were investigated. A comparison between the thermal behavior of the air-based BTMS and PCM-assisted cooling system was performed. The results indicated that adding jute to the BTMS increased the cooling efficiency and especially decreased the temperature development. Furthermore, the temperature difference (ΔT) efficiency was enhanced by 60% when integrating jute with PCM, and temperature uniformity improved by 3% when integrating jute with air-based BTMS. This article compared the integration of jute with active cooling and passive cooling; thus, it shed light on the importance of jute as a novel, eco-friendly, lightweight, cheap, available, and nontoxic material added to two strategies of BTMS. The setup was physically made and experimentally studied for the purpose of BTMS optimization.

Wireless Sensor Actuator Network Architecture and Energy Model of a Camera Based Lighting Management System

Communication protocols and wireless networking technique is a cohesive part of any light management system. IEEE 802.15.4 standard based networking techniques for the climate-adaptive light management system and its energy model for sensor actuator node is the focus of this paper. This light integrated scheme with adaptive controls provides the desired illuminance at appropriate times with uniformity, by reducing the discomfort glare and energy use. The first part of this paper investigates the architecture and energy consumption of camera-based wireless sensor-actuator nodes with the help of energy models, including a control unit for the Light Management System. Then focused on the energy savings and techno-economic analysis of this wireless networked lighting system in a test room with automated control of Light Emitting Diode luminaire and Venetian blinds. The wireless sensor actuator networked lighting system performance is analyzed by evaluating the node energy consumption in idle and active mode with real time measurements. The energy consumption evaluation of the nodes allows users to improve node life time and think about power management schemes. The climate-adaptive control scheme shows improved uniformity and significant energy savings.

A framework for dynamic smart traffic light management system

A framework for dynamic smart traffic light management system

Pedestrian intention prediction: A convolutional bottom-up multi-task approach

The ability to predict pedestrian behaviour is crucial for road safety, traffic management systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous vehicles. We present a vision-based system that simultaneously locates where pedestrians are in the scene, estimates their body pose and predicts their intention to cross the road. Given a single image, our proposed neural network is designed using a bottom-up approach and thus runs at nearly constant time without relying on a pedestrian detector. Our method jointly detects human body poses and predicts their intention in a multitask framework. Experimental results show that the proposed model outperforms the precision scores of the state-of-the-art for the task of intention prediction by approximately 20% while running in real-time (5 fps). The source code is publicly available so that it can be easily integrated into an ADAS or into any traffic light management systems.

The Surfaces of the Ceratonia siliqua L. (Carob) Leaflet: Insights from Physics and Chemistry.

The production of superhydrophobic coatings inspired by the surface of plant leaves is a challenging goal. Such coatings hold a bright technological future in niche markets of the aeronautical, space, naval, building, automobile, and biomedical sectors. This work is focused on the adaxial (top) and abaxial (bottom) surfaces of the leaflet of the Ceratonia silique L. (carob), a high-commercial-value Mediterranean tree cultivated in many regions of the world. The adaxial and abaxial surfaces feature hydrophobic and superhydrophobic behaviors, respectively. Their chemical composition is very simple: monopalmitin ester and palmitic acid are protuberant in the epicuticular and intracuticular wax layers of the adaxial surface, respectively, whereas 1-octacosanol dominates in the abaxial wax layers. In both surfaces, epicuticular wax is organized along a randomly oriented and intricate network of nanometer-thick and micrometer-long plates, whose density and degree of interconnection are significantly higher in the abaxial surface. The measured tilting angles for the abaxial surface (12-70°) reveal unusual variable density and water adhesion of the nanostructured plate-based texture. Optical measurements demonstrate that light reflectance/absorbance of the glaucous abaxial surface is significantly higher/lower than that of the nonglaucous adaxial surface. In both surfaces, diffuse reflectance is dominant, and the absorbance is weakly dependent on the light incidence angle. We show that the highly dense nanostructured platelike texture of the epicuticular abaxial layer of the C. siliqua leaflet works as a sophisticated light and water management system: it reflects solar radiation diffusely to lower the surface temperature, and it has superhydrophobic character to keep the surface dry. Such attributes enable efficient gas exchange (photosynthesis and respiration), transpiration, and evaporation. To mimic for the first time the abaxial surface, a templation approach was adopted using poly(dimethylsiloxane) (PDMS)/poly(methylphenylsiloxane) (PMPS) positive/negative replicas and a soft polymer/siloxane negative replica produced by the sol-gel process. Because high topographical variations of the biotemplate and wax adhesion to the biohybrid film affected the replication quality, the reproduction of the wax texture via the synthesis of 1-octacosanol-grafted siloxane-based hybrid materials is proposed as a suitable route to duplicate the abaxial surface with high fidelity. The natural chemical/physical strategy adopted by the C. siliqua leaflet to face the harsh Mediterranean climate is a powerful source of bioinspiration for the development of diffuse reflecting and superhydrophobic material systems with foreseen applications as dual-functional antiglare and water-repelling coatings.

Smart Traffic Light and Street Light Management System

Smart Traffic Light and Street Light Management System

Fast-prototyping Approach to Design and Validate Architectures for Smart Home

The Internet of Things has contributed to make smarter houses and buildings in the last decades. Different existing works already integrate IoT technologies in homes, but end-user needs continuously change and researchers must face this challenge in identifying platforms to fast prototype solutions satisfying these new needs. This paper presents a solution that demonstrates how well-known fast-prototyping technologies like Node-RED, IBM Watson, Telegram, Raspberry Pi 4, and secured MQTT can contribute to develop complex systems facing the challenge. The selected tools are used within a smart home context to support features inspired by people needs and allow users to: a) consult real time conditions (i.e., temperature, humidity, gas), b) remotely manage lights, c) save energy through a light management system based on user movements, d) remotely monitor the house through dedicated webcams, e) generate warning notifications in case of danger. Users can interact with the systems through a web Node-RED dashboard and a Telegram bot. Differently from existing works, the feasibility of the implemented system and the efficacy of the exploited platforms are demonstrated through a running scenario extracted from a consolidated study on user needs in smart homes. The performed experiment can facilitate the fast prototyping of new solutions.

Smart street light management system for conservation of electrical energy

The street light control frameworks are broadly utilized to screen and control the progression of vehicles through the numerous streets to conserve the electrical energy. In traditional manual and automatic street lights monitoring system even though uses renewable resources like solar and wind energy, they were turned ON in the evening i.e. when there is not sufficient sunlight and transitioned in the morning i.e. when there is an adequate light outwardly. The lights once turned ON, remain in the same state continuously for about twelve hours a day. This leads to wastage of electrical energy. To overcome this, this paper proposed a method, to conserve the electricity that is being utilized to light up the streetlights on Highways, Village roads, streets in urban and rural areas, parks etc. the proposed system monitors the obstacles on the road and controls the street lights. This system is not only to turn ON and OFF the lights and also control the intensity of the streetlights on the basis of the movements of vehicles and pedestrians in turn conserving the energy. This system also updates the status of street lights onto the cloud server.

An integrated light management system with real-time light measurement and human perception

Illumination is important for well-being, productivity and safety across several environments, including offices, retail shops and industrial warehouses. Current techniques for setting up lighting require extensive and expert support and need to be repeated if the scene changes. Here we propose the first fully automated light management system which measures lighting in real-time, leveraging an RGBD sensor and a radiosity-based light propagation model. Thanks to the integration of light distribution and perception curves into the radiosity, we outperform a commercial software (Relux) on a newly introduced dataset. Furthermore, our proposed light management system is the first to estimate both the presence and the attention of the people in the environment, as well as their light perception. Our new light management system adapts lighting to the scene and human activity and it is capable of energy savings of up to 66%, as we experimentally quantify, without compromising the lighting quality.