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This article proposes an improved Visible Light Communication (VLC) solution that, besides the indoor lighting and data transfer, offers an energy-efficient alternative for modern workspaces. Unlike Light-Fidelity (LiFi), designed for high-speed data communication, VLC primarily targets applications where fast data rates are not essential. The developed prototype ensures reliable communication under variable lighting conditions, addressing low-speed requirements such as test bench monitoring, occupancy detection, remote commands, logging or access control. Although the tested data rate was limited to 100 kb/s with a Bit Error Rate (BER) below 10−7, the key innovation is the light dimming dynamic adaptation. Therefore, the system self-adjusts the LED duty cycle between 10% and 90%, based on natural or artificial ambient light, to maintain a minimum illuminance of 300 lx at the workspace level. Additionally, this work includes a scalability analysis through simulations conducted in an office scenario with up to six users. The results show that the system can adjust the lighting level and maintain the connectivity according to users’ presence, significantly reducing energy consumption without compromising visual comfort or communication performance. With this light intensity regulation algorithm, the proposed solution demonstrates real potential for implementation in smart indoor environments focused on sustainability and connectivity.

The idea of smart lighting has emerged over the years in commercial and industrial environments, with a focus on energy saving. With the advancement in technology, smart lighting can now offer opportunities in addition to energy saving to users in home environments for the provision of a comfortable atmosphere and the maintenance of user well-being. Currently, research in the smart lighting field is predominantly dedicated to energy saving in non-residential environments; meanwhile, the residential environments have not been explored. Therefore, a literature review was conducted to provide an overview of smart lighting systems’ effect on energy and well-being in the residential environment. Current research is mostly limited to designing and developing a smart lighting system in a controlled environment, with a limited evaluation of well-being and comfort. The review shows that residential smart lighting application possibilities and opportunities are not widely and thoroughly explored.

With the rapid development of human society, people’s requirements for lighting are also increasing. The amount of energy consumed by lighting systems in buildings is increasing, but most current lighting systems are inefficient and provide insufficient light comfort. Therefore, this paper proposes an intelligent lighting control system based on a distributed architecture, incorporating a dynamic shading system for adjusting the interior lighting environment. The system comprises two subsystems: lighting and shading. The shading subsystem utilizes fuzzy control logic to control lighting based on the room’s temperature and illumination, thereby achieving rapid control with fewer calculations. The lighting subsystem employs a Deep Deterministic Policy Gradient (DDPG) algorithm to optimize the luminaire dimming problem based on room illuminance in order to maximize user convenience while achieving uniform illumination. This paper also includes the construction of a prototype box on which the system is evaluated in two distinct circumstances. The results of the tests demonstrate that the system functions properly, has stability and real-time performance, and can adapt to complex and variable outdoor environments. The maximum relative error between actual and expected illuminance is less than 10%, and the average relative error is less than 5% when achieving uniform illuminance.

As the interest toward communication-based vehicle safety applications is increasing, the development of secure wireless communication techniques has become an important research area. In this context, the article addresses issues that are related to the use of the visible light communication (VLC) technology in vehicular applications. Thus, it provides an extensive presentation concerning the main challenges and issues that are associated to vehicular VLC applications and of some of the existing VLC solutions. Moreover, the article presents the aspects related to the design and intensive experimental evaluation of a new automotive VLC system. The experimental evaluation performed in indoor and outdoor conditions shows that the proposed system can achieve communication distances up to 50 m and bit error ratio (BER) lower than 10−6, while being exposed to optical and weather perturbations. This article provides important evidence concerning the snowfall effect on middle to long range outdoor VLC, as the proposed VLC system was also evaluated in snowfall conditions. Accordingly, the experimental evaluation showed that snowfall and heavy gust could increase bit error rate by up to 10,000 times. Even so, this article provides encouraging evidence that VLC systems will soon be able to reliably support V2X communications.

An imaging sensor-based intelligent Light Emitting Diode (LED) lighting system for desk use is proposed. In contrast to the traditional intelligent lighting system, such as the photosensitive resistance sensor-based or the infrared sensor-based system, the imaging sensor can realize a finer perception of the environmental light; thus it can guide a more precise lighting control. Before this system works, first lots of typical imaging lighting data of the desk application are accumulated. Second, a series of subjective and objective Lighting Effect Evaluation Metrics (LEEMs) are defined and assessed for these datasets above. Then the cluster benchmarks of these objective LEEMs can be obtained. Third, both a single LEEM-based control and a multiple LEEMs-based control are developed to realize a kind of optimal luminance tuning. When this system works, first it captures the lighting image using a wearable camera. Then it computes the objective LEEMs of the captured image and compares them with the cluster benchmarks of the objective LEEMs. Finally, the single LEEM-based or the multiple LEEMs-based control can be implemented to get a kind of optimal lighting effect. Many experiment results have shown the proposed system can tune the LED lamp automatically according to environment luminance changes.

This paper investigated and compared how the energy consumption of a conventional and Smart Lighting System (SLS) in a simulated residential setting is affected by different households' arrangements and occupancy pattern. An agent-based simulation model of a one-bedroom apartment in Sweden was chosen for comparison with different scenarios. The result shows that the number of residents within an apartment does not necessarily lead to higher energy consumption. Further findings indicate that, even though it has standby energy consumption, SLS is more energy efficient compared to the conventional lighting system. Additionally, energy consumption during weekends was considerably higher than during weekdays.

This paper presents and compares the possible energy savings in various approaches to outdoor lighting modernization. Several solutions implementable using currently-available systems are presented and discussed. An innovative approach using real-time sensor data is also presented in detail, along with its formal background, based on Artificial Intelligence methods (rule-based systems) and graph transformations. The efficiency of all approaches has been estimated and compared using real-life data recorded at an urban setting. The article also presents other aspects which influence the efficiency and feasibility of intelligent lighting projects, including design quality, design workload and conformance to standards.

Lighting energy consumption accounts for a considerable proportion of the total electricity consumption of a building; therefore, the lighting system of a building has great potential for energy saving. With the development of technology, intelligent lighting control systems are widely used in buildings and have good energy-saving effects. Previous reviews of intelligent lighting control systems have analyzed the research progress, advantages, and limitations of a single technology in depth, but have not yet summarized the combination of various technologies and the scope of application. Therefore, this study starts from the components of intelligent lighting control system and focuses on the data acquisition module and control module. Researches on these two modules are analyzed from the aspects of multi-technology combination, combination with artificial intelligence and multi-system considerations, and the application scope and limitations of the related technologies are summarized to provide references for future engineering practice. Finally, based on the development overview of current research, this study explores research directions that can be further expanded in intelligent lighting control systems to handle future development trends.

The demand for personalized lighting environments based on households is steadily increasing among users. This article proposes a novel intelligent control system for personalized lighting in home environments, aiming to automatically capture user information, such as homecoming time and light switching behavior, in order to train a model that intelligently regulates the lights for users. Facial recognition technology is employed by this system to identify users and record their lighting data. Subsequently, nine commonly used machine learning models were evaluated, revealing that the error back-propagation neural network algorithm exhibits excellent performance in time-series analysis. The BPNN weights were optimized using genetic algorithms, resulting in an improved coefficient of determination (R2) of 0.99 for turn-on time and 0.82 for turn-off time test sets. Furthermore, testing was conducted on data collection duration which demonstrated that even with only 20 time-series data collected from new users, the model still displayed exceptional performance in training prediction tasks. Overall, this system effectively identifies users and automatically adjusts the lighting environment according to their preferences, providing comfortable and convenient lighting conditions tailored to individual needs. Consequently, a broader goal of energy conservation and environmental sustainability can be achieved.

Although there are many sources of electric energy, there are various waste phenomena in various occasions, especially the waste of electric energy in building corridors is often ignored. This paper introduces a corridor intelligent light system, which includes several sensors, the gateway, core controller and cloud platform. The system can effectively save energy and has intelligent lighting control such as automatic induction of people, automatic switching of corresponding upper and lower floor lights, etc.