The Intelliguard Helmet is a combination of the latest IoT (Internet of Things) technology to enhance safety, health tracking, and communication in dangerous areas. The helmet has sensors like accelerometers, gyroscopes, heart rate indicators, among others to track impacts, fast motions, and physiological data in real-time to inform emergency personnel or supervisors. A GPS system allows accurate positioning of locations, and geofencing helps wearers be contained within safe areas, with alerts being sent when boundaries are violated. The helmet also provides hands-free voice communication by using inbuilt microphones and speakers, which improves teamwork and responsiveness. The information from the helmet is transmitted to a cloud platform, which provides real-time analytics about the health and environment of the user to avoid accidents and long-term health problems. Having many applications, including sports, factories, etc., the helmet can be equipped with such features as smart lighting in low-light conditions, diagnostic alarms in case of maintenance, and long battery life due to energy-saving technologies. The Intelliguard Helmet will change the way people use personal protective equipment by incorporating real-time monitoring, emergency response features and proactive health monitoring, which will fundamentally change the safety and performance of the equipment in high-pressure conditions.

Introduction Today, the rapid acceleration of urbanization has made it necessary to reconsider the balance among energy consumption, environmental sustainability, and quality of life. Buildings, which account for a significant share of cities’ carbon footprint, play a critical role in efforts to improve energy efficiency and ensure user well-being. In this context, advances in digitalization and Internet of Things (IoT) technologies have enabled buildings to evolve beyond mere physical structures into dynamic, data-driven, and user-interactive systems. Within this framework, the present study constitutes a systematic literature review addressing the effects of indoor environment design in smart cities on energy efficiency (E), user comfort (C), and environmental sustainability (S). In recent years, IoT-based sensor and control technologies have reconfigured approaches to energy management in buildings by enabling the continuous monitoring of environmental parameters such as temperature, humidity, carbon dioxide levels, lighting, and movement, while also strengthening user experience through a holistic perspective. Methods In this regard, the study examined 76 different works in the literature, including field applications, experimental research, and conceptual models. These studies were evaluated through an inductive thematic analysis approach based on content and classified according to recurring conceptual clusters in the literature. Results An examination of these sources reveals that the contributions of IoT technologies to smart buildings and cities are multidimensional in nature. This extensive body of knowledge in the literature demonstrates that IoT is not merely a technical infrastructure but also an ecosystem that transforms energy, health, the environment, transportation, and social life. It is evident that the data derived from all reviewed studies were synthesized under the headings of energy efficiency, indoor environmental quality and comfort, smart city infrastructure, user interaction, security/facility management, and industrial applications. Accordingly, the present study evaluates the contributions of IoT-based solutions to reducing energy consumption, improving environmental conditions, and supporting user-centered indoor design. The reviewed studies show that these technologies not only enhance energy efficiency (maximum savings: smart parking [92.6%] and smart lighting [73.2%]; average building savings: 20–30% through BEMS and IoT systems; HVAC optimization: 30–70% through artificial intelligence), but also support user health and comfort (the use of smart systems is generally expected to produce an improvement of more than 20% in comfort levels, while this rate can reach the 70–90% range with advanced personalized models). Furthermore, they demonstrate that IoT-based systems play a strategic role in achieving environmental sustainability goals, reducing carbon emissions, and implementing smart city policies. Discussion The original contribution of this study lies in its systematic synthesis of energy, comfort, and sustainability within an integrated thematic classification framework, thereby revealing trends in the field, research gaps, and potential future directions.

This study presents a modular smart lighting system that employs an open-loop, daylight-based control strategy supported by artificial intelligence (AI). Unlike conventional closed-loop systems that rely on multiple indoor sensors and complex wiring, the proposed system adopts a distributed architecture in which a Master LED Luminaire (MLL) coordinates Slave LED Luminaires (SLLs). The MLL incorporates a Support Vector Regression (SVR)-based daylight prediction model that estimates indoor daylight availability using fixed architectural parameters such as room dimensions, window geometry, and surface reflectance together with real-time outdoor irradiance data. The system was implemented and validated in a public building under real operating conditions. Comparative evaluation with traditional on–off control and sensor-based closed-loop control indicated that, while the closed-loop approach achieved the lowest artificial lighting demand (56.4%), the proposed system required only slightly more (66.4%) yet offered clear advantages in scalability, ease of installation, and reduced infrastructure costs by eliminating the need for multiple sensors. Integration with IoT-enabled communication further allows real-time parameter updates and adaptive dimming control. The findings demonstrate that the proposed method provides a scalable and cost-effective retrofit solution, addressing the installation, maintenance, and adaptability challenges of conventional lighting control systems.

The rapid expansion of smart homes has resulted in a wide range of interconnected IoT devices such as smart lights, thermostats, cameras, smoke detectors, and smart sockets each producing unique patterns of sensor readings and network behaviour. Accurate classification of these smart-home devices is crucial for ensuring household security, enabling intelligent automation, and supporting early anomaly detection. However, existing machine-learning methods such as K-Nearest Neighbours (KNN) and Random Forest suffer from several challenges, including sensitivity to imbalanced datasets, dependency on shallow hand-crafted features, and limited ability to distinguish between devices with similar behavioural patterns. This project proposes a hybrid IoT device classification framework that combines smart data balancing, comprehensive Exploratory Data Analysis (EDA), and a novel random feature extraction technique integrated with a deep autoencoder. The autoencoder generates a compact, noiseresistant latent feature representation that captures hidden relationships within the IoT sensor data, improving discriminative power and robustness. These enhanced features are then classified using Logistic Regression, selected for its computational efficiency, interpretability, and strong performance when applied to high-quality feature embeddings. The dataset includes multiple IoT device categories such as baby monitors, smart lights, motion sensors, security cameras, smoke detectors, smart sockets, thermostats, TVs, and smartwatches, each represented through multidimensional sensor readings. Smart data balancing techniques ensure fair representation of all device types, reducing bias and improving classification stability. The proposed framework significantly enhances classification accuracy compared to conventional methods while providing stronger resilience to noise, class imbalance, and overlapping feature distributions. By integrating automated feature learning through deep autoencoders with lightweight classification via Logistic Regression, the model offers a secure, scalable, and efficient solution for real-world IoT device identification tasks.

Abstract - It is a smart street lighting system implemented using IoT technology based on ESP32 that automatically controls and monitors the operations of streetlights in real time. The LDR and PIR sensors will regulate the switching on/off of the street lights depending on whether it is night time or not and in case there is movement. Other sensors such as the current sensors for fault detection, MPU6050 sensors for pole safety, and the MQ135 sensor for air quality monitoring have also been incorporated into the system. Energy harvesting will also be employed together with IoT technology by connecting the street light poles to WiFi. Key Words: Internet of Things (IoT), Smart Street Lighting, Energy Efficiency, Energy Harvesting, Sensor-Based Automation

Abstract This study addresses the high energy consumption of auxiliary systems in indoor substations and their significant sensitivity to climate conditions, proposing an integrated climate-responsive energy-saving design method. Based on typical indoor substations from four climate zones in China, dynamic energy consumption simulations and CFD analysis were used to evaluate the energy-saving effects of a synergistic strategy combining “passive design and smart control”. The results show that energy consumption in southern substations is primarily dominated by ventilation and air conditioning (accounting for >80%), while lighting and heating are more prominent in northern substations. After implementing measures such as grating enclosures, enhanced thermal-pressure ventilation, smart lighting, and temperature-control linkage, all sites achieved significant energy savings, with the Harbin station reaching a saving rate of 60.9% and the Xiamen station reaching 55.8%. Based on these findings, the study proposes that southern regions should follow the “heat dissipation priority” principle, strengthening passive ventilation, while northern regions should emphasize “balancing heat dissipation and thermal management”, integrating smart controls for precise operation. This research provides a systematic climate-adaptive solution for the low-carbon and efficient design and operation of substations.

Dynamic orchestration of MEC and caching in smart light pole networks: A DRL-driven framework for latency minimization

Abstract One of the fastest and most effective ways to cut emissions, prepare for climate change, and develop a sustainable future is to switch to green buildings. With the rise of green buildings taking advantage of technological advancements in construction, power efficiency is a major concern because of excessive energy consumption and occupant misuse. This paper focuses on designing and implementing smart lighting systems in the MTC Systems Engineering department in Muscat. The analysis and implementation of converting conventional buildings into energy-efficient structures involves transitioning from traditional fluorescent lighting to LED panels equipped with presence detector systems. The study demonstrates that the proposed systems can minimize power waste, reduce expenses, and have the potential to create environmentally friendly building designs.

Smart lighting has moved from a niche building automation feature to a strategic infrastructure layer for high-performance buildings, campuses, and public-realm developments. In Saudi Arabia, this shift is particularly significant because Vision 2030 links digital transformation, energy productivity, sustainable urbanism, and quality-of-life improvements to the delivery of new districts, giga-projects, and retrofit programs. This review examines how smart lighting control systems can support energy conservation across Saudi Vision 2030 developments, including residential communities, commercial buildings, institutional campuses, mixed-use districts, public space, and transport corridors. The paper is structured as a PRISMA-guided review and synthesizes peer-reviewed and institutional sources published from 2020 to early 2026. The literature screened for the review covered LED efficacy, occupancy and vacancy sensing, daylight harvesting, scheduling, addressable controls, wireless communication, Internet of Things integration, building energy management systems, digital twins, artificial intelligence, public-lighting analytics, Saudi policy frameworks, and implementation challenges in hot-arid climates. The synthesis shows that the strongest savings do not come from any single technology alone. Rather, the most credible energy-conservation pathway combines high-efficacy LED luminaires, robust commissioning, zone-level sensing, daylight-responsive dimming, adaptive scheduling, and integration with broader building or city-management platforms. For Saudi developments, climate responsiveness matters: deep floor plates, high solar exposure, glare control, dust, maintenance cycles, and the interaction between lighting, cooling loads, and occupant comfort all affect system performance. The review also finds that the value proposition of smart lighting is widening beyond operational electricity savings to include demand flexibility, maintenance intelligence, user wellbeing, urban safety, and carbon-aware asset management. Based on the evidence, the paper proposes a Saudi-oriented implementation framework that aligns lighting design, controls architecture, governance, procurement, and post-occupancy verification. The paper concludes that smart lighting is one of the most scalable and near-term opportunities for energy conservation in Vision 2030 developments, but its full impact depends on standards alignment, interoperable controls, data governance, commissioning discipline, and life-cycle decision making.

Adolescents worldwide suffer from severe sleep deprivation and negative emotional states, leading to diminished learning performance. It may be possible to increase students' sleep as well as improve associated emotional states (e.g., stress, depression) and learning performance (e.g., concentration, academic achievement) through non-pharmacological methods. Five classrooms of tenth grade female students in Taiwan were exposed to five different interventions meant to increase sleep duration over the course of a 19-week semester: control (conventional health course, typical indoor lighting), Group 1 (30 min of morning outdoor exercise), Group 2 (mindfulness-based sleep course), Group 3 (4 h of morning simulated sunlight exposure from a smart lighting system), Group 4 (both mindfulness-based sleep course and smart lighting). Sleep, cognitive, emotional, and educational outcomes were tracked. As compared to the control condition, morning outdoor exercise did not improve health or learning performance. The smart indoor lighting and mindfulness-based sleep course independently improved students' sleep duration, emotional states, and concentration. The combination of the mindfulness-based sleep course and the smart lighting system elicited the greatest changes in students' physical and mental health, including improving academic achievement. This study suggests that in the future, existing conventional health courses in schools could be replaced or supplemented with a mindfulness-based sleep course and classroom lighting could be replaced with smart lighting systems to provide better health and learning benefits for adolescent students.

The construction industry has increasingly adopted sustainable practices to reduce environmental impact, improve resource efficiency, and enhance long-term project performance. Green construction strategies, which included energy-efficient designs, waste minimization, sustainable material use, and environmentally friendly technologies, became central to achieving these objectives. The Lusaka-Ndola Dual Carriageway, as one of Zambia’s major infrastructure projects, provided a critical case for examining the application and outcomes of such strategies. This study was aimed at analyzing the effectiveness of green strategies in construction projects, focusing on their implementation, impact on project performance, and associated challenges. Specifically, it sought to identify the types of green strategies applied in the Lusaka-Ndola Dual Carriageway, evaluate their effectiveness, determine the relationship between green strategies and project performance, and examine the limitations that affected their full potential. The study employed an exploratory case study design. Data collection was conducted using a semi-structured questionnaire that included both open-ended and closed-ended questions. Primary data were obtained through structured surveys administered to participants. The study recommends that construction companies prioritize the adoption of low-carbon and recycled materials, alongside energy-efficient technologies such as solar panels and smart lighting, to enhance sustainability and operational efficiency. Companies should implement systematic water conservation and waste management practices, while investing in staff training to build technical capacity for green construction. Firms are encouraged to integrate green strategies into project planning to improve quality, risk management, and stakeholder engagement, while leveraging technology to monitor effectiveness and optimize cost, timeline, and environmental performance.

Banjarwaru Village in Cilacap Regency is facing problems related to the lack of Public Street Lighting (PJU), with several vital road sections being dark at nite. This condition significantly reduces safety levels, increases the risk of crime and accidents, and hinders the mobility and socio-economic activities of the community. Residents, especially women and children, feel unsafe to go outside, which impacts religious, social, and economic activities. Responding to this urgency, this community service program aims to improve the quality of life and well-being of the Banjarwaru Village community through the application of appropriate technology. The proposed solution is the installation of two units of an Arduino-based smart street lighting system powered by solar energy. The implementation method for this service involves field observation and experimentation in the design and construction process of public streetlights, and technology transfer to the community. The results of creating a smart public street light based on Arduino with solar power supply function well. At night, electricity is supplied by batteries. The batteries are charged during the day, when the solar cells receive sunlight. The final activity in this series of community service, and the closing event, was the handover and guidance to the Banjarwaru community, thru the Village Head of Banjarwaru, on how the smart public street lighting system powered by solar energy works and how to maintain it.

Effects of LED lighting environments on tomato (Solanum lycopersicum L.)

This study compares the dimming performance of DALI and 0–10 V using three commercial LED drivers at eight dimming levels. The testing included power consumption, illuminance, and flicker stability. The results showed that DALI was able to maintain a linear dimming curve down to low levels such as 10%, 5%, and 1%, with consistent flicker within the No Risk zone. Power consumption at 0% was also low, making it suitable for applications requiring deep dimming and stable visual comfort. Conversely, the 0–10 V system performed quite well at high levels and was slightly more efficient at low power, but its performance dropped drastically below 30%. At 10%, the illuminance was almost zero and the flicker level entered the High Risk zone. Overall, DALI is more ideal for smart lighting with smooth transitions, while 0–10 V is more suitable for basic installations.

The rapid development of technology has encouraged the creation of automated systems to improve efficiency and convenience in daily life, especially in controlling electrical devices. This study aims to design and implement a smart home light control system based on Android using Arduino Uno and a 5V relay, with Bluetooth communication as the interface between the smartphone and the microcontroller. The research method includes hardware design, software development, and system testing. The system was tested for its ability to receive ON and OFF commands from the Android application, the responsiveness of the relay, and the stability of Bluetooth communication within a range of approximately 10 meters. The results show that the system can control the lamp effectively and reliably, with fast response time and minimal communication errors. This implementation demonstrates a practical, low-cost solution for home automation, providing convenience, energy efficiency, and flexibility for users. The findings suggest that such systems can be further developed to include multiple lamps or integrated with IoT networks for enhanced smart home applications

ABSTRACT Tunnel electrification is essential for road safety and visibility inside tunnels. Conventional tunnel lighting consumes high power because lights remain ON continuously irrespective of traffic density. This research paper presents an ESP32 based smart lighting and safety system for road tunnels. The system uses sensors such as IR/Ultrasonic sensor for vehicle detection and LDR sensor for ambient light detection. Based on real-time sensor input, the ESP32 controller activates tunnel lighting section-wise, which reduces unnecessary electricity consumption. The system is further expandable with IoT monitoring through Wi-Fi for remote supervision and fault indication. The proposed smart tunnel electrification system improves energy efficiency, enhances safety and provides an economical solution for smart city infrastructure. Keywords: ESP32, Tunnel Electrification, Smart Lighting, IoT, LED Lighting, Road Safety, Energy Saving

Smart lighting is emerging in student housing across Africa. This study assesses the drivers and satisfaction of smart lighting in university housing in Southern Ghana. The study used an embedded research method, including a survey of 334 student residents and interviews with 10 housing managers across five purpose-built student housing facilities at five selected public universities in Southern Ghana. The Relative Importance Index and thematic analysis were used as data analytical techniques. The findings reveal that accommodation needs primarily drove the adoption of smart lighting in student housing. The satisfaction levels of smart lighting were limited to lighting controls in lavatories, bedrooms, study areas, the kitchen, and common areas. However, dissatisfaction with lighting was associated with adjusting to the minimum light intensity and controlling artificial lighting. Technically, the limited influence of facilities management factors on smart lighting adoption poses a significant risk to energy sustainability if left unaddressed. Interviews reveal that students' lack of knowledge impacts their satisfaction with and usage of smart lighting systems. At a minimum, student housing managers and students would need education on smart lighting. Keywords: Energy sustainability, smart lighting, sustainable design, student housing, Technology Acceptance Model

Light Fidelity (Li-Fi) is an emerging wireless communication technology that uses visible light for high-speed data transmission instead of conventional radio frequency waves. With the rapid growth of wireless devices, radio spectrum congestion and security concerns have become major challenges. Li-Fi offers an effective solution by utilizing light emitted from Light Emitting Diodes (LEDs) to transmit data through rapid modulation that is imperceptible to the human eye. At the receiver end, a photodiode detects the light signals and converts them into electrical signals for data recovery. This project focuses on the design and implementation of a Li-Fi communication system that demonstrates secure, fast, and interference-free data transmission in indoor environments. Compared to traditional Wi-Fi systems, Li-Fi provides higher data rates, improved security, and immunity to electromagnetic interference, making it suitable for sensitive areas such as hospitals, aircraft cabins, and defense applications. The system is energy-efficient as it utilizes existing lighting infrastructure for communication. Li-Fi technology has the potential to revolutionize future wireless communication by enabling smart lighting, Internet of Things (IoT) integration, and high-density data networks. This project highlights the working principle, advantages, and practical significance of Li-Fi as a next- generation communication technology.

The increasing demand for electrical energy, particularly in offices and commercial buildings, has made energy efficiency a critical aspect of sustainable development. Among various building components, lighting systems are recognized as one of the major consumers of energy. This study investigates the potential for energy savings through the adoption of a smart lighting system incorporating IoT-based sensors, motion detectors, and dimming controls. Employing a quantitative descriptive approach, the research was conducted at the workspace of Indie Light, comparing energy consumption before and after the implementation of the system. Data were collected using direct observation, light and power meters, and real-time monitoring devices to ensure accurate measurement. The results demonstrate that smart lighting systems can substantially reduce energy use without compromising lighting quality or comfort. By integrating intelligent sensors and adaptive control algorithms, the system not only optimizes energy efficiency but also aligns with national policies on energy conservation, supporting broader environmental sustainability efforts. These findings suggest that smart lighting solutions can play a significant role in promoting energy-efficient practices in commercial spaces while contributing to sustainable development goals.

Automated, adaptive lighting systems based on LED arrays offer significant potential for general lighting applications. Unlike conventional lighting systems, LED arrays enable real-time, spatially adaptive illumination by dimming individual LED pixels through camera-detected environmental features. While already mature in the automotive industry, the translation of LED array technology to general lighting comes with various key challenges, limiting their broader adoption: (1) current high-resolution LED arrays are optimized for automotive use; (2) existing projection optics have low efficiency; and (3) determining optimal LED activation patterns from camera data is non-trivial. This manuscript identifies these challenges in-depth, followed by possible solutions. These solutions are then combined and integrated into a fully automated adaptive lighting system with LED array technology. This demonstration is followed by a discussion of the system's shortcomings, concluding that LED array technology for general lighting remains in an early stage but holds significant promise for future development.