Smart lighting has become a universal smart product solution, with global revenues of up to US $\$ $ 5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird’s eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof’s comfort curve, correlated color temperature, and relative mean square error. Finally, we discuss in-depth open issues and future challenges in increasing user comfort in smart lighting using activity recognition.

Investigating the potential for deploying a wind farm requires accurate knowledge of the vertical profile of wind speed (WS), both temporal and with height. Commonly, WS estimations at different heights involves calculating site‐dependent parameters such as roughness length, atmospheric conditions, and wind shear coefficient. This study proposes a hybrid model to estimate WS at different heights based on measurements at lower heights using particle swarm optimization and long short‐term memory (PSO‐LSTM). The training procedure consists of two steps, namely the Levenberg–Marquardt (LM) and the PSO, to minimize the mean squared error. To perform vertical extrapolation, the PSO‐LSTM utilizes the measured WS at 10‐40 m heights to predict WS at 50 m. The predicted results at 50 m are then utilized along with the measured WS at 10–40 to extrapolate WS to 60 m. This process continues until the extrapolation of WS at 120 m is made. The PSO is compared with other optimization methods like genetic algorithm (GA), tuna swarm optimization (TSO), grey wolf optimization (GWO), sparrow search algorithm (SSA), and bald eagle search (BES) on improving the LSTM accuracy. The performance of the PSO‐LSTM is compared with the standard LSTM, feedforward neural network and the logarithmic law (LogLaw) based estimation. The extrapolated values obtained using these methods are compared with LiDAR system‐based measurements. The proposed method outperformed the other methods.

Understanding the landscape of privacy protection in governmental systems is crucial for ensuring the trustworthiness of public services and safeguarding citizens’ sensitive data from breaches or misuse. Systematic mapping and synthesis of previous research can help identify existing privacy-preserving techniques, assess their effectiveness, and highlight areas for improvement, offering valuable insights for policymakers and practitioners. We aim to conduct a systematic literature review (SLR) of privacy-preserving tools and technologies, focusing on their adoption and governments’ challenges. This study also uncovers emerging trends and future research directions, contributing to developing more robust privacy strategies tailored to government needs.Given its extensive reach and government-centric methodology, this evaluation distinguishes itself from previous research. Our work methodically synthesizes privacy-preserving tools and technologies from the distinct perspective of government roles, in contrast to previous assessments that concentrate narrowly on certain technologies or areas. Our findings offer a synthesis of the government’s diverse roles in privacy preservation—regulator, enforcer, user, and service provider—and address existing concerns and key privacy-related technologies. Finally, we identify significant research opportunities, such as improving privacy-preserving mechanisms to strengthen the integrity of public services and mitigate the risks of data breaches and misuse.

Our previous research applied a classification-integrated moving average (CIMA). This intelligence method improves the performance of passive infrared (PIR) sensors in smart lighting to make control more comfortable for the user. However, intelligence, synonymous with the use of the cloud and large latency, contradicts the real-time nature demanded by smart lighting. This paper proposes edge smart lighting (EdgeSL) architecture, an edge-computing architecture for real-time improved CIMA smart lighting control. We developed novel intelligent control using permutation importance feature selection to improve the CIMA algorithm. Three model compression methods for the k-nearest neighbor (KNN) model are compared, including the novel knowledge distillation on KNN called DistilKNN. The KNN model is the basis of CIMA. The method allows the model to run in an edge-computing environment. The experiment was carried out by evaluating processing time in three different environments, namely edge, fog, and cloud architecture. Our test results show that DistilKNN has the best accuracy compared to other methods, including pruning and quantization, which is 0.93. After deploying the compressed model to the NodeMCU, edge computing has a lower average processing time than fog computing and cloud computing, namely 9.0, 60.1, and 207.6 ms, respectively. After going through the Shapiro-Wilk test, we learned that the three processing times are not normally distributed. So after testing with the Wilcoxon test, it is proved that the EdgeSL has the best performance, where the average processing time with the other architectures has significant differences.

Smart lighting systems utilize advanced data, control, and communication technologies and allow users to control lights in new ways. However, achieving user comfort, which should be the focus of smart lighting research, is challenging. One cause is the passive infrared (PIR) sensor that inaccurately detects human presence to control artificial lighting. We propose a novel classification‐integrated moving average (CIMA) model method to solve the problem. The moving average (MA) increases the Pearson correlation (PC) coefficient of motion sensor features to human presence. The classification model is for a smart lighting intelligent control based on these features. Several classification models are proposed and compared, namely, k‐nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), näive Bayes (NB), and ensemble voting (EV). We build an Internet of things (IoT) system to collect movement data. It consists of a PIR sensor, a NodeMCU microcontroller, a Raspberry Pi‐based platform, a relay, and LED lighting. With a sampling rate of 10 seconds and a collection period of 7 days, the system achieved 56852 data records. In the PC test, movement data from the PIR sensor has a correlation coefficient of 0.36 to attendance, while the MA correlation to attendance can reach 0.56. In an exhaustive search of an optimum classification model, KNN has the best and the most robust performance, with an accuracy of 99.8%. It is more accurate than direct light control decisions based on motion sensors, which are 67.6%. Our proposed method can increase the correlation value of movement features on attendance. At the same time, an accurate and robust KNN classification model is applicable for human presence‐based smart lighting control.