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Intelligent streetlight systems divide the streetlight network into multiple sectors, activating only the streetlights in the corresponding sectors when traffic elements pass by, rather than all streetlights, effectively reducing energy waste. This strategy requires streetlights to understand their neighbor relationships to illuminate only the streetlights in their respective sectors. However, manually configuring the neighbor relationships for a large number of streetlights in complex large-scale road streetlight networks is cumbersome and prone to errors. Due to the crisscrossing nature of roads, it is also difficult to determine the neighbor relationships using GPS or communication positioning. In response to these issues, this article proposes a systematic approach to model the streetlight network as a social network and construct a neighbor relationship probabilistic graph using IoT event records of streetlights detecting traffic elements. Based on this, a multi-objective genetic algorithm based probabilistic graph clustering method is designed to discover the neighbor relationships of streetlights. Considering the characteristic that pedestrians and vehicles usually move at a

Streetlights shining across rippled water often produce tall, narrow reflections with strikingly parallel sides. These light pillars appear almost architectural, yet the water surface is neither vertical nor smooth. We develop a geometric optics model that explains the phenomenon using the specular reflection rule, projection geometry, and the physics and statistics of surface slopes. A rippled water surface can be viewed as an ensemble of small facets (tangent planes on waves) acting as tiny mirrors. As one looks farther across the water (coordinate x), the physical width of the region whose facets reflect the light into the eye (or camera/pinhole) increases, but the pinhole projection onto the image plane compresses this widening by a factor proportional to 1/x. The two effects cancel, producing a reflection whose image width remains constant. This paper appears to be the first to formally document this. (Deviations from the strict cancellation at both ends of the image are also explained.) The analysis clarifies how earlier qualitative treatments failed through a lack of identifying the role of projection geometry. The model also qualitatively explains the brightness variation a

We present a large-scale, longitudinal visual dataset of urban streetlights captured by 22 fixed-angle cameras deployed across Bristol, U.K., from 2021 to 2025. The dataset contains over 526,000 images, collected hourly under diverse lighting, weather, and seasonal conditions. Each image is accompanied by rich metadata, including timestamps, GPS coordinates, and device identifiers. This unique real-world dataset enables detailed investigation of visual drift, anomaly detection, and MLOps strategies in smart city deployments. To promtoe seconardary analysis, we additionally provide a self-supervised framework based on convolutional variational autoencoders (CNN-VAEs). Models are trained separately for each camera node and for day/night image sets. We define two per-sample drift metrics: relative centroid drift, capturing latent space deviation from a baseline quarter, and relative reconstruction error, measuring normalized image-domain degradation. This dataset provides a realistic, fine-grained benchmark for evaluating long-term model stability, drift-aware learning, and deployment-ready vision systems. The images and structured metadata are publicly released in JPEG and CSV format

Accurate and robust state estimation at nighttime is essential for autonomous robotic navigation to achieve nocturnal or round-the-clock tasks. An intuitive question arises: Can low-cost standard cameras be exploited for nocturnal state estimation? Regrettably, most existing visual methods may fail under adverse illumination conditions, even with active lighting or image enhancement. A pivotal insight, however, is that streetlights in most urban scenarios act as stable and salient prior visual cues at night, reminiscent of stars in deep space aiding spacecraft voyage in interstellar navigation. Inspired by this, we propose Night-Voyager, an object-level nocturnal vision-aided state estimation framework that leverages prior object maps and keypoints for versatile localization. We also find that the primary limitation of conventional visual methods under poor lighting conditions stems from the reliance on pixel-level metrics. In contrast, metric-agnostic, non-pixel-level object detection serves as a bridge between pixel-level and object-level spaces, enabling effective propagation and utilization of object map information within the system. Night-Voyager begins with a fast initializa

Path Loss (PL) is vital to evaluate the performance of Unmanned Aerial Vehicles (UAVs) as Aerial Base Stations (ABSs), particularly in urban environments with complex propagation due to various obstacles. Accurately modeling PL requires a generalized Probability of Line-of-Sight (PLoS) that can consider multiple obstructions. While the existing PLoS models mostly assume a simplified Manhattan grid with uniform building sizes and spacing, they overlook the real-world variability in building dimensions. Furthermore, such models do not consider other obstacles, such as trees and streetlights, which may also impact the performance, especially in millimeter-wave (mmWave) bands. This paper introduces a Manhattan Random Simulator (MRS) to estimate PLoS for UAV-based communications in urban areas by incorporating irregular building shapes, non-uniform spacing, and additional random obstacles to create a more realistic environment. Lastly, we present the PL differences with and without obstacles for standard urban environments and derive the empirical PL for these environments.

The safety of autonomous driving systems (ADS) depends on accurate perception across distance and driving conditions. The outputs of AI perception algorithms are stochastic, which have a major impact on decision making and safety outcomes, including time-to-collision estimation. However, current perception evaluation metrics do not reflect the stochastic nature of perception algorithms. We introduce the Perception Characteristics Distance (PCD), a novel metric incorporating model output uncertainty as represented by the farthest distance at which an object can be reliably detected. To represent a system's overall perception capability in terms of reliable detection distance, we average PCD values across multiple detection quality and probabilistic thresholds to produce the average PCD (aPCD). For empirical validation, we present the SensorRainFall dataset, collected on the Virginia Smart Road using a sensor-equipped vehicle (cameras, radar, and LiDAR) under different weather (clear and rainy) and illumination conditions (daylight, streetlight, and nighttime). The dataset includes ground-truth distances, bounding boxes, and segmentation masks for target objects. Experiments with sta

Light pollution is an increasing environmental concern, impacting both ecological systems and human health. This report presents an analysis of light pollution data from the washetdonker.nl SQM network from 2020 until 2023, with a focus on indirect light pollution, commonly known as skyglow. By integrating measurements from Sky Quality Meter (SQM) stations in the network and cloud cover data from EUMETSAT, we conducted a comprehensive analysis of night sky brightness across a region encompassing northern Netherlands and the western part of the German Wadden Coast. Yearly changes in brightness for 27 locations were ranked and plotted, revealing that in the darkest areas, light pollution is increasing at a rate of 2.78 to 6.68 percent per year. A trend emerged showing that brighter areas experienced lower variability in brightness, while darker zones exhibited higher variability. This is due to the dominance of artificial light sources, such as street lighting, in brighter areas, which reduces the influence of natural light sources like the Moon, stars, and cloud backscatter. Seasonal patterns and the effects of the Milky Way were also investigated. Density plots were employed to vis

Neighborhood environments include physical and environmental conditions such as housing quality, roads, and sidewalks, which significantly influence human health and well-being. Traditional methods for assessing these environments, including field surveys and geographic information systems (GIS), are resource-intensive and challenging to evaluate neighborhood environments at scale. Although machine learning offers potential for automated analysis, the laborious process of labeling training data and the lack of accessible models hinder scalability. This study explores the feasibility of large language models (LLMs) such as ChatGPT and Gemini as tools for decoding neighborhood environments (e.g., sidewalk and powerline) at scale. We train a robust YOLOv11-based model, which achieves an average accuracy of 99.13% in detecting six environmental indicators, including streetlight, sidewalk, powerline, apartment, single-lane road, and multilane road. We then evaluate four LLMs, including ChatGPT, Gemini, Claude, and Grok, to assess their feasibility, robustness, and limitations in identifying these indicators, with a focus on the impact of prompting strategies and fine-tuning. We apply ma

Vision-aided localization for low-cost mobile robots in diverse environments has attracted widespread attention recently. Although many current systems are applicable in daytime environments, nocturnal visual localization is still an open problem owing to the lack of stable visual information. An insight from most nocturnal scenes is that the static and bright streetlights are reliable visual information for localization. Hence we propose a nocturnal vision-aided localization system in streetlight maps with a novel data association and matching scheme using object detection methods. We leverage the Invariant Extended Kalman Filter (InEKF) to fuse IMU, odometer, and camera measurements for consistent state estimation at night. Furthermore, a tracking recovery module is also designed for tracking failures. Experimental results indicate that our proposed system achieves accurate and robust localization with less than $0.2\%$ relative error of trajectory length in four nocturnal environments.

This paper proposes a hybrid infrastructure-to-vehicle (I2V) communication framework to support future 6G-enabled intelligent transportation systems (ITS) in smart cities. Leveraging existing LED streetlighting infrastructure, the system simultaneously delivers energy-efficient illumination and high-speed wireless connectivity. The proposed scheme integrates visible light communication (VLC) with a complementary ter-ahertz (THz) antenna array to overcome VLC limitations under high ambient light and adverse weather conditions. Key con-tributions include the design of a VLC/THz access network, seamless integration with lighting infrastructure, a proposed switching-combination (PSC) mechanism, and a physical layout optimization strategy. Using a grid search method, thousands of configurations were evaluated to maximize lighting coverage, re-ceived power, signal-to-noise ratio (SNR), signal-to-interference-and-noise ratio (SINR), and minimize outage probability. Results show that optimized lighting coverage improves from 35% to 97%, while hybrid communication coverage increases from 49%to 99.9% at the same power level. Under extreme environmental conditions, the hybrid system maintains

This article includes a comprehensive collection of over 800 high-resolution streetlight images taken systematically from India's major streets, primarily in the Chennai region. The images were methodically collected following standardized methods to assure uniformity and quality. Each image has been labelled and grouped into directories based on binary class labels, which indicate whether each streetlight is functional or not. This organized dataset is intended to make it easier to train and evaluate deep neural networks, allowing for the creation of pre-trained models that have robust feature representations. Such models have several potential uses, such as improving smart city surveillance systems, automating street infrastructure monitoring, and increasing urban management efficiency. The availability of this dataset is intended to inspire future research and development in computer vision and smart city technologies, supporting innovation and practical solutions to urban infrastructure concerns. The dataset can be accessed at https://github.com/Team16Project/Street-Light-Dataset/.

UMBRELLA is a large-scale, open-access Internet of Things (IoT) ecosystem incorporating over 200 multi-sensor multi-wireless nodes, 20 collaborative robots, and edge-intelligence-enabled devices. This paper provides a guide to the implemented and prospective artificial intelligence (AI) capabilities of UMBRELLA in real-world IoT systems. Four existing UMBRELLA applications are presented in detail: 1) An automated streetlight monitoring for detecting issues and triggering maintenance alerts; 2) A Digital twin of building environments providing enhanced air quality sensing with reduced cost; 3) A large-scale Federated Learning framework for reducing communication overhead; and 4) An intrusion detection for containerised applications identifying malicious activities. Additionally, the potential of UMBRELLA is outlined for future smart city and multi-robot crowdsensing applications enhanced by semantic communications and multi-agent planning. Finally, to realise the above use-cases we discuss the need for a tailored MLOps platform to automate UMBRELLA model pipelines and establish trust.

This paper introduces the "GPT-in-the-loop" approach, a novel method combining the advanced reasoning capabilities of Large Language Models (LLMs) like Generative Pre-trained Transformers (GPT) with multiagent (MAS) systems. Venturing beyond traditional adaptive approaches that generally require long training processes, our framework employs GPT-4 for enhanced problem-solving and explanation skills. Our experimental backdrop is the smart streetlight Internet of Things (IoT) application. Here, agents use sensors, actuators, and neural networks to create an energy-efficient lighting system. By integrating GPT-4, these agents achieve superior decision-making and adaptability without the need for extensive training. We compare this approach with both traditional neuroevolutionary methods and solutions provided by software engineers, underlining the potential of GPT-driven multiagent systems in IoT. Structurally, the paper outlines the incorporation of GPT into the agent-driven Framework for the Internet of Things (FIoT), introduces our proposed GPT-in-the-loop approach, presents comparative results in the IoT context, and concludes with insights and future directions.

One of the most neglected sources of energy loss is streetlights which generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of the operation, streetlights are frequently seen being turned ON during the day and OFF in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight 'ON' and 'OFF' to save energy consumption costs. According to the aforementioned approach, geolocation senso

The Streetlight Effect represents an observation bias that occurs when individuals search for something only where it is easiest to look. Despite the significant development of Post-Publication Peer Review (PPPR) in recent years, facilitated in part by platforms such as PubPeer, existing literature has not examined whether PPPR is affected by this type of bias. In other words, if the PPPR mainly concerns publications to which researchers have direct access (eg to analyze image duplications, etc.). In this study, we compare the Open Access (OA) structures of publishers and journals among 51,882 publications commented on PubPeer to those indexed in OpenAlex database (\#156,700,177). Our findings indicate that OA journals are 33% more prevalent in PubPeer than in the global total (52% for the most commented journals). This result can be attributed to disciplinary bias in PubPeer, with overrepresentation of medical and biological research (which exhibits higher levels of openness). However, after normalization, the results reveal that PPPR does not exhibit a Streetlight Effect, as OA publications, within the same discipline, are on average 16% less prevalent in PubPeer than in the glob