The swift development of Internet of Things (IoT) technology has led to the expanded application of sensor networks in smart cities. Streetlights, as a vital aspect of smart city infrastructure, serve as a reflection of a town’s development. While streetlights are crucial for ensuring optimal visibility, road security, and public space utilization at night, they contribute significantly to electricity consumption. Governments across the globe are committed to reducing residential and industrial electricity usage. Regarding electricity usage, the Intelligent Street Lights System based on LED lights is a crucial concept today. This system functions by switching on the LED lights when a vehicle approaches and adjusting them to a dimmed or off state when no vehicle is present. The primary focus of this research paper, implementing an Intelligent Street Lights System based on LED lights, has emerged as a critical approach to achieving energy efficiency and cost reduction. Experimental findings demonstrate the potential for up to 80% energy savings compared to traditional streetlamp systems. Additionally, the system enables remote monitoring and intelligent management of urban street light conditions through terminal devices.

The increasing demand for reliable power distribution necessitates advanced monitoring solutions for distribution transformers. This paper presents an IoT-driven health monitoring system designed to enhance the reliability and efficiency of smart power grids. The system integrates sensors to measure voltage, current, oil temperature, and body temperature, ensuring real-time data acquisition and fault detection. An ESP32 microcontroller processes sensor data and transmits it to the Blynk IoT platform for remote monitoring and predictive maintenance. The system effectively identifies phase failures, earth faults, overheating, and other anomalies, allowing for timely intervention and reduced downtime. Unlike conventional manual inspection methods, this low-cost solution provides continuous monitoring, improving transformer lifespan and operational efficiency. The proposed approach offers a scalable and cost-effective strategy for smart power grid applications, promoting sustainable energy management through data-driven decision-making. Future enhancements may include AI-based fault prediction and expanded integration with smart grid infrastructures.

DeepFake technology is rapidly advancing, fundamentally changing how digital media is created and consumed. This evolution brings both exciting opportunities, such as enhanced entertainment and communication, and serious challenges, including misinformation, fraud, and threats to privacy and security. Using advanced AI methods such as deep learning and generative models, DeepFakes can create highly realistic but fake images, text, audio, and videos that are hard to identify. These synthetic media are often hard to distinguish from real content, which raises concerns about their misuse. This research presents a comprehensive review of the latest DeepFake detection methods across different types of media, including images, audio, video, and text. We investigate the application of deep learning models for detecting fraudulent information, including transformers, hybrid frameworks, convolutional neural networks (CNNs), and generative adversarial networks (GANs). By analyzing a wide range of studies, we compare these methods based on their accuracy, speed, and ability to perform well on various datasets such as FaceForensics++, DFDC, and DeepFakeTIMIT. One significant issue we identify is the lack of diversity in current datasets, which often leads to biased detection results and weak performance when models encounter new or real‐world DeepFakes. The present research emphasizes the need to develop better dataset standards that respect ethical and privacy concerns. Improving these areas will improve the reliability and generalization of detection systems. Our findings emphasize the need to develop adaptive detection models, privacy‐preserving techniques, and ethical frameworks to address emerging threats. The overall goal of this study is to give practitioners and scholars a comprehensive grasp of the state of DeepFake detection today. By identifying key challenges and suggesting promising future directions, we hope to support efforts to build safer digital environments where trust and authenticity are preserved.

Deep fake technology has significantly advanced the creation of synthetic images and videos, sparking widespread concerns about its potential misuse in spreading misinformation, violating privacy, and enabling identity theft. As these manipulations be-come increasingly sophisticated, the development of reliable detection methods has become a pressing necessity. This research tackles this challenge by proposing a robust deep fake detection pipeline, leveraging a custom dataset created using Roboflow. The dataset is divided into two primary classes: real and fake, with the fake class further categorized into three subtypes based on complexity: easy fake, mid fake, and hard fake. Easy fake images involve basic manipulations that are easily identifiable by the human eye, while mid fake images combine AI-generated and human-generated elements, and hard fake images are entirely AI-generated, posing significant challenges for detection. To ensure authenticity and diversity, real images were collected from personal networks and online repositories. We trained and evaluated four YOLO-based models YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for the detection task. YOLOv8 emerged as the top-performing model, achieving an accuracy of 96.2% in distinguishing between real and fake images. Finally, addressing ethical considerations and developing countermeasures to mitigate the societal impact of deep fakes should remain a priority for future research.