Enhancing Smart Parking Management through Machine Learning and AI Integration in IoT Environments DOI Creative Commons
Vesna Knights,

Olivera Petrovska,

Marija Prchkovska

и другие.

IntechOpen eBooks, Год журнала: 2024, Номер unknown

Опубликована: Авг. 23, 2024

The integration of Internet Things (IoT) technology has profoundly transformed urban life, particularly in the realm parking management. Smart systems harness capabilities IoT to optimize space utilization, alleviate congestion, and elevate user experience. This chapter delves into intricate process data collection within IoT-enabled smart environments, with a specific emphasis on seamless machine learning artificial intelligence (AI) techniques. By conducting comprehensive analysis various sources, algorithms, AI technologies, this elucidates how leverage intelligent enhance operational efficiency effectiveness. Through convergence IoT, learning, AI, are poised revolutionize mobility drive sustainable development.

Язык: Английский

FindMySpot: AI & AR Based Parking System DOI

G. Padmapriya,

Alok Prasad,

Vrutika Panikar

и другие.

SSRN Electronic Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

A new initiative has been endorsed to develop a user-friendly smart parking system that operates with minimal human intervention. The proposed leverages computer vision and AR/VR technology create seamless experience. Upon arrival, the scans vehicle's license plate verifies user's details using combination of advanced techniques. suitable spot is then located based on real-time availability, user guided slot through an immersive navigation system. also integrates robust security features, employing sensors cameras for comprehensive surveillance, including automated recognition emergency alerting mechanisms. This dual-layer ensures vehicle safety while minimizing Additionally, platform offers mobile application, featuring payment processing, customizable interfaces, community page enhanced interaction. Our comparative analysis highlights system's superiority over traditional methods, which often rely static maps manual searching, leading inefficiencies dissatisfaction. On contrary, our recommended solution provides experience easy, effective safe especially considering difficulties presented by cities, most notably in places like India where this not common. information provided via application easy-to-use interface handling specifics, involvement. Furthermore, it internet-based storage acts as central place data management verification users. Proposed AI-driven systems have found satisfactorily meet demands showing functioned optimally high levels precision time plus features working perfectly even under conditions.

Язык: Английский

Процитировано

0

Challenges in Pursuing AI Transparency DOI Creative Commons

Marka F. Ellertson,

Richard R. Sharp

The American Journal of Bioethics, Год журнала: 2025, Номер 25(3), С. 4 - 6

Опубликована: Фев. 24, 2025

Язык: Английский

Процитировано

0

Integrating Machine Learning and AI into IoT-Enabled Smart Parking DOI
Vesna Knights,

Olivera Petrovska,

Marija Prchkovska

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Machine Learning Models and Mathematical Approaches for Predictive IoT Smart Parking DOI Creative Commons
Vesna Knights,

Olivera Petrovska,

Jasmina Bunevska-Talevska

и другие.

Sensors, Год журнала: 2025, Номер 25(7), С. 2065 - 2065

Опубликована: Март 26, 2025

This paper aims to create an innovative approach improving IoT-based smart parking systems by integrating machine learning (ML) and Artificial Intelligence (AI) with mathematical approaches in order increase the accuracy of availability predictions. Three regression-based ML models, random forest, gradient boosting, LightGBM, were developed their predictive capability was compared using data collected from three locations Skopje, North Macedonia 2019 2021. The main novelty this study is based on use autoregressive modeling strategies lagged features Z-score normalization improve time series forecasts. Bayesian optimization chosen for its ability efficiently explore hyperparameter space while minimizing RMSE. able capture temporal dependencies more effectively than other resulting lower RMSE values. LightGBM model produced R2 0.9742 0.1580, making it best prediction. Furthermore, system architecture also deployed which included real-time collection sensors placed at entry exit lots individual slots. integration ML, AI, IoT technologies improves efficiency management system, reduces traffic congestion and, most importantly, offers a scalable development urban mobility solutions.

Язык: Английский

Процитировано

0

Bluetooth-Based Dynamic Nexus Mesh Communication Network for High-Density Urban Interaction Spaces DOI Creative Commons
Yufei Hu,

Ngai Cheong,

Muya Yao

и другие.

Sensors, Год журнала: 2025, Номер 25(8), С. 2495 - 2495

Опубликована: Апрель 15, 2025

Traditional centralized network structures exhibit clear scalability and communication efficiency bottlenecks. This paper proposes a solution based on bidirectional unweighted heterogeneous graph, Dynamic Nexus Mesh Communication, designed to improve optimize user experience, particularly for high-density urban interaction spaces. DNMC reduces the reliance central super nodes in traditional networks by redistributing centrality while increasing betweenness of broader set nodes. Additionally, introduces multiple node types, improving both robustness network. Through series simulation experiments, we compared performance with that networks. The results show achieves an average delay 1.5824 s, representing 13.69% improvement over architectures. These findings demonstrate significantly outperforms terms efficiency, at larger scales, where exhibits enhanced stability scalability.

Язык: Английский

Процитировано

0

Enhancing Smart Parking Management through Machine Learning and AI Integration in IoT Environments DOI Creative Commons
Vesna Knights,

Olivera Petrovska,

Marija Prchkovska

и другие.

IntechOpen eBooks, Год журнала: 2024, Номер unknown

Опубликована: Авг. 23, 2024

The integration of Internet Things (IoT) technology has profoundly transformed urban life, particularly in the realm parking management. Smart systems harness capabilities IoT to optimize space utilization, alleviate congestion, and elevate user experience. This chapter delves into intricate process data collection within IoT-enabled smart environments, with a specific emphasis on seamless machine learning artificial intelligence (AI) techniques. By conducting comprehensive analysis various sources, algorithms, AI technologies, this elucidates how leverage intelligent enhance operational efficiency effectiveness. Through convergence IoT, learning, AI, are poised revolutionize mobility drive sustainable development.

Язык: Английский

Процитировано

2