Real‐Time Mobile Data Traffic and Noise Monitoring System for AI Data Prediction Using Open Source Frame Work

International Journal of Communication Systems, Journal Year: 2025, Volume and Issue: 38(6)

Published: March 5, 2025

ABSTRACT The predictive analysis of mobile network traffic is important for future generation cellular networks. Knowing user requests in advance enables the system to allocate resources best way possible. In this manuscript, Real‐Time Mobile Data Traffic and Noise monitoring System AI Prediction Using open Source Frame Work (RMTNMS‐OSF) proposed. Unlike previous studies that primarily remained theoretical, research aims identify areas with highest demand 5G internet service also promptly provide information IT professionals. This significant because high services among tech professionals working from home rural areas. developed software now utilizes HTML, OpenLayers, real‐time spatial location data along Google Satellite Map API as its base layer detect locations well ensure uninterrupted high‐speed service. innovation proposed RMTNMS‐OSF model lies integration AI‐driven models geospatial processing optimize performance by dynamically predicting demand, detecting congestion, preventing loss using cost‐effective open‐source technology, mark up a advancement prediction resource allocation. method evaluated existing methods.

Language: Английский

Urban travel carbon emission mitigation approach using deep reinforcement learning

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 13, 2024

The urbanization process has led to a significant increase in energy consumption and carbon emissions, which can be mitigated through scientific urban planning management. This research proposes bottom-up emission mitigation strategy based on deep reinforcement learning (DRL). Using Ningbo City as case study, multi-source data, including points of interest (POI) data transportation system are utilized, along with varying coefficients for different travel modes, construct comprehensive environment areas. proposed DRL model adopts an Actor-Critic framework, iteratively optimizes the land use configuration building type proportions within matrix achieve goal mitigating emissions. Experimental results demonstrate that this approach exhibits reduction effects scenario. By adjusting discount rate reward function, various optimization strategies obtained, such short-term long-term strategies, achieving reductions 0.47% 0.61%, respectively, notably higher than 0.39% expected if emissions were uniformly distributed across matrix. findings highlight potential DRL-based approaches adaptive data-driven mitigation.

Language: Английский

Exploring the Role of Artificial Intelligence in Achieving a Net Zero Carbon Economy in Emerging Economies: A Combination of PLS-SEM and fsQCA Approaches to Digital Inclusion and Climate Resilience

Sustainability, Journal Year: 2024, Volume and Issue: 16(23), P. 10299 - 10299

Published: Nov. 25, 2024

In this paper, we examine the role of artificial intelligence (AI) in sovereignty and carbon neutrality, emphasizing digital inclusion climate-resilient AI strategies for emerging markets. Considering previous studies on neutrality climate research along with technology policy frameworks as a guide, paper undertakes Partial Least Squares Structural Equation Modelling (PLS-SEM) outcomes. At same time, fuzzy-set Qualitative Comparative Analysis (fsQCA) is used to reveal different configurations leading achieving resilience. The model covers various aspects AI-enabled policy, including adoption, frameworks, literacy, public engagement. Survey data were collected from key stakeholders sectors, local communities using structured survey understand their attitudes towards negative emissions technologies prominent experts countries like Vietnam, Italy, Malaysia, Greece. PLS-SEM results importance developing critical strategic dimension (Data analytics capability support). Some fsQCA findings present heterogeneous outcomes, highlighting complex combinations inclusion, resilience which are industry-specific. This study would further enrich literature concerning by exploring AI, interactions. Theoretically, practical enriching suggestions future derived help infuse sustainable actions.

Language: Английский