Review of smart water management: IoT and AI in water and wastewater treatment

World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(1), P. 1373 - 1382

Published: Jan. 19, 2024

Integrating the Internet of Things (IoT) and Artificial Intelligence (AI) in smart water management revolutionizes sustainable resource utilization. This comprehensive review explores these technologies' benefits, challenges, regulatory implications, future trends. Smart enhances operational efficiency, predictive maintenance, conservation while addressing data security infrastructure investment challenges. Regulatory frameworks play a pivotal role shaping responsible deployment AI IoT, ensuring privacy ethical use. Future trends include advanced sensors, decentralized systems, quantum computing, blockchain for enhanced security. The alignment with Sustainable Development Goals (SDGs) underscores transformative potential achieving universal access to clean water, climate resilience, inclusive, development. As we embrace technologies, collaboration, public awareness, considerations will guide evolution intelligent equitable systems.

Language: Английский

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Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data

Electronics, Journal Year: 2025, Volume and Issue: 14(4), P. 696 - 696

Published: Feb. 11, 2025

The integration of artificial intelligence (AI) agents with the Internet Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, more effective decision making. This comprehensive literature review explores AI IoT technologies within sciences, particular focus on applications related to water quality climate data. methodology involves systematic search selection relevant studies, followed by thematic, meta-, comparative analyses synthesize current research trends, benefits, challenges, gaps. highlights how enhances IoT’s collection capabilities through predictive modeling, real-time analytics, automated making, thereby improving accuracy, timeliness, efficiency systems. Key benefits identified include enhanced precision, cost efficiency, scalability, facilitation proactive management. Nevertheless, this encounters substantial obstacles, including issues quality, interoperability, security, technical constraints, ethical concerns. Future developments point toward enhancements technologies, incorporation innovations like blockchain edge computing, potential formation global systems, greater public involvement citizen science initiatives. Overcoming these challenges embracing new technological trends could enable play pivotal role strengthening sustainability resilience.

Language: Английский

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Artificial Intelligence and Machine Learning in Tribology: Selected Case Studies and Overall Potential

Advanced Engineering Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

Artificial intelligence (AI) and machine learning (ML) have been the subjects of increased interest in recent years due to their benefits across several fields. One sector that can benefit from these tools is tribology industry, with an emphasis on friction wear prediction. This industry hopes train utilize AI algorithms classify equipment life status forecast component failure, mainly using supervised unsupervised approaches. article examines some methods used accomplish this, such as condition monitoring for predictions material selection, lubrication performance, lubricant formulation. Furthermore, ML support determination tribological characteristics engineering systems, allowing a better fundamental understanding friction, wear, mechanisms. Moreover, study also finds continued use requires access findable, accessible, interoperable, reusable data ensure integrity prediction tools. The advances show considerable promise, providing more accurate extensible than traditional

Language: Английский

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Human-Machine Nexus for Digital Rebound Fostering Futuristic Energy-Efficiency

Practice, progress, and proficiency in sustainability, Journal Year: 2024, Volume and Issue: unknown, P. 222 - 244

Published: June 28, 2024

Human-machine interaction plays a pivotal role in realizing energy-efficient and sustainable urban mobility. There is vital contribution of HMI facilitating more environmentally responsible transportation solutions. Through the seamless between users, smart infrastructure, autonomous vehicles, HMI-driven approaches promise to optimize traffic flows, reduce energy consumption, minimize emissions. In rapidly urbanizing world, evolution smart-sustainable mobility pressing concern, necessitating judicious integration cutting-edge technology with ecological sustainability. This chapter explores multifaceted nexus human-machine interaction, technology, sustainability, mobility, specific focus on footprint within context systems.

Language: Английский

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Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution

Water, Journal Year: 2024, Volume and Issue: 16(23), P. 3380 - 3380

Published: Nov. 24, 2024

This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance accuracy, speed, accessibility monitoring. Data collected from various samples in Algeria were analyzed determine key such as conductivity, turbidity, pH, total dissolved solids (TDS). These measurements integrated into deep neural networks (DNNs) predict indices sodium adsorption ratio (SAR), magnesium hazard (MH), percentage (SP), Kelley’s (KR), potential salinity (PS), exchangeable (ESP), well Water Quality Index (WQI) Irrigation (IWQI). DNNs model, optimized through selection activation functions hidden layers, demonstrated high precision, with a correlation coefficient (R) 0.9994 low root mean square error (RMSE) 0.0020. AI-driven methodology significantly reduces reliance traditional laboratory analyses, offering real-time assessments that are adaptable local conditions environmentally sustainable. provides practical solution resource managers, particularly resource-limited regions, efficiently monitor make informed decisions public health agricultural applications.

Language: Английский

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Smart-Circular strategies for managing biomass resource challenges: A novel approach using circular intuitionistic fuzzy methods

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 314, P. 118690 - 118690

Published: June 27, 2024

Language: Английский

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Blockchain-Enabled Security Enhancement for IoT Networks: Integrating LEACH Algorithm and Distributed Ledger Technology

Journal of Machine and Computing, Journal Year: 2025, Volume and Issue: unknown, P. 483 - 495

Published: Jan. 3, 2025

The rapid proliferation of Internet Things (IoT) networks has significantly advanced various sectors such as smart cities, healthcare, and industrial automation, but it also introduced substantial security challenges. Protecting data integrity, confidentiality, availability in these is critical, yet traditional measures often fall short due to the decentralized resource-constrained nature IoT devices. Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, designed optimize energy consumption sensor networks, lacks intrinsic features. To address challenges, this paper proposes a novel approach that integrates LEACH with Distributed Ledger Technology (DLT), specifically blockchain. Blockchain’s immutable ledger can enhance integrity within networks. methodology involves modifying incorporate blockchain for secure transmission. In clustering phase, forms clusters designates cluster head (CH) aggregation Each CH maintains local log verify transactions its cluster, using consensus mechanism ensure integrity. Smart contracts are implemented automate policies detect anomalies, while encryption digital signatures provide additional layers. Simulations NS-3 simulator showed promising results: was reduced by 18% compared LEACH, latency increased 5% processing overhead, throughput improved 12%, metrics indicated 25% improvement 30% reduction successful attack attempts. conclusion, integrating algorithm enhances efficiency This leverages optimization robust framework blockchain, offering scalable solution diverse applications. Future research will focus on optimizing operations reduce further exploring model's applicability scenarios.

Language: Английский

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Artificial intelligence techniques applications in the wastewater: A comprehensive review

E3S Web of Conferences, Journal Year: 2025, Volume and Issue: 605, P. 03006 - 03006

Published: Jan. 1, 2025

There are some challenges firms the wastewater treatment, numerous hurdles concerning enhancement of energy efficiency, compliance with increasingly stringent water quality regulations, and maximizing resource recovery opportunities. In recent years, computational models have garnered acknowledgment as potent instruments for tackling these various challenges, bolstering operational economic effectiveness treatment plants (“WWTPs”). Also, review discusses application (AI) algorithms on (WWTPs), predicting (“WWTP”) effluent properties, inflows, anomaly detecting, optimization. The critical gaps future directions in including explain ability data-driven or transfer Learning processes reinforcement learning, also addressed.

Language: Английский

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Machine learning and artificial intelligence application in automotive water quality monitoring, analysis, and management

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 89 - 109

Published: Jan. 1, 2025

Language: Английский

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Machine Learning for Accessible and Precise Assessment in Smart Monitoring Systems

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 135 - 148

Published: Jan. 1, 2025

Language: Английский

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