Water Resources Management, Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 14, 2024
Language: Английский
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
Citations
4
Published: Jan. 1, 2025
Language: Английский
Citations
1
Journal of Natural Fibers, Journal Year: 2025, Volume and Issue: 22(1)
Published: Jan. 30, 2025
Language: Английский
Citations
0
Open Computer Science, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 1, 2025
Abstract The high development of sensors and wireless network technology has led to the widespread application sensor networks in field environmental monitoring. How establish efficient, fast, stable data collection algorithms become a hot research field. Given this, dynamic clustering multi-hop algorithm is proposed based on neighbor propagation algorithm, low-power adaptive layered routing protocol, priority strategy. final experimental results indicated that only entered significant decay period after 2,000 rounds collection, indicating under same conditions, had better transmission performance. In Scenario 1, survival rate was still close 80% at 300 rounds. 2 75% 1,500 3, remaining decreased below 50% 100 rounds, while remained 90% 4 dropped by 500 70% experiment fully demonstrates strong comprehensive performance, best stability, highest energy utilization efficiency. Therefore, study survivability performance advantages various scenarios.
Language: Английский
Citations
0
Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105182 - 105182
Published: May 1, 2025
Language: Английский
Citations
0
Biology, Journal Year: 2025, Volume and Issue: 14(5), P. 520 - 520
Published: May 8, 2025
Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, conservation planning. This systematic review follows the PRISMA framework to analyze AI applications freshwater studies. Using structured literature search across Scopus, Web of Science, Google Scholar, we identified 312 relevant studies published between 2010 2024. categorizes into assessment, ecological risk evaluation, strategies. A bias assessment was conducted using QUADAS-2 RoB 2 frameworks, highlighting methodological challenges, such measurement inconsistencies model validation. The citation trends demonstrate exponential growth AI-driven with leading contributions from China, United States, India. Despite growing use this field, also reveals several persistent including limited data availability, regional imbalances, concerns related generalizability transparency. Our findings underscore AI’s potential revolutionizing but emphasize need for standardized methodologies, improved integration, interdisciplinary collaboration enhance insights efforts.
Language: Английский
Citations
0
Sensors, Journal Year: 2024, Volume and Issue: 24(14), P. 4433 - 4433
Published: July 9, 2024
The advent of internet things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues real-time surveillance and assessment operational conditions intricate mechanical systems. Nowadays, system monitoring technologies are extensively utilized various sectors, such as rotating reciprocating machinery, expansive bridges, aircraft. Nevertheless, comparison to standard frameworks, large amusement facilities, which constitute primary manned electromechanical installations parks scenic locales, showcase myriad structural designs multiple failure patterns. predominant method fault diagnosis still relies on offline manual evaluations intermittent testing vital elements. This practice heavily depends inspectors’ expertise proficiency effective detection. Moreover, periodic inspections cannot provide immediate feedback safety status crucial components, they lack preemptive warnings potential malfunctions, fail elevate measures during equipment operation. Hence, developing an grounded IoT sensor networks is paramount, especially considering nuances risk profiles facilities. study aims develop customized sensors platform roller coasters, encompassing design fabrication platforms data acquisition processing. ultimate objective enable timely when signals deviate from normal ranges or violate relevant standards, thereby facilitating prompt identification hazards faults.
Language: Английский
Citations
2
Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(4), P. 5813 - 5829
Published: July 16, 2024
Language: Английский
Citations
2
Water, Journal Year: 2024, Volume and Issue: 16(20), P. 2951 - 2951
Published: Oct. 17, 2024
Water is a vital resource, and its quality has direct impact on human health. Groundwater, as one of the primary water sources, requires careful monitoring to ensure safety. Although manual methods for testing are accurate, they often time-consuming, costly, inefficient when dealing with large complex data sets. In recent years, machine learning become an effective alternative assessment. However, current approaches still face challenges, such limited performance individual models, minimal improvements from optimization algorithms, lack dynamic feature weighting mechanisms, potential information loss simplifying model inputs. To address these this paper proposes hybrid model, BS-MLP, which combines GBDT (gradient-boosted decision tree) MLP (multilayer perceptron). The leverages GBDT’s strength in selection MLP’s capability manage nonlinear relationships, enabling it capture interactions between parameters. We employ Bayesian fine-tune model’s parameters introduce feature-weighting attention mechanism develop BS-FAMLP dynamically adjusts weights, enhancing generalization classification accuracy. addition, comprehensive parameter strategy employed maintain integrity. These innovations significantly improve efficiency handling environments imbalanced datasets. This was evaluated using publicly available groundwater dataset consisting 188,623 samples, each 15 corresponding labels. shows strong performance, optimized hyperparameters adjusted mechanism. Specifically, achieved accuracy 0.9616, precision 0.9524, recall 0.9655, F1 Score 0.9589, AUC score 0.9834 test set. Compared single improved by approximately 10%, compared other models additional optimal balance computational efficiency. core objective study utilize acquired efficient assessment aim streamlining traditional laboratory-based analysis processes. By developing reliable research provides robust technical support safety management.
Language: Английский
Citations
2
Technologies, Journal Year: 2024, Volume and Issue: 12(11), P. 211 - 211
Published: Oct. 23, 2024
Water is a critical resource for human survival worldwide, and its availability quality in natural reservoirs such as lakes rivers must be monitored. In that way, wireless dynamic sensor networks can help monitor water quality. These have significantly advanced across various sectors, including industrial automation environmental monitoring. Moreover, the Internet of Things has emerged global technological marvel, garnering interest ability to facilitate information visualization ease deployment—the combination improves monitoring helps care this vital resource. This article presents design deployment network comprising mobile node outfitted with multiple sensors remote aquatic navigation stationary similarly equipped linked server via IoT. Both nodes measure parameters like pH, temperature, total dissolved solids (TDS), enabling real-time data through user interface generating database future reference. The integrated control system within developed enhances node’s survey points interest. project enabled aforementioned parameters, recorded being stored subsequent graphing analysis using facilitated collection at interest, allowing graphical representation parameter evolution. included consistent temperature trends, neutral alkaline zone pH levels, variations (TDS) by node, reaching up 100 ppm.
Language: Английский
Citations
1