Environmental Monitoring with IoT and Big Data: A Systematic Review of Advances and Challenges.
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Abstract
The integration of the Internet of Things (IoT) and Big Data analytics has revolutionized
environmental monitoring, enabling quantitative and qualitative advances in natural resource
management. This systematic review, based on 55 studies published between 2019 and 2024 and
following the PRISMA protocol, identifies trends, achievements and challenges in the application of
these technologies. In agriculture, the adoption of IoT sensors has reduced water consumption by 35%
and increased productivity by 12%. In water management, IoT nanosensors have increased the
detection capacity of contaminants fivefold over conventional methods. Deep learning models, such
as LSTM networks fed by data from more than 15,000 IoT nodes, have achieved greater than 92%
accuracy in predicting extreme weather events. In addition, the integration of IoT and Big Data
platforms in cities has helped reduce CO₂ emissions by 15% by optimizing traffic and energy
consumption.
Innovations such as edge computing architecture have decreased processing latency by 68%, and
digital twins have improved the accuracy of predictive models by 40%. Globally, the environmental
IoT market is projected to be worth $21.5 billion by 2028, with more than 152 cities implementing
IoT networks for air quality and 35 million hectares under precision agriculture. However, challenges
remain, such as interoperability (23% of deployments), initial deployment cost and the technology
gap between regions. The IoT-Big Data convergence drives a transition towards proactive
environmental management systems, although its scalability depends on overcoming these challenges.
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