Internet of Things for Environmental Management: A Review

Abstract

The emergence of the Internet of Things (IoT) has developed an important technology of environmental management, which provides unprecedented functionalities for real-time sensing, data analysis, and automated control in a variety of ecological fields. This review combines studies of over 108 peer-reviewed publications (2014–2025) that were categorized within five application domains: (1) air quality monitoring, (2) precision agriculture and soil management, (3) water and wastewater monitoring, (4) wildlife and habitat conservation, and (5) smart-city integration. For each domain, we review system architectures, deployment case studies, performance numbers and bottlenecks—like energy harvesting, sensor calibration, interoperability, data security, and equitable access. Finally, we highlight remaining challenges and future work, such as Green IoT paradigm, edge-AI fusion, standardized evaluation protocols, and the novel participatory governance model, to enable scalable, sustainable and socially beneficial deployments.

Introduction

The Internet of Things (IoT) is emerging as a transformative technology for improving environmental sustainability. It utilizes interconnected sensors to monitor parameters like air and water quality, temperature, and soil health in real time. This data-driven approach enhances decision-making for climate change mitigation, pollution control, and resource conservation. However, adoption faces hurdles like system integration, scalability, and data security.

2. IoT Architecture and Applications

IoT systems typically consist of four layers: perception, network, processing, and application. They enable automation across diverse sectors such as healthcare, agriculture, smart cities, and industrial operations. In environmental monitoring, IoT’s real-time data collection helps in detecting pollution levels, managing resources efficiently, and deploying early warning systems. Machine learning integration supports predictive analytics, though issues like interoperability and energy consumption remain challenges.

3. Air Quality Monitoring

IoT-based air quality systems utilize low-cost, portable sensors and wireless communication to offer real-time, high-resolution pollution data. These systems outperform traditional static monitors in spatial-temporal coverage and public accessibility. Challenges include sensor calibration, data protection, and device power efficiency. Integration with AI, blockchain, and next-gen networks (e.g., 6G) enhances system performance, scalability, and public engagement.

4. Wildlife Conservation

IoT has revolutionized wildlife monitoring through GPS collars, RFID tags, and real-time sensor networks that track animal behavior, habitat conditions, and threats like poaching. Integration with AI (e.g., systems like EarthRanger, PAWS) allows predictive poaching risk analysis and smarter patrol planning. Limitations include device size, battery life, connectivity in remote regions, and ethical concerns. LoRa networks and ultra-low-power devices offer potential solutions.

5. Precision Agriculture and Resource Conservation

IoT enables precision farming by monitoring soil moisture, pH, weather, and crop health, supporting efficient irrigation, fertilization, and resource usage. When combined with AI and drones, it boosts crop yields and reduces water and chemical usage. Applications also extend to livestock monitoring. Nonetheless, scalability, network limitations, data privacy, and high costs continue to hinder widespread adoption.

6. Data Analytics & Machine Learning in IoT

Combining IoT with AI and machine learning (ML) significantly enhances environmental control and predictive modeling. ML techniques (e.g., decision trees, neural networks) process sensor data for anomaly detection and trend analysis in water, air, and climate monitoring. Innovations like federated learning and reinforcement learning improve privacy and resource optimization. Challenges include data quality, interpretability, and edge device limitations.

7. Smart Cities and Policy Integration

IoT technologies are central to smart city initiatives, enabling real-time management of waste, energy, and environmental quality. Green IoT strategies promote energy efficiency and carbon reduction. However, challenges in data privacy, interoperability, and infrastructure require coordinated policy frameworks involving government, industry, and citizens. Future systems must be energy-efficient and privacy-preserving.

Conclusion

The combination of Internet of Things (IoT) and artificial intelligence (AI) is changing the landscape of environmental monitoring and control in various fields. IoT enables real-time monitoring and large-scale data mining service for air quality, water resources and ecosystem dynamics, and AI could improve the analytical ability because of the approaches such as predictive model and automatic decision support system. These innovations, taken together, support the move to more sustainable practices, greater operational efficiency, and more innovation in natural resources management. However, it faces major technical, data management, as well as possible ethical issues related to data privacy and fairness. Provisions need to be implemented to address these challenges by developing scalable, resource-conserving approaches and stimulating interdisciplinary engagement in future work. This fusion of IoT and AI represents a new epoch for environmental stewardship and a transformative route to sustainability/resilience across multiple sectors. Environmental Internet of Things (IoT-based) tools allow powerful mechanisms towards achieving sustainability and resilience in environmental management. Tracking critical indicators such as air quality, water resources, and ecological health in real time enables informed decision-making and pre-emptive action. IoT combined with AI and big data analytics can create predictive models, optimize resource use, and make targeted environmental improvements. However, the potential of these technologies cannot be fully exploited without overcoming challenges concerning data security, interoperability, and technological limitations. Environmental IoT initiatives will achieve greater success if they are integrated into wider environmental programs and are inclusive of diverse stakeholders and supported by flexible policy structures. This is a potentially powerful approach for advancing environmental monitoring, guiding sustainable resource management, and supporting the fight against global climate change.

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