Topical Collection on "IoT Applications for Remote Environmental Data Collection and Analysis"
Deadline for manuscript submissions: 30 October 2024
Collection Editors
Hanoi University of Science and Technology, Vietnam
Email: linh.tranhoai@hust.edu.vn, prof.linhtran@gmail.com
University of South Africa, Pretoria, South Africa
Email: yusufaa@unisa.ac.za
Universitas Putra Indonesia YPTK Padang, Indonesia
Email: abulwafa@upiyptk.ac.id
Topical Collection Information
IoT applications for remote environmental data collection enable real-time monitoring, predictive analysis, and more informed decision-making to address environmental challenges. This technology utilizes interconnected devices to collect huge amounts of data from various locations, providing insights that were previously unattainable. IoT devices provide continuous, real-time data collection, which improves the precision and effectiveness of environmental monitoring. They minimize the requirement for onsite employees and operational expenses by enabling the regular monitoring of previously unattainable rural places. Large-scale data collection makes it possible to make data-driven decisions to inform policy, forecast trends, and facilitate quick responses. IoT systems utilize sensor networks to gather data on environmental parameters like temperature, humidity, air and water quality, and soil conditions. This data is transmitted through wireless networks to cloud-based platforms for storage and analysis. Advanced analytics, include machine learning are used to derive actionable insights, and visualization tools aid in data interpretation for informed decision-making.
Geographically dispersed sensors are used by Wireless Sensor Networks (WSNs) to monitor environmental conditions, and satellite communication makes it easier to collect data in remote locations without access to conventional network infrastructure. Large datasets may profit from the scalable storage and robust processing provided by cloud computing. Pattern recognition and predictive modeling are enhanced by machine learning algorithms and big data analytics, which includes anomaly detection and predictive analytics. Integrating IoT with AI enables more sophisticated data analysis and predictive modeling, while edge computing processes data closer to the source, minimizing latency and bandwidth usage. Blockchain technology improves the security and integrity of IoT-collected data. The predictive power of IoT systems will be improved by developments in AI and machine learning, more robust and widely distributed IoT networks will ensure that data is continuously collected even in adverse situations. The development of sustainable Internet of Things (IoT) devices with low environmental effect and deeper integration with GIS-based technologies for comprehensive environmental monitoring will receive more attention.
Despite its potential, it has several challenges include protecting data privacy and security from cyberattacks, establishing system and device interoperability, controlling the energy usage of distant devices, and managing the massive amounts of data that are produced. In order to address these issues, sophisticated data management and processing techniques are used to handle large datasets effectively, low-power devices and solar power are researched, universal standards for device interoperability are created, and advanced encryption and authentication methods are implemented to improve security. However, Environmental monitoring can be revolutionized with IoT applications for remote environmental data collecting and analysis. IoT makes it possible to monitor environmental parameters in an accurate, economical, and efficient manner by utilizing advanced analytics and networked devices.
List of Topics:
- Advancements of Analytics and Machine Learning in IoT Environmental Monitoring.
- Optimization of Real-Time Environmental Data Collection and Analysis with IoT Technologies.
- Role of IoT in Predictive Environmental Analysis and Trend Forecasting.
- Employing IoT for Real-Time Environmental Monitoring and Decision Making.
- Utilization of IoT Sensor Networks for Comprehensive Environmental Data Collection.
- Enhancing Environmental Monitoring Accuracy and Efficiency through IoT.
- Contribution of Satellite Communication in Enhancing IoT Data Collection.
- Analysis of Wireless Sensor Networks for Remote Environmental Monitoring.
- Applications of Edge Computing in IoT Environmental Systems.
- Strategies to Overcome Challenges in IoT for Environmental Monitoring.