Wireless Sensor Networks Applications in Agriculture, Disaster Prevention and Environmental Management

Dear Colleagues，

Advances in wireless connectivity and sensor technologies complemented by low cost computational power have resulted in the rapid proliferation of monitoring and automation systems. Combined with Internet-of-Things (IoT) applications in agriculture, environmental monitoring and disaster prediction and prevention are expected to make the greatest impact on all communities. Whilst some of the information from wireless sensor networks (WSNs) is used in decision support, the combination with advanced algorithms (e.g. machine learning) is accelerating the pace of development from supervised to autonomous systems.

The effectiveness of WSN based systems require reliable connectivity, energy efficiency, scalability and adaptability to dynamic environments. Implemented on automated, semi-autonomous or autonomous platforms, WSN nodes may be armed with the ability to make simple decisions or aggregate data from multiple sensors for context-aware decisions, e.g. collision avoidance or dead reckoning in unmanned aerial vehicles.

In agriculture, WSNs face unique challenges posed by the changing environment in which they operate. In addition, there is a need to minimize power consumption and continually perform self-organizing and self-healing network operations. WSNs are also widely used to monitor environmental conditions, to predict and mitigate the impact of climate change, and for disaster prediction and prevention. Within cities, multiple sensors that range from a low data rate to high bandwidth devices like wireless cameras are often deployed. These present unique challenges.

This Special Issue aims to publish original research in the planning, deployment and management of the expanding ecology of wireless sensor networks in an ever-changing environment. There is particular interest in WSNs in agriculture, natural disaster (e.g. flooding, landslides, etc.) prediction, climate change mitigation systems and smart cities-related research studies.

The topics of interest include, but not limited to:

• Use-case deployments of WSNs and IoT for agriculture/disaster/environmental/smart cities
• WSN topology, standards, protocols and routing control
• Static and dynamic sensor network planning and management
• Real time and near-real time data processing and context-aware WSN decision support systems
• AI, machine learning and deep learning in WSNs for agriculture/disaster/environmental/smart cities
• Application of WSNs and IoT in climate change adaptation, disaster prediction, prevention and mitigation, food security, smart utilities, smart economy and smart supply chain
• Security and privacy in WSNs
• Multi-tier and hybrid WSNs
• WSN for digital twin in agriculture/environmental/disaster/smart city systems
• Use-cases of context awareness and activity monitoring
• Big data analytics in WSN applications
• Multi-layer solutions for WSNs and IoT for smart city services
• Traffic sensing and management for environmental management and smart city applications
• Cloud/fog/edge architecture for WSN
• Communication protocols for WSN

Dr. David L. Ndzi
Dr. Latifah Munirah Kamarudin
Prof. Dr. Wan-Young Chung
Guest Editors

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