AI and ML-driven deployment and optimization in wireless sensor networks
- Submission status
- Open
- Submission deadline
AI and machine learning (ML) technologies are revolutionizing the deployment and optimization of wireless sensor networks (WSNs). These advanced techniques enable the efficient placement and management of sensor nodes, enhancing network coverage, energy efficiency, and data accuracy. Studies explore methods like federated learning, which allows distributed model training across sensor nodes, while preserving data privacy. Using these AI or ML-driven approaches aids in developing robust and scalable WSNs that can adapt to dynamic environments and support various applications, from smart cities to environmental monitoring.
This Collection calls for submissions of original research into techniques that help to develop and deploy strategies of AI or ML-driven design for wireless sensor networks, contributing to the advancement of intelligent and efficient network systems.
