SenseCast: Context Prediction for Optimisation of Network Parameters in Wireless Sensor Networks

SenseCast investigates context prediction in wireless sensor networks (WSNs) to dynamically optimize communication parameters like duty cycling and transmission power.

Use predictive models to adapt WSN parameters. Improve energy efficiency and data quality. Apply machine learning to optimize distributed communication.

Combines context prediction with adaptive networking. Integrates environmental and system context. Demonstrates smart network adaptation in TECO testbeds.

Extends lifetime of sensor networks. Enhances reliability of IoT communication. Bridges ML and network optimization research.