Adaptive data sensing rate in ad-hoc sensor networks for autonomous transport application

A novel algorithm for adaptive control of the data sensing rate in wireless ad-hoc sensor networks (WSN) is presented. The WSN is applied to monitor the environmental parameters e.g. temperature and humidity, inside containers for the food transportation. Using the proposed algorithm, each sensor node can control its sensing intervals according to the surrounding environmental conditions. The data sink receives only the required information from data sources, so that the process of data fusion could be optimized by minimizing the overall data transmissions in WSN. In order to approximate the development trend of the environment, a neural network algorithm is used due to its learning capability and flexibility. The algorithm of adaptive sensing rate is tested in a simulation environment and implemented in a refrigerator truck for a food transport purpose. Both the simulation and implementation results show that the algorithm can autonomously concentrate on the problematic situation and efficiently reduce the redundant data measurements.