Efficient Data-Processing Algorithms for Wireless-Sensor-Networks-Based Planetary Exploration

The Space Wireless Sensor Networks for Planetary Exploration project aims to design a wireless sensor network that consists of small wireless sensor nodes dropped onto the moon surface to collect scientific measurements. Data gathered from the sensors will be processed and aggregated for uploading to a lunar orbiter and subsequent transmission to Earth. In this paper, efficient data-processing/fusion algorithms are proposed, the purpose of which is to integrate the scientific sensor data collected by the wireless sensor network, reducing the data volume without sacrificing the data quality to satisfy energy constraints of wireless-sensor-network nodes operating in the extreme moon environment. The results of an extensive simulation experiment targeted at the Space Wireless Sensor Networks for Planetary Exploration lunar exploration mission are reported, which quantify the performance efficiency of the data-processing scheme. It is shown that the proposed data-processing algorithms can reduce the wireless-sensor-network node energy consumption significantly, decreasing the data transmission energy up to 91%. In addition, it is shown that up to 99% of the accuracy of the original data can be preserved in the final reconstructed data.