Cognitive WSN access based on local WLAN traffic estimation

Abstract: The recent growth in the deployment of wireless networks that access the unlicensed ISM bands has introduced the issue of co-existence between different kinds of wireless technologies. In this work we address the enhancement of the performance of the low-cost Wireless Sensor Network nodes competing for spectrum access with the WLAN devices, that are more powerful in terms of transmission and computational power. Those low-cost WSN nodes are usually battery powered devices with low computational capabilities and limited battery capacity, thus the traditional methods for accessing the wireless spectrum should be revised, including throughput and energy efficiency optimization while considering the hardware constraints of the nodes. In this work we provide tools for spectrum activity prediction that will be used by a WSN cognitive MAC to minimize the collision probability with WLAN transmissions, hence the number of retransmissions, increasing consequently the energy efficiency of communication. We propose two approaches for WLAN spectrum activity modeling considering different probability distributions for the idle WLAN spectrum periods. The first considers the universal activity of the WLAN, while the second takes into account the hardware limitations of the sensors in terms of the detection range. For both modeling options we provide low-complexity algorithms based on either numerical estimation methods or neural networks for the parameter estimation that are suitable for the employment in the WSN. We complete the work with an extensive performance analysis in terms of estimation accuracy and we show that, even with a spatial limitedknowledge of the WLAN traffic, the sensors could derive a good approximation of the universal activity model.