Dynamic channel selection algorithms for coexistence of wireless sensor networks and wireless LANs

Due to the advances in wireless technology and spectrum scarcity, unlicensed band heterogeneous networks are growing rapidly. Increasing users of these networks should compete for the shared spectrum. Therefore, interoperability and coexistence of such networks are becoming key issues that require novel media access protocols equipped with dynamic channel selection to avoid harmful interference. In this paper we focus on dynamic channel selection for coexistence of IEEE 802.11 Wireless LAN and IEEE 802.15.4 sensor networks. Dynamic channel selection algorithm can either be implemented on top of an existing wireless sensor network or assisted with an auxiliary spectrum sensing device. In this research couple of dynamic channel selection algorithms have been developed and implemented to evaluate the added value of the auxiliary sensing device. As such, we propose a novel energy-aware metric to detect and quantify the harmfulness of dynamic interference. We also investigated the impact of interference dynamism on algorithms performance and validated the efficiency of the implemented mechanisms by three sets of experiments. Experiments results primarily validate the efficiency of both interference mitigation techniques. Besides, these measurements suggest that the auxiliary sensing device is most beneficial for highly complex interference profiles

Surrogate modeling based cognitive decision engine for optimization of WLAN performance

Due to the rapid growth of wireless networks and the dearth of the electromagnetic spectrum, more interference is imposed to the wireless terminals which constrains their performance. In order to mitigate such performance degradation, this paper proposes a novel experimentally verified surrogate model based cognitive decision engine which aims at performance optimization of IEEE 802.11 links. The surrogate model takes the current state and configuration of the network as input and makes a prediction of the QoS parameter that would assist the decision engine to steer the network towards the optimal configuration. The decision engine was applied in two realistic interference scenarios where in both cases, utilization of the cognitive decision engine significantly outperformed the case where the decision engine was not deployed