Context-Aware Software-Defined Wireless Networks: An AI-based Approach to Deal with QoS

The model was evaluated using several SDWNs that operate under dynamic conditions. The experimental results show how incorporating artificial intelligence into the control loop enables the use of the context information to enhance the predictability of the medium access protocol parameters, handling thus different traffic QoS according to the demand of IoT applications. In the data you can find the evaluation results for different network sizes (number of nodes) and type of traffic (high, medium, low and non real-time) obtained with four different methods: i) Best effort IEEE 802.11 (no QoS handling), ii) Orig.

SDWN (IEEE 802.11e standard configuration), iii) Modif. SDWN (IEEE 802.11e modified parameters configuration based on heuristic approach), and iv) IA SDWN (IEEE 802.11e configuration modified dinamically with an intelligent agent). For more details on the four methods and simulation scenarios and experiments see the paper: Context-Aware Software-Defined Wireless Networks: An AI-based Approach to Deal with QoS to be published in Future Internet.