Fuzzy Logic Driven Load Balanced Channel Allocation Algorithm for Enhancing Channel Utilization and Throughput in Large Heterogeneous IEEE 802.11ah Network

A wireless sensor network typically comprises of heterogeneous nodes with varying sampling rates and sample sizes. IEEE 802.11ah supports up to 8192 sensor nodes, offering a data rate of approximately 16 Mbps from an individual access point. It utilizes a feature called Restricted Access Window (RAW) in the MAC protocol; a limited number of nodes are assigned to access the channel at a designated time to reduce contention. However, most of the existing mechanisms do not account for this heterogeneity, and they randomly group the nodes. In this work, we introduce a Load Balanced Channel Allocation (LBCA) algorithm for a large heterogeneous sensor network. This algorithm collects the sampling interval and sample size from the node and identifies the appropriate group based on the fuzzy logic approach. We calculate the grouping probability (GP) for each RAW and assign the node to the RAW with the highest GP. The number of nodes, the standard deviation of RAW utilization ratio among the groups and the average transmission efficiency rate were considered as the input parameters for the fuzzy logic. We simulated this work using ns-3 simulator and evaluated the performance against random grouping in terms of improvement in channel utilization of around 7.65%, throughput by 1.37%, decrease in collision rate by 16.42% and minimization of delay by about 8.57%. These results highlight the effectiveness of the LBCA algorithm in achieving load balance along with efficient channel utilization.