White Space Prediction for Low-power Wireless Networks: A Data-Driven Approach

In the 2.4 GHz unlicensed spectrum, the coexistence of WiFi, Bluetooth and IEEE 802.15.4 devices generates increased channel contention. Notably, low-power wireless networks experience packet loss and delays due to interference. To improve the performance of low-power wireless networks under interference, we propose a data driven proactive approach based on interference modeling for white space prediction. We leverage statistical analysis of real-world traces from two indoor environments characterized by varying channel conditions to identify interference patterns. We characterize interference in terms of Inter-Arrival Time (IAT) and number of interfering signals and use a Gaussian Mixture Model (GMM) to accurately estimate the interference distribution as observed by the low-power wireless nodes. Then, we use a Hidden Markov Model (HMM) for white space prediction. Our validation w.r.t. real-world traces from two environments show that our GMM model can estimate interference with an accuracy higher than 94.7%. Moreover, the white space prediction evaluation shows an average accuracy of 97.7% and 89.5% across the two environments.