White Space Prediction for Low-Power Wireless Networks: A Data-Driven Approach
2018 14th International Conference on Distributed Computing in Sensor Systems (DCOSS)(2018)
摘要
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.
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关键词
Cross Technology Interference, low-power wireless communication, wireless sensor networks, interference modeling, white space, predictive models
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