RSSI-Based for Device-Free Localization Using Deep Learning Technique

Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

Keywords: device-free localization; machine learning classifier; deep learning; big data; wireless networks; classification; received signal strength

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Random subspace oracle (RSO) ensemble to solve small sample-sized classification problems

Under certain situations, researchers were forced to work with small sample-sized (SSS) data. With very limited sample size, SSS data have the tendency to undertrain a machine learning algorithm and rendered it ineffective. Some extreme cases in SSS problems will have to deal with large feature-to-instance ratio, where the high number of features compared to small number of instances will overfit the classification algorithm. This paper intends to solve small sample-sized classification problems through hybrid of random subspace method and random linear oracle ensemble by utilizing binary feature subspace splitting and oracle selection scheme. Experimental results on artificial data indicate the proposed algorithm can outperform single decision tree and linear discriminant classifiers in small sample-sized data, but its performance is identical to k-nearest neighbor classifier due to both shared similar selection approach. Results from real-world medical data indicate the proposed method has better classification performance than its corresponding single base classifier especially in the case of decision tree.

Keywords: Ensemble method, classification, small sample, Euclidian’s distance