Abstract
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.
Highlights
The Internet of Things (IoT) has become a widespread phenomenon due to its progressive capabilities [1]
Comparison was performed between the two approaches, i.e., deep learning and basic machine learning algorithms, namely, using support vector machine (SVM), decision tree (DT) and multi-layer perceptron neural network (MLP)
Using the MLP deep learning model, the performance of the system to classify location based on received signal strength indicator (RSSI) data is better compared to the basic machine learning classifiers
Summary
The Internet of Things (IoT) has become a widespread phenomenon due to its progressive capabilities [1]. The WSN provides various advanced technologies such as sensor development, wireless communication and distributed information processing and because of this, it has received a great amount of attention in IoT communities. A number of sensor nodes are deployed in WSN applications. These sensors are connected through wireless communication to perform several tasks such as sensing, recognizing, and monitoring. These tasks are used in many applications, for example, object tracking [2], intruder detection [3], indoor fire detection [4] and human localization as well as activity recognition in indoor environments [5,6]
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