Enhancing the Accuracy of Wi-Fi Tomographic Imaging Using a Human-Interference Model

Abstract

Abstract Since the reconstruction process of Wi-Fi Tomographic imaging is an ill-posed inverse problem and due to the unstable nature of the measurements, converting the signal strength measurements into a two dimensional image is computationally expensive. Therefore this research is intended to propose a human-interference model based on the probabilistic model that can be used to enhance the accuracy of the tomographic imaging process while reducing the computational cost. This dissertation discusses the usage of the human-interference model to im- prove the Wi-Fi tomographic imaging including implementation of the algorithm and improved image reconstruction and practical results in real-world. From a research conducted by Patwari et al, it has been concluded that, due to the ill- posedness of the radio tomographic imaging process,many di erent attenuation elds can lead to the same noisy measurement data.There is no unique solution to the least-squares formulation exists. The problem is made stable by incorporat- ing additional information about the solution into the mathematical framework[17]. The proposed novel methodology uses both regularization and the human in- terference model to enhance the accuracy of the tomographic imaging process. Keywords: Wi-Fi tomography, human-interference model, Sensor Networks