Improving the speed and accuracy of indoor localization

Konstantinos Kleisouris
Advances in technology have enabled a large number of computing devices to communicate wirelessly. In addition, radio waves, which are the primary means of transmitting data in wireless communication, can be used to localize devices in the 2D and 3D space. As a result there has been an increasing number of applications that rely on the availability of device location. Many systems have been developed to provide location estimates indoors, where Global Positioning System (GPS) devices do not
more » ... . However, localization indoors faces many challenges. First, a localization system should use as little extra hardware as possible, should work on any wireless device with very little or no modification, and localization latency should be small. Also, wireless signals indoors suffer from environmental effects like reflection, diffraction and scattering, making signal characterization with respect to location difficult. Moreover, many algorithms require detailed profiling of the environment, making the systems hard to deploy. This thesis addresses some of the aforementioned issues for localization systems that rely on radio properties like Received Signal Strength (RSS). The advantage of these systems is that they reuse the existing communication infrastructure, rather than necessitating the deployment of specialized hardware. Specifically, we improved the latency of a particular localization method that relies on Bayesian Networks (BNs). This method has the advantage of requiring a small size of training data, can localize many devices simultaneously, and some versions of BNs can localize without requiring the knowledge of the locations where signal strength properties are collected. We proposed Markov Chain Monte Carlo (MCMC) algorithms and evaluated their performance by introducing a metric which we call relative accuracy.We reduced latency by identifying MCMC methods that improve the relative accuracy to solutions returned by existing statistical packages in as little time as possible. In addition, we parallelized the [...]
doi:10.7282/t3td9xmb fatcat:bwzam5fdhnhw5dh7nbsnqq56la