CTLL: A Cell-Based Transfer Learning Method for Localization in Large Scale Wireless Sensor Networks

Localization is emerging as a fundamental component in wireless sensor network and is widely used in the field of environmental monitoring, national and military defense, transportation monitoring, and so on. Current localization methods, however, focus on how to improve accuracy without considering the robustness. Thus, the error will increase rapidly when nodes density and SNR (signal to noise ratio) have changed dramatically. This paper introduces CTLL, Cell-Based Transfer Learning Method for Localization in WSNs, a new way for localization which is robust to the variances of nodes density and SNR. The method combines samples transfer learning and SVR (Support Vector Regression) regression model to get a better performance of localization. Unlike past work, which considers that the nodes density and SNR are invariable, our design applies regional division and transfer learning to adapt to the variances of nodes density and SNR. We evaluate the performance of our method both on simulation and realistic deployment. The results show that our method increases accuracy and provides high robustness under a low cost.