Wireless Sensor Network-based topology structures for the internet of things localization

Wireless Sensor Network-based localization technique, as one fundamental component of the Internet of Things, has attracted more and more attentions. However, some of the collected data easily result in faulty data with large biases due to the network attacks, large obstacles, network environments and hardware errors, which brings a big challenge for localization. The localization research in such a scenario is still relatively few. The authors try to develop a novel localization algorithm to solve this problem, by characterizing the density information of data distribution with sufficiently exploiting topology structures in WSN, and name it as Location Estimation-Robust Locality Preserving Canonical Correlation Analysis (LE-RLPCCA). Experimental results in realistic environments show that LE-RLPCCA achieves higher robust and stability for faulty data than other publicly available advanced localization algorithms.