Locality Preserving Canonical Correlation Analysis Distributed Localization Algorithm for Wireless Sensor Networks

Localization is essential for wireless sensor networks. The state-of-the-art methods mainly adopt low accurate signal strength to perform localization, which are suffering from low localization accuracy and high variance. The machine learning methods are introduced to confront the low data quality challenges and provide considerable localization accuracy and other advantages. However, these series of methods also bear some drawbacks such as high training cost and high energy consumption. To this end, learning from our previous algorithm LE-LPCCA (Location Estimation-Locality Preserving-Canonical Correlation Analysis), we proposed an improved version, called, LE-DLPCCA (LE-Distributed-LPCCA), which greatly reduces the training cost and energy consumption. Specifically, LE-DLPCCA employs a clustering algorithm based on energy equilibrium. The training process, which maps the signal space into physical space, is conducted in a distributed manner for each cluster. Then, in the positioning phase, the unknown node estimates the distances from the most similar anchor nodes through the mapping and perform the localization of the unknown nodes through the maximum likelihood method. Demonstrated by multiple simulations, LE-DLPCCA algorithm is in high accuracy, fast localization model efficiency and low average energy consumption.