UniLoc: A Unified Mobile Localization Framework Exploiting Scheme Diversity

Abstract

Current localization schemes on mobile devices are experiencing great diversity that is mainly shown in two aspects: the large number of available localization schemes and their diverse performance. This paper presents <italic>UniLoc</italic>, a unified framework that gains improved performance from multiple localization schemes by exploiting their diversity. UniLoc predicts the localization error of each scheme online based on an error model and real-time context. It further combines the results of all available schemes based on the error prediction results and an ensemble learning algorithm. The combined result is more accurate than any individual schemes. With the flexible design of error modeling and ensemble learning, UniLoc can easily integrate a new localization scheme. The energy consumption of UniLoc is low, since its computation, including both error prediction and ensemble learning, only involves simple linear calculation. Our experience with extensive experiments tells that such easy aggregation incurs little overhead in integrating and training a localization scheme, but gains substantially from the scheme diversity. UniLoc outperforms individual localization schemes by 1.6× in a variety of environments, including <inline-formula><tex-math notation= LaTeX >$>89\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>></mml:mo><mml:mn>89</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href= du-ieq1-2979857.gif /></alternatives></inline-formula> new places where we did not train the error models.