access icon free Distributed estimation using online semi-supervised particle filter for mobile sensor networks

This study proposes an improved particle filter by incorporating semi-supervised machine learning for location estimation in mobile sensor networks (MSNs). A time-varying prior model is learned online as the likelihood of particle filter in order to adapt to dynamic characteristics of state and observation. Thanks to semi-supervised learning, the proposed particle filter can improve efficiency and accuracy, where the amount of available labelled training data is limited. The authors compare the proposed algorithm with the particle filter based on supervised learning. The algorithms are evaluated for received signal strength indicator (RSSI)-based distributed location estimation for MSN in which communication bandwidth and accuracy of the range measurement are limited. First, experimental results show that the semi-supervised algorithm can learn suddenly-changed RSSI characteristics while the supervised learning cannot. Second, the proposed particle filter is more accurate and robust against variations of the environment such as new obstacle configurations. Furthermore, the suggested particle filter shows low statistical variability during repeated experiments, confirmed by much smaller error deviation than the compared particle filter.

Inspec keywords: learning (artificial intelligence); wireless sensor networks; particle filtering (numerical methods); statistical analysis; telecommunication computing

Other keywords: available labelled training data; mobile sensor networks; time-varying prior model; semisupervised machine learning; online semisupervised particle filter; obstacle configurations; statistical variability; communication bandwidth; received signal strength indicator-based distributed location estimation; distributed estimation; MSN; suddenly-changed RSSI characteristics

Subjects: Other topics in statistics; Other topics in statistics; Wireless sensor networks; Communications computing; Knowledge engineering techniques

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