Self-Organizing Map for data collection planning in persistent monitoring with spatial correlations
Jan Faigl, Petr Váňa · 2016
This paper introduces an extension of the unsupervised learning method to solve data collection planning problems where particular sensor measurements can be spatially correlated. The problem is motivated by monitoring tasks formulated as the Prize-Collecting Traveling Salesman Problem with Neighborhoods (PC-TSPN). A solution of the PC-TSPN consists of a selection of important sensors, determination of the locations to read data from these sensors, and finding the shortest path to visit the locations. The solution cost is defined as a sum of the travel cost and penalty characterizing additional cost associated to sensors from which data are not retrieved. The penalty represents importance of particular sensor measurements to the quality of the model and existing solutions assume the penalties are constant values. However, for spatially close sensor locations, data from one sensor may contain also information about nearby locations and thus, its penalty depends on locations selected for data collection. The proposed generalization of the PC-TSPN solver allows to consider spatial correlations of sensor measurements and the proposed approach provides better solutions than the previous algorithm with fixed penalties.