Extraction of Nonredundant Information from Sensor Networks

In this paper we present four new algorithms for solving Minimum Base Problem (MBP) which is known to be NP-complete. The problem arises in wide or dense sensor networks in which a huge number of sensors provides a highly redundant source of information and should be reduced for further processing. In many cases a proper selection of nonredundant subset of the set of all sensors is reasonable both from economical point of view and the computational complexity of processing vast input data by software or hardware. Both exact and approximate algorithms are to be developed and applied for this task. In addition a hybrid metaheuristic shown.