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In this paper a multi-sensor data fusion approach for wireless sensor network based on bayesian methods and ant colony optimization techniques has been proposed. In this method, each node is equipped with multiple sensors (i.e., temperature and humidity). Use of more than one sensor provides additional information about the environmental conditions. The data fusion approach based on the competitive-type hierarchical processing is considered for experimentation. Initially the data are collected by the sensors placed in the sensing fields and then the data fusion probabilities are computed on the sensed data. In this proposed methodology, the collected temperature tand humidity data are processed by multi-sensor data fusion techniques, which help in decreasing the energy consumption as well as communication cost by fusing the redundant data. The multiple data fusion process improves the reliability and accuracy of the sensed information and simultaneously saves energy, which was our primary objective. The proposed algorithms were simulated using Matlab. The executions of proposed arnd low-energy adaptive clustering hierarchy algorithms were carried out and the results show that the proposed algorithms could efficiently reduce the use of energy and were able to save more energy, thus increasing the overall network lifetime.

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