A new approach for improving lifetime in wireless sensor networks based on distributed learning automata

In wireless sensor networks, due to the strict resource limitations, consumption energy has becomes the most challenging issue. Decreasing consumption energy has a direct impact on increasing the lifetime of wireless sensor networks; data aggregation was put forward as an essential paradigm for wireless routing in sensor networks. The idea is to combine the data coming from different sources, eliminating redundancy, minimizing the number of transmissions, and thus, saving energy. For this purpose, in this paper, we propose an energy aware algorithm for the construction of virtual backbone. The proposed algorithm considers both the consumption energy and remaining energy parameters to construct the virtual backbone which increases the network lifetime. In this paper, a learning automata based data aggregation method in sensor networks was proposed. In the proposed method, each node in the network is equipped with a learning automaton. These learning automata in the network collectively learn the path of aggregation with minimum consumption energy for each node for transmitting its packets toward the sink. To evaluate the performance of the proposed method, computer simulations were conducted and the results were compared with the results of previous methods. The results show that the proposed method outperforms previous methods in terms of energy consumption and network lifetime.