Data Aggregation in Wireless Sensor Networks Based on Environmental Similarity: A Learning Automata Approach

Sensor networks are established of many inexpensive sensors with limited energy and computational resources and memory. Each node can sense special information, such as the temperature, humidity, pressure and so on and then send them to the central station. One of the major challenges in these networks, is limit energy consumption and one of the ways for reducing energy consumption in wireless sensor networks, is reducing the number of packets that are transmitted in the network. Data Aggregation technique that combines related data together and prevents sending additional packets on the network can be effective in reducing the number of packets sent over the network. In this paper a Data Aggregation method based on learning automata is presented and with identifying sensors that are in the similar area, and produce the same data and enable the sensor nodes periodically avoid sending additional packets on the network, and significantly saves energy and increases the lifetime of the network. Simulation results show the optimal performance of the proposed method.