Lifetime expansion in WSNs using mobile data collector: A learning automata approach

Abstract Traditionally Wireless Sensor Networks (WSNs) were established at environment with a stationary Base Station (BS) and a large number of sensor nodes. BS performs data gathering of sensor nodes at regular time intervals or after each event occurrence. Sensor nodes closer to the BS have the more energy consumption than other sensor nodes due to their additional contribution in the gathering operation. This paper uses a mobile data collector which moves freely at environment and acts as an interface between a BS and sensor nodes. The proposed method divides a WSN into four logical partitions and leads the mobile data collector toward the center of each logical partition at a regular time interval. The mobile data collector exploits a learning automata to move either to the center of each logical partitions or to the center of the network. The learning automaton updates its actions probability vector based on logical partitions information to select the best logical partition at each interval. Simulation results with NS-2 simulator show that the proposed method improves the network lifetime as well as energy efficiency in the network. The proposed method increases the network coverage-efficiency and it balances number of sensor nodes in each logical partition.