A Congestion Control Mechanism for WSN in Nonstationary Q-Model Environment using Learning Automata

Samaneh Alikhanzadeh and Mohammad Hossein Yaghmaee


Wireless sensor network, Congestion control, Learning automata, Loss rate, Buffer size


One of the main issues in transport protocols of wireless sensor network is congestion control, reliability assurance and energy saving which is a function of congestion control. Congestion causes packet loss and thus wasting the energy of the sensor nodes. In this paper, a novel approach is presented based on learning automata in wireless sensor networks to reduce congestion. In the proposed method the embedded automata in sensor nodes interact with the environment to find out an optimal answer in each interval based on knowledge obtained from the congestion of the previous stages, this means that it can greatly reduce the congestion amount. Simulations show that by using the proposed method, the amount of packet loss and consequently the energy consumption in the network reduces and the network lifetime increases.

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