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Congestion Control based on Genetic Algorithm in Wireless Sensor Network  

Park, Chong-Myung (강원대학교 컴퓨터정보통신공학)
Lee, Joa-Hyoung (강원대학교 컴퓨터정보통신공학)
Jung, In-Bum (강원대학교 컴퓨터정보통신공학)
Publication Information
Journal of KIISE:Information Networking / v.36, no.5, 2009 , pp. 413-424 More about this Journal
Abstract
Wireless sensor network is based on an event driven system. Sensor nodes collect the events in surrounding environment and the sensing data are relayed into a sink node. In particular, when events are detected, the data sensing periods are likely to be shorter to get the more correct information. However, this operation causes the traffic congestion on the sensor nodes located in a routing path. Since the traffic congestion generates the data queue overflows in sensor nodes, the important information about events could be missed. In addition, since the battery energy of sensor nodes exhausts quickly for treating the traffic congestion, the entire lifetime of wireless sensor networks would be abbreviated. In this paper, a new congestion control method is proposed on the basis of genetic algorithm. To apply genetic algorithm, the data traffic rate of each sensor node is utilized as a chromosome structure. The fitness function of genetic algorithm is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets, the proposed method selects the optimal data forwarding sensor nodes for relieving the traffic congestion. In experiments, when compared with other methods to handle the traffic congestion, the proposed method shows the efficient data transmissions due to much less queue overflows and supports the fair data transmission between all sensor nodes as possible. This result not only enhances the reliability of data transmission but also distributes the energy consumptions across the network. It contributes directly to the extension of total lifetime of wireless sensor networks.
Keywords
sensor network; traffic congestion; genetic algorithm; fitness function;
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