The Journal of Science and Engineering Applications (JSEA)

Abstract

At present, wireless sensor networks (WSNs) are applied as a paradigm in environments where the use of 
conventional sensor networks is impossible. Many characteristics of WSNs make it suitable to apply, such 
as low power consumption, mobility of sensors, and the ability to deploy on a large scale but, when the 
size of these wireless networks increases, some challenges need appropriate solutions, and one of these 
challenges is the congestion. Congestion occurs when the network is more than the available capacity at 
any network point. The cause of congestion can also be attributed to the nature of wireless sensors and the 
nature of the data transmitted through these sensors. For this purpose, it is necessary to analyze the layers 
of these networks specifically (transport layer) properly and provide appropriate mechanisms for 
communicating information through the sensor network for each layer. The discovery of congestion is 
significant for the effective utilization of network resources to balance traffic load balancing.  In this paper, 
we propose the use of the genetic algorithm for routing knowledge of congestion control and interference 
in WSNs where we defined two objective functions which are reduced packet transmission delay and the 
expected transmission number to reach the destination. The results of the simulation show that the response 
time of the two-objective genetic algorithm is shorter than the basic genetic algorithm.