EACSRO: Epsilon constraint-based Adaptive Cuckoo Search algorithm for rate optimized congestion avoidance and control in wireless sensor networks

Normally, the WSN network is employed to provide the data transmission devoid of degrading the quality. Nowadays, the congestion becomes major cumbersome to degrade the network performance. Due to congestion, some of the significant packets are lost to mitigate the quality. To surmount this congestion issue, the Epsilon constraint-based Adaptive Cuckoo Search Rate Optimization algorithm is proposed. Initially, the occurrence of congestion is detected by incoming packets (traffic) of sensor nodes. Then, the congestion level is determined by virtual queue length. If congestion occurs, then it is fed into the proposed optimization algorithm. In order to find out the optimal value, the fitness function is newly formulated with the aid of Epsilon parameter. Then, the proposed algorithm exploits the new fitness in which the new solution is generated by adjusting the step size adaptively. Finally, the proposed algorithm obtains the best solution where the data is transmitted without congestion. The experimental results and performance is analysed using MATLAB implementation by throughput and sending rate. The performance outcome of proposed algorithm attains 0.99 high throughput value and 0.1 sending rates to enhance the transmission efficiency.