Adaptive source transmission rate algorithm for IoT network

Congestion is an unavoidable problem in the Internet of Things (IoT) network because it is equipped with non-standardized devices. The many-to-one and carry-to-send nature of nodes leads to congestion. The underlying node becomes a bottleneck and faces serious communication problems due to unbalanced internal network parameters. This research presents an adaptive source transmission rate optimization algorithm to resolve these issues by performing a runtime fine-tuning of the proportional integral derivative (PID) controller by applying Non-Dominated Sorting Genetic Algorithm III PID (N3PID) in a cascaded manner. The optimal adjustment of internal network parameters results from overcoming the drawbacks of insufficient diversity, slow convergence, and overshoot. The N3PID provides a more accurate response due to efficient and robust parameter tuning. Moreover, the optimally modified parameters are passed to a PID controller that uses the error (the variation between the instantaneous and predicted queues) as input to optimize the transmission rate for the origin node. The N3PID increases the convergence speed and accelerates the accuracy. The N3PID algorithm is assessed with PID, Particle Swam Optimization–neural PID (PNPID), Cuckoo Fuzzy PID (CFPID), and Neural Network PID (NNPID) through a simulation in Network Simulator software. The experimental results reveal that the packet delivery ratio is increased by 9.924% and the average delay is substantially reduced by 14.152% while packet loss is significantly reduced by 12.311% and minimized the energy consumption to 5.899% as compared with NNPID.