Modeling a Software‐Defined Network for Analyzing Route and Energy With Sensor Nodes
TL;DR
This study presents a new method for selecting the most suitable route by combining the Markov Chain Model (MCM) with reinforcement learning techniques (MCM‐RLA) to ensure that the chain and reward functions align with the Quality of Service (QoS).
Abstract
The practical architecture known as software‐defined networking (SDN) enables the Internet of Things (IoT) to function in various applications. Also, SDN has been adopted for effective routing in wireless networks. The controller intends to work using an algorithm to offer secure routing. However, some existing algorithms must provide optimized and secured routing paths. This study presents a new method for selecting the most suitable route by combining the Markov Chain Model (MCM) with reinforcement learning techniques (MCM‐RLA). The aim is to ensure that the chain and reward functions align with the Quality of Service (QoS). The reward regarding the following successive routing path is analyzed where SDN‐enabled IoT enhances the routing based on the prior routing ideas. Moreover, the entire network is managed via the network remotely. The performance of the anticipated is compared with various prevailing approaches. Multiple metrics like packet delivery rate (PDR), network lifetime, routing overhead, energy efficiency, and delay are compared to attain suitable WSN performance via efficient routing.