Quality of service aware energy efficient multipath routing protocol for internet of things using hybrid optimization algorithm

The Internet of Things (IoT) is evolving from a media term to a reality. We entered a new age of invention and creativity as a result of the development of IoT. Despite the fact that IoT has a lot of promise in the modern world, there are still a lot of obstacles. The main focus of this study is to improve the IoT’s resilience by resolving energy optimization, routing, and other issues to increase network lifetime. In this research, we present an IoT hybrid optimization algorithm-based QoS aware energy efficient multipath routing (QEMR) protocol. The first step is to achieve optimal clustering using a modified teaching-learning-based optimization (MTLO) method. Next, we determine the cluster head (CH) using the nonlinear regression-based pigeon optimization (NR-PO) method. For routing and determining the optimum path, we use the Deep Kronecker Neural Network (DKNN). The NS-3 simulation tool is utilised to model our proposed routing QEMR system and assess its effectiveness. The proposed QEMR scheme’s simulation findings are contrasted with existing techniques: REER, Rumor, and EOMR techniques in terms of the impacts of node density, node speed, and network traffic. In comparison of energy usage and the effect of node density, the suggested QEMR system uses less energy than the REER, Rumor, and EOMR schemes by 86.296%, 81.94%, and 75% respectively. The proposed QEMR scheme is used for studying the application of problem by enhancing the network performance and improving QoS by implementing DKNN based routing methodology to send data from source to the destination by selecting the optimal path.