Efficient Neuro-Fuzzy based Relay Selection in IoT-enabled SDWSN

Abstract

The Internet of Things is made up of wireless sensor devices (nodes) that work together to create a dynamic network without central management or continuous assistance. High mobility sensor nodes cause periodic topological changes in the network and link failures, which frequently force nodes to rediscover new routes for efficient data transmission in IoT, this brings attention to the issue of energy management and improvement in network lifetime. The relay selection is one method to reduce the node energy in the IoT network. However, designing communication protocols for relay selection, especially for dynamic networks, is a big challenge for researchers. To overcome these challenges, Software Defined Networking (SDN) architecture is used to minimize the overhead of sensor nodes by managing the topology control and routing decisions through artificial intelligent algorithms. The fuzzy logic and neural networks are combined to solve more complex problems such as decision-making, and optimization. This paper presents an Energy-aware Relay Selection Technique using an adaptive Neuro fuzzy-based model (ERST) to optimize the overall energy usage and improve the span of the network, a relay node is selected depending on remaining energy, signal strength, and expected transmission ratio. The proposed ERST uses a fuzzy logic inference system to make intelligent decisions based on the fuzzy rules. The neural network can be trained to fine-tune the fuzzy system using the feedback concepts to select the optimal relay node. In addition, the simulation results prove that the suggested work outperforms the previous protocols in terms of an 8% improvement in packet delivery ratio, reduces 5% of end-to-end delay, 4% minimization of energy usage, and an 8% increase in average throughput and overall network lifetime.