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.
