MS-EAR: A mobile sink based energy aware routing technique for SDN enabled WSNs

Abstract

The paradigm of sensor networks involves connecting wireless electronic devices through small sensor nodes to gather and sense surrounding information. As these networks have limited resources, it is crucial to optimize their usage to enhance network performance. To achieve this, software-defined network technology is integrated into wireless sensor networks to efficiently utilize network resources. Furthermore, optimized clustering and energy-aware routing techniques are employed to evenly distribute network traffic and enable energy-efficient data transmission in SDN-enabled WSNs. However, the issue of hotspots or energy holes consistently persists in cluster-based routing protocols. This research aims to develop an energy-aware routing protocol incorporating a mobile sink, aiming to achieve energy consumption equilibrium and extend the network's lifespan. To address these concerns and ensure the longer sustainability of SDN-enabled WSNs, a novel approach called mobile sink-based energy-aware routing is proposed for energy-efficient data delivery. It utilizes optimized sink mobility to resolve the hotspot issue based on a proposed fitness function and centroid point approach. The fitness function considers essential parameters such as energy, control node density, and distances from control nodes. The flow rules are also generated based on the rank-based tree topology for multi-hop data transmission. The proposed approach is executed with an ONOS controller to implement SDN policies, and the performance of the heterogeneous network is evaluated through simulation using the ns-3 simulator. Furthermore, the proposed MS-EAR demonstrates significant improvements in the network lifespan compared to existing techniques such as GM-WOA, GMPSO, and FJAPSO, with increases of \(18.0\mathbf{\%},47.5\mathbf{\%}\) and \(94.0\mathbf{\%}\), respectively. It also outperforms the current state-of-the-art by considering various performance metrics, including stability period, number of alive nodes, network residual energy, packets transmitted to the control server, and average delay.