EOMCSR: An Energy Optimized Multi-Constrained Sustainable Routing Model for SDWSN

Abstract

Improving the network lifetime is a major concern in Wireless Sensor Networks (WSNs) due to the limited network resources. As the sensor nodes are usually deployed in a random fashion across the network area, network-wide energy optimization becomes a challenge. An energy-optimized WSN offers improved fault tolerance, and this can be further enhanced with the help of Software Defined Networking (SDN). Hence, a Software Defined WSN (SDWSN) based energy efficient approach is proposed in this paper to improve the performance of the network. The proposed approach discusses an Energy Optimized Multi-Constrained Sustainable Routing (EOMCSR) model. This model formulates a Mixed Integer Linear Programming (MILP) problem to optimize the network resource based energy consumption in SDWSN. The simulation results are compared with the existing SDWSN and traditional WSN approaches with respect to the performance metrics for different numbers of rounds. The experimental results verify that EOMCSR achieves an efficiency of around <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8%</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">48%</i> for average energy per node in comparison to the SDWSN approach (MES) and traditional approach (E-TORA) respectively, after <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">100</i> rounds for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">200</i> nodes. Similarly, an efficiency of around <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">36%</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">60%</i> is achieved for the number of dead nodes. In addition to this, the proposed approach is also tested under different network scenarios w.r.t. multiple network performance metrics, and substantial improvements have been obtained w.r.t. each performance metric.