Optimizing qos-based multicast routing in wireless networks: A multi-objective genetic algorithmic approach

Abstract

With increasing demand for real-time services in next generation wireless networks, quality-of-service (QoS)-based routing offers significant challenges. Multimedia applications like video conferencing, real-time streaming of stock quotes or processing of scientific images relayed from satellites require strict QoS guarantee (e.g. bandwidth, delay) while communicating among multiple hosts. This gives rise to the need for an efficient multicast routing protocol which will be able to determine multicast routes satisfying the different QoS constraints. Design of such protocol boils down to a multi-objective optimization problem, which is computationally intractable. In fact, discovering optimal multicast routes is an NP-hard problem when the network state information is inaccurate – a common scenario in mobile wireless networks. In this paper, we propose a novel multicast tree selection algorithm that determines near-optimal multicast routes on demand. Based on the multiobjective genetic algorithmic (MOGA) approach, our solution attempts to optimize multiple QoS parameters (e.g. end-to-end delay, bandwidth guarantee and residual bandwidth utilization) simultaneously. We mathematically analyze the performance and convergence of the developed algorithm. Simulation results demonstrate that our algorithm is capable of discovering on-demand a set of QoS-based, near-optimal multicast routes within a few iterations, even with imprecise network information. From these set of routes one can choose the best possible multicast route depending on the specified QoS requirements.