Ant based Pareto optimal solution for QoS aware energy efficient multicast in wireless networks

The graphical abstract in the form of Pareto front solutions showing the trade-offs between the objectives is depicted in Fig. 1.Display Omitted Wireless networks have finite battery source, so QoS aware multicast routing must also be energy efficient.There is trade-off between energy consumption and QoS guarantees (i.e. end-to-end delay, delay jitter and packet loss) provided by a wireless network.These requirements are formulated as a multiobjective optimization problem.Ant based multiobjective algorithms are proposed to build an energy efficient multicast tree.Simulation results are compared with NSGA-III. Most of the group communication technologies support real-time multimedia applications such as video conferencing and distributed gaming. These applications require quality-of-service (QoS) aware multicast routing protocol to deliver the same data stream to a predefined group of receivers. Since nodes in wireless networks are severely energy constrained due to finite battery source, hence it is of paramount importance that QoS aware multicast routing protocol be energy efficient. Transmission power control is one of the methods used to save energy. In this method, the nodes dynamically adjust the transmission power so that energy consumption in the tree is minimized. However, reduction in the transmission power increases the number of forwarding nodes in the multicast tree. This negatively impacts the QoS in terms of propagation delay, delay jitter, and packet loss etc. In wireless networks, there is a trade-off between the energy consumption and the QoS guarantees provided by the network. We unify these requirements into a multiobjective framework referred to as Energy Efficient QoS Multicast Routing (E2QoSMR). The goal is to simultaneously optimize the total power consumption and the QoS parameters in the multicast tree. We extend two algorithms based on metaphor of swarm intelligence for finding an energy efficient multicast tree satisfying the QoS guarantees. Extensive simulations have been conducted to validate the correctness and efficiency of the algorithms. The simulation result of the algorithms is compared with the nondominated sorting genetic algorithm, NSGA-III. The experimental results are consolidated by statistical analyses that demonstrate the ability of the algorithms to generate the Pareto optimal solution set.