Energy efficient resource allocation algorithms combining PSO with FLC and Taguchi method in hybrid opportunistic networks

In order to reduce power consumption and delay time when mobile devices use wireless networks, this paper proposes dividing groups of mobile devices into clusters, and proposes two cluster architectures, which are dual -radio opportunistic networking for energy efficiency combining particle swarm optimization with fuzzy logic control and Taguchi by 2-hop of priority weighted round robin (DRONEE-PFT2-PWRR) and dual-radio opportunistic networking for energy efficiency combining particle swarm optimization with fuzzy logic control and Taguchi by multi-hop of priority weighted rate control (DRONEE-PFTM-PWRC). Furthermore, our proposed DRONEE-PFT2-PWRR algorithm increases cluster coverage using 2-hop and our proposed DRONEE-PFTM-PWRC algorithm improves power consumption of the system by multi-hop cluster architecture. Internal cluster communication uses Wi-Fi transmission packets, which can reduce energy consumption when a mobile device communicates with a long term evolution advanced (LTE-A) base station, and reduce interference on signals between mobile devices. In the selection of cluster heads, this paper uses particle swarm optimization (PSO) to find the best cluster head position, and the PSO fitness parameter value is adjusted using fuzzy logic control (FLC) combined with the Taguchi method. Priority weighted round robin (PWRR) architecture is proposed for LTE-A base station and cluster network resource allocation, and quality of service is introduced to give priority weight to packets according to data type. Furthermore, priority weighted rate control (PWRC) is proposed. This method determines whether the network resources of the base station meet the demands of the cluster, and then assigns weights according to the requirements of the cluster and its priorities, and finally allocates network resources according to those weights. This increases the uplink throughput of network resource allocation. Simulation results show that our proposed methods significantly outperform the DRONEE-weighted (DRONEE-W) algorithm method in terms of power consumption, network throughput, and transmission delay time.