SDQIM: Software-Defined Quadcopter Inter-domain Management Protocol with Decentralized Inductive Power Transfer (DIPT) platform

Due to the flexible deployment, low-cost and management, unmanned aerial vehicles, quadcopters have attracted significant interest lately in expanding wireless communications to areas that lack to cellular coverage network. Currently, software-defined network (SDN) is considered a viable alternative that improves network scalability and management. Earlier in 2018, we designed a novel software-defined quadcopter (SDQ) with a mobile Internet of Things vehicle to measure the best efficient wind power location for effective future wind turbine placement. However, large wind farms require complex and scalable wind measurement platform that consists of multiple quadcopters and ground mobile nodes that operate under a decision-making softwarized protocol. To this end, this paper proposes a novel cognitive routing protocol called SDQ interdomain management (SDQIM) Protocol. The SDQIM structure consists of multiple SDN controllers with failover capability. Our proposed protocol operate with multiple phases as each phase is used to cover a specific scenario that could occur in the interdomain network. The SDQ-main will have a total overview of the network topology and the SDQ-slave will be responsible for communication management with all other local subdomains. However, for large-scale farmland, quadcopter power level is essential for the continuity of the system services. Thus, we additionally propose a decentralized inductive power transfer (DIPT) platform that operates autonomously using software defined controlling approach. In essence, The DIPT platform is considered to be automated and low-cost approach that encompasses a custom-designed SDN controller for all interdomain quadcopters. Our system has been implemented by using an experimental testbed. The experimental results showed that scalability of the SDQ system can be increased to cover large wind farms with uninterrupted power management platform.