Energy-efficient RL-based aerial network deployment testbed for disaster areas

Abstract

<p>Rapid deployment of wireless devices with 5G and beyond enabled a connected world. However, an immediate demand increase right after a disaster paralyzes network in-frastructure temporarily. The continuous flow of information is crucial during disaster times to coordinate rescue operations and identify the survivors.</p> <p>Communication infrastructures built for users of disaster areas should satisfy rapid deployment, increased coverage, and avail-ability. Unmanned air vehicles (UAV) provide a potential solution for rapid deployment as they are not affected by traffic jams and physical road damage during a disaster. In addition, ad-hoc WiFi communication allows the generation of broadcast domains within a clear channel which eases one-to-many communications. Moreover, using reinforcement learning (RL) helps reduce the computational cost and increases the accuracy of the NP-hard problem of aerial network deployment.</p> <p>To this end, a novel flying WiFi ad-hoc network management model is proposed in this paper. The model utilizes deep-Q-learning to maintain quality-of-service (QoS), increase user equipment (UE) coverage, and optimize power efficiency. Fur-thermore, a testbed is deployed on Istanbul Technical Univer-sity (ITU) campus to train the developed model. Training results of the model using testbed accumulates over 90% packet delivery ratio as QoS, over 97% coverage for the users in flow tables, and 0.28 KJ/Bit average power consumption.</p>