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

Rapid deployment of wireless devices with 5G and beyond enabled a connected world. However, an immediate demand increase right after a disaster paralyzes network infrastructure temporarily. The continuous flow of information is crucial during disaster times to coordinate rescue operations and identify the survivors. Communication infrastructures built for users of disaster areas should satisfy rapid deployment, increased coverage, and availability. 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. 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. Furthermore, a testbed is deployed on Istanbul Technical University (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

Phoenix: Aerial Monitoring for Fighting Wildfires

Wildfires have become a global problem in recent years. Authorities are looking for various technological and auxiliary solutions to deal with this environmental crisis. One of the advances being utilized in the forest fire field and its aftermath is unmanned aerial vehicles (UAVs). UAVs play a fundamental role in wildfire-fighting solutions due to their ease of use and high accessibility. However, the energy constraints of a single UAV and the fire areas make monitoring challenging. Therefore, to address these issues, we propose a monitoring application called Phoenix. We make three main contributions with the Phoenix application. Firstly, we implement a monitoring application consisting of path planning, graph engine, and modified TSP algorithms to help the UAV’s fire tracking and shorten its route. Secondly, we develop a network architecture to transfer the tracking data we obtained to provide information to the fire brigade and other firefighting units. Thirdly, we provide energy optimization for a single UAV mission. The first part of the application uses the elliptical fire model and simulation. In addition, Phoenix utilizes fuel moisture content (fmc) data of the fire zone to analyze the critical fire regions. The simulation results show that our Phoenix application reduces energy consumption by 38 % and enhances coverage by up to 51%