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- Onboard Deep Deterministic Policy Gradients for Online Flight Resource Allocation of UAVsPublication . Li, Kai; Emami, Yousef; Ni, Wei; Tovar, Eduardo; Han, ZhuIn Unmanned Aerial Vehicle (UAV) enabled data collection, scheduling data transmissions of the ground nodes while controlling flight of the UAV, e.g., heading and velocity, is critical to reduce the data packet loss resulting from buffer overflows and channel fading. In this letter, a new online flight resource allocation scheme based on deep deterministic policy gradients (DDPG-FRAS) is studied to jointly optimize the flight control of the UAV and data collection scheduling along the trajectory in real time, thereby asymptotically minimizing the packet loss of the ground sensor networks. Numerical results confirm that the proposed DDPG-FRAS can gradually converge, while enlarging the buffer size can reduce the packet loss by 47.9%.
- Deep Q-Networks for Aerial Data Collection in Multi-UAV-Assisted Wireless Sensor NetworksPublication . Emami, Yousef; Wei, Bo; Li, Kai; Ni, Wei; Tovar, EduardoUnmanned Aerial Vehicles (UAVs) can collaborate to collect and relay data for ground sensors in remote and hostile areas. In multi-UAV-assisted wireless sensor networks (MA-WSN), the UAVs' movements impact on channel condition and can fail data transmission, this situation along with newly arrived data give rise to buffer overflows at the ground sensors. Thus, scheduling data transmission is of utmost importance in MA-WSN to reduce data packet losses resulting from buffer overflows and channel fading. In this paper, we investigate the optimal ground sensor selection at the UAVs to minimize data packet losses . The optimization problem is formulated as a multiagent Markov decision process, where network states consist of battery levels and data buffer lengths of the ground sensor, channel conditions, and waypoints of the UAV along the trajectory. In practice, an MA-WSN contains a large number of network states, while the up-to-date knowledge of the network states and other UAVs' sensor selection decisions is not available at each agent. We propose a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MUAIS) to minimize the data packet loss, where the UAVs learn the underlying patterns of the data and energy arrivals at all the ground sensors. Numerical results show that the proposed MUAIS achieves at least 46\% and 35\% lower packet loss than an optimal solution with single-UAV and an existing non-learning greedy algorithm, respectively.
- Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement LearningPublication . Li, Kai; Ni, Wei; Emami, Yousef; Dressler, FalkoEnergy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight control decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes.
- Buffer-Aware Scheduling for UAV Relay Networks with Energy FairnessPublication . Emami, Yousef; Li, Kai; Tovar, EduardoFor assisting data communications in human-unfriendly environments, Unmanned Aerial Vehicles (UAVs) are employed to relay data for ground sensors thanks to UAVs' flexible deployment, high mobility, and line-of-sight communications. In UAV relay networks, energy efficient data relay is critical due to limited battery of the ground sensing devices. In this paper, we propose a butter-aware transmission scheduling optimization to minimize the energy consumption of the ground devices under constraints of butter overflows and energy cost fairness on the ground devices. Moreover, we show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. The performance of the proposed algorithm is evaluated in terms of network sizes, packet arrival rates, and fairness of the energy consumption. Numerical results confirm that the proposed scheduling algorithm reduces the energy consumption of the ground devices in a fair fashion, while the butter overflow constraint holds.