The Journal Paper entitled "LSTM-characterized Deep Reinforcement Learning for Continuous Flight Control and Resource Allocation in UAV-assisted Sensor Network", authored by Kai Li, Wei Ni, Falko Dressler is published in IEEE Internet of Things Journal.

Unmanned aerial vehicles (UAVs) can be employed to collect sensory data in remote wireless sensor networks (WSN). This is useful in many real-life situations like agriculture monitoring, forest fire prevention and control, smart traffic management, and other similar situations where there is the need to gather information over very large areas but keeping costs and resource usage to the minimum while guaranteeing an adequate quality of service.

Due to UAV's maneuvering, scheduling a sensor device to transmit data can overflow data buffers of the unscheduled ground devices. Moreover, lossy airborne channels can result in packet reception errors at the scheduled sensor.

This paper proposes a new deep reinforcement learning based flight resource allocation framework (DeFRA) to minimize the overall data packet loss in a continuous action space. DeFRA is based on Deep Deterministic Policy Gradient (DDPG), optimally controls instantaneous headings and speeds of the UAV, and selects the ground device for data collection. Furthermore, a state characterization layer, leveraging long short-term memory (LSTM), is developed to predict network dynamics, resulting from time-varying airborne channels and energy arrivals at the ground devices.

To validate the effectiveness of DeFRA, experimental data collected from a real-world UAV testbed and energy harvesting WSN are utilized to train the actions of the UAV. Numerical results demonstrate that the proposed DeFRA achieves a fast convergence while reducing the packet loss by over 15%, as compared to existing deep reinforcement learning solutions.

The CISTER Research Centre, located in Porto, Portugal, an international reference in the area of real-time and embedded computing systems, is opening 8-10 MSc-Student positions for candidates already enrolled or that will enroll in the 2021/2022 edition of the MSc Program on Critical Computing Systems Engineering at ISEP, Porto, Portugal.

Successful candidates will perform research activities within ongoing CISTER projects, in parallel with the specific MSc studies. Research grants require exclusivity and will be funded by ISEP, Vortex CoLab or one of the companies collaborating with the MSc program. It is therefore a great opportunity for the candidates to get introduced to what scientific research is, and to interact with professionals from big players (companies and research institutions) also participating in those projects.

Deadline is 9th July 2021, to apply and find out more information about these scholarships, please visit https://www.cister-labs.pt/mscccse/scolarship