AETD: An application-aware, energy-efficient trajectory design for flying base stations

Recent developments in consumer Unmanned Aerial Vehicles (UAVs) technology have created unprecedented opportunities for their applications in various civil domains. These ubiquitous vehicles of different shapes and sizes with easy and user-friendly configurations are favorite choices for providing different services such as wireless communications, emergency medical deliveries, disaster handling and many more. However, the limited battery life of UAVs pose a challenge to their service continuity, thus mechanisms to extend the UAVs’ battery life are required. For service delivery, UAVs consume energy for mechanical functionalities as well as for communicating with other network nodes. To reduce the mechanical energy consumption, the shortest flying path can be considered while selecting a right radio frequency level for UAV’s communications can effectively reduce the remaining required energy. In this paper, we analyze energy requirements for providing different communication services using different radio frequency bands. We propose an application-aware, energy-efficient trajectory design method which dynamically adapts the UAV’s communication radio frequency to the requested services in the best flying trajectory while considering service level priorities as well. Our simulation results show that our approach can save up to 14% energy while providing even higher Quality of Service (QoS) in a given trajectory

Poster abstract: A QoS-Aware, energy-efficient trajectory optimization for UAV base stations using Q-Learning

Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes with potentially varying QoS requirements. However, the dependence on the on-board, limited-capacity battery of the UAV-BS limits their service continuity. While conserving energy is important, meeting the QoS requirements of the ground nodes is equally important. We present an energy-efficient trajectory optimization for the UAV-BS while satisfying QoS requirements. We model the trajectory optimization as an MDP problem and solve it using Q-Learning. Simulation results reveal that our proposed algorithm decreases the average energy consumption by nearly 55% compared to a randomly-served algorithm

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease