Unmanned aerial vehicles (UAVs) have been widely employed for emergency communications in Internet of Things (IoT) networks. The temporal freshness of data in disaster rescue, represented by the Age of Information (AoI), holds critical significance. Due to UAVs' limited energy capacity, it is crucial to optimize their energy consumption while ensuring effective assistance in the IoT system's fresh data collection tasks. Therefore, our work sets out to minimize the AoI and energy consumption to meet time-sensitive and priority-aware demands in resource-constrained environments. We formulate a multi-objective optimization problem by jointly optimizing UAV flight trajectory, IoT device transmitting power, and UAV radio frequency (RF) power, with constraints such as IoT devices' priority levels and UAVs' locations. This problem is modeled as a Markov Decision Process (MDP), and we propose a novel reinforcement learning (RL)-based method, termed the MultiAgent Data Collection Energy Transmitting Scheme (MADCET), to solve it. Extensive simulations demonstrate that the proposed scheme significantly outperforms benchmarks by reducing AoI, satisfying priority demands, minimizing energy consumption, and achieving good convergence.

When ground base stations are not available in the aftermath of a disaster, unmanned aerial vehicle (UAV) acting as flying relay is a promising option. The UAVs with limited energy as flying relays allow for wider data coverage and more stable data transmission. However, with the changes of ground devices topology and channel, it is challenging to consider quality of service (QoS) and the age of information (AoI) in UAV communication under the energy constraint. In this paper, we propose a dynamic priority packet scheduling for UAV assisted AoI-aware network whose utility is maximized subject to QoS to get the best tradeoff of the energy consumption and the weighted AoI. Specifically, the dynamics of devices are characterized by Gauss-Markov mobility model. Dynamic priority is affected by devices' movement, channel changes and others. We optimize the trajectory of the UAV and the scheduling scheme of the packets by the Dueling Double Deep Q Network (D3QN) algorithm. Simulations show that the scheme significantly improves the utility of the system compared to the benchmarks.

MOTIVATION: Fast and accurate genotype imputation is necessary for facilitating gene-mapping studies, especially with the ever increasing numbers of both common and rare variants generated by high-throughput-sequencing experiments. However, most of the existing imputation approaches suffer from either inaccurate results or heavy computational demand. RESULTS: In this article, aiming to perform fast and accurate genotype-imputation analysis, we propose a novel, fast and yet accurate method to impute diploid genotypes. Specifically, we extend a hidden Markov model that is widely used to describe haplotype structures. But we model hidden states onto single reference haplotypes rather than onto pairs of haplotypes. Consequently the computational complexity is linear to size of reference haplotypes. We further develop an algorithm 'merge-and-recover (MAR)' to speed up the calculation. Working on compact representation of segmental reference haplotypes, the MAR algorithm always calculates an exact form of transition probabilities regardless of partition of segments. Both simulation studies and real-data analyses demonstrated that our proposed method was comparable to most of the existing popular methods in terms of imputation accuracy, but was much more efficient in terms of computation. The MAR algorithm can further speed up the calculation by several folds without loss of accuracy. The proposed method will be useful in large-scale imputation studies with a large number of reference subjects. AVAILABILITY: The implemented multi-threading software FISH is freely available for academic use at https://sites.google.com/site/lzhanghomepage/FISH.

Bioinformatics (2014) 30 (13), 1876–1883 The author would like to note an affiliation correction. The first affiliation address was mistakenly stated as ‘School of Public Health, Xi'an Jiaotong University, Shaanxi, China’, while the correct one should be ‘Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Jiangsu, P. R. China’.