Xiaonan Liu

The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

Ad hoc networks are a new wireless networking paradigm for mobile hosts. In this paper, we designed an intelligent transport system. The ITS (intelligent transport system) includes two big function modules: Information processing application system and Road condition information transferring system. The main task of the road condition information transferring module is in charge of the information exchange of the car inside, car to car and car to road. The module works in ad hoc network, we call the network VANET (vehicular ad-hoc network) . Vehicular networks are likely to become the most relevant form of mobile ad hoc networks. For the sake of insuring the system can run normally, the information can be transferring correctly and fleetly, the security of VANET (vehicular ad-hoc network) of the road condition information transferring system is crucial. So integrate the characteristics of ad hoc network itself, in the ITS of this paper, we concern the security issues of VANETs from some aspects and provide the appropriate solving measures. To make sure the ITS can be used under the security pattern.

When natural disasters strike, the coverage for Internet of Things (IoT) may be severely destroyed, due to the damaged communications infrastructure. Unmanned aerial vehicles (UAVs) can be exploited as flying base stations to provide emergency coverage for IoT, due to its mobility and flexibility. In this paper, we propose multiantenna transceiver design and multihop device-to-device (D2D) communication to guarantee the reliable transmission and extend the UAV coverage for IoT in disasters. First, multihop D2D links are established to extend the coverage of UAV emergency networks due to the constrained transmit power of the UAV. In particular, a shortest-path-routing algorithm is proposed to establish the D2D links rapidly with minimum nodes. The closed-form solutions for the number of hops and the outage probability are derived for the uplink and downlink. Second, the transceiver designs for the UAV uplink and downlink are studied to optimize the performance of UAV transmission. Due to the nonconvexity of the problem, they are first transformed into convex ones and then, low-complexity algorithms are proposed to solve them efficiently. Simulation results show the performance improvement in the throughput and outage probability by the proposed schemes for UAV wireless coverage of IoT in disasters.

Unmanned aerial vehicles (UAVs) can be used as flying base stations to provide ubiquitous connections for mobile devices in over-crowded areas. On the other hand, non-orthogonal multiple access (NOMA) is a promising technique to support massive connectivity. In this letter, the placement and power allocation (PA) are jointly optimized to improve the performance of the NOMA-UAV network. Since the formulated joint optimization problem is non-convex, the location of the UAV is first optimized, with the total path loss from the UAV to users minimized. Then, the PA for NOMA is optimized using the optimal location of the UAV to maximize the sum rate of the network. Simulation results are presented to show the effectiveness and efficiency of the proposed scheme for NOMA-UAV networks.

A challenge for rescue teams when fighting against wildfire in remote areas is the lack of information, such as the size and images of fire areas. As such, live streaming from Unmanned Aerial Vehicles (UAVs), capturing videos of dynamic fire areas, is crucial for firefighter commanders in any location to monitor the fire situation with quick response. The 5G network is a promising wireless technology to support such scenarios. In this paper, we consider a UAV-to-UAV (U2U) communication scenario, where a UAV at a high altitude acts as a mobile base station (UAV-BS) to stream videos from other flying UAV-users (UAV-UEs) through the uplink. Due to the mobility of the UAV-BS and UAV-UEs, it is important to determine the optimal movements and transmission powers for UAV-BSs and UAV-UEs in real-time, so as to maximize the data rate of video transmission with smoothness and low latency, while mitigating the interference according to the dynamics in fire areas and wireless channel conditions. In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming. We applied the Deep Q-Network (DQN) and Actor-Critic (AC) to maximize the QoE of video transmission from all UAV-UEs to a single UAV-BS to learn the dynamic fire areas and communication environment. Simulation results show the effectiveness of our proposed algorithm in terms of the QoE, delay and video smoothness compared to the Greedy algorithm.