Internet of remote things (IoRT) networks are regarded as an effective approach for providing services to smart devices, which are often remote and dispersed over in a wide area. Due to the fact that the ground base station deployment is difficult and the power consumption of smart devices is limited in IoRT networks, the hierarchical Space-Air-Ground architecture is very essential for these scenarios. This paper aims to investigate energy efficient resource allocation problem in a two-hop uplink communication for Space-Air-Ground Internet of remote things (SAG-IoRT) networks assisted with unmanned aerial vehicle (UAV) relays. In particular, the optimization goal of this paper is to maximize the system energy efficiency by jointly optimizing sub-channel selection, uplink transmission power control and UAV relays deployment. The optimization problem is a mix-integer non-linear non-convex programming, which is hard to tackle. Therefore, an iterative algorithm that combines two sub-problems is proposed to solve it. First, given UAV relays deployment position, the optimal sub-channel selection and power control policy are obtained by the Lagrangian dual decomposition method. Next, based on the obtained sub-channel allocation and power control policy, UAV relays deployment is obtained by successive convex approximation (SCA). These two sub-problems are alternatively optimized to obtain the maximum system energy efficiency. Numerical results verify that the proposed algorithm has at least 21.9% gain in system energy efficiency compared to the other benchmark scheme.

The detection of drug metabolites, especially for minor metabolites, continues to be a challenge because of the complexity of biological samples. Imperatorin (IMP) is an active natural furocoumarin component originating from many traditional Chinese herbal medicines and is expected to be pursued as a new vasorelaxant agent. In the present study, a generic and efficient approach was developed for the in vivo screening and identification of IMP metabolites using liquid chromatography-Triple TOF mass spectrometry. In this approach, a novel on-line data acquisition method mutiple mass defect filter (MMDF) combined with dynamic background subtraction was developed to trace all probable urinary metabolites of IMP. Comparing with the traditionally intensity-dependent data acquisition method, MMDF method could give the information of low-level metabolites masked by background noise and endogenous components. Thus, the minor metabolites in complex biological matrices could be detected. Then, the sensitive and specific multiple data-mining techniques extracted ion chromatography, mass defect filter, product ion filter, and neutral loss filter were used for the discovery of IMP metabolites. Based on the proposed strategy, 44 phase I and 7 phase II metabolites were identified in rat urine after oral administration of IMP. The results indicated that oxidization was the main metabolic pathway and that different oxidized substituent positions had a significant influence on the fragmentation of the metabolites. Two types of characteristic ions at m/z 203 and 219 can be observed in the MS/MS spectra. This is the first study of IMP metabolism in vivo. The interpretation of the MS/MS spectra of these metabolites and the proposed metabolite pathway provide essential data for further pharmacological studies of other linear-type furocoumarins.

Abstract-Massive Machine Type Communication (mMTC) to serve billions of IoT devices is considered to open a potential new market for the next generation cellular network. Legacy cellular networks cannot meet the requirements of emerging mMTC applications, since they were designed for human-driven services. In order to provide supports for mMTC services, current research and standardization work focus on the improvement and adaptation of legacy networks. However, these solutions face challenges to enhance the service availability and improve the battery life of mMTC devices simultaneously. In this article, we propose to exploit a network controlled sidelink communication scheme to enable cellular network with better support for mMTC services. Moreover, a context-aware algorithm is applied to ensure the efficiency of the proposed scheme and multiple context information of devices are taken into account. Correspondingly, signaling schemes are also designed and illustrated in this work to facilitate the proposed technology. The signaling schemes enable the network to collect required context information with light signaling effort and thus network can derive a smart configuration for both the sidelink and cellular link. In order to demonstrate the improvements brought by our scheme, a system-level simulator is implemented and numerical results show that our scheme can simultaneously enhance both the service availability and battery life of sensors.Index Terms-5G, mMTC, IoT, D2D, cellular network, signaling schemes, system level simulation[2] is widely considered as an important service to be offered by the upcoming fifth generation (5G) cellular networks. mMTC refers to a typical Internet-of-Things (IoT) scenario, where a large amount of static sensors are deployed and report sporadically to an application server in the cloud (e.g., to enable environment monitoring and object condition tracking). While opening a new potential market, mMTC also poses different requirements on network. Since legacy cellular networks were designed for services with high data rate and low latency, they experience technical challenges to meet the requirements of mMTC services (e.g., low device cost, long device battery life and high service availability). In order to obtain a deep market penetration in exploiting 5G to support mMTC services, the Third Generation Partnership Project (3GPP) has conducted studies to adapt and evolve legacy networks. For instance, to reduce device complexity, a new type of user equipment (UE) is introduced as the category 0 in [3], which has a reduced peak data rate, a single antenna design and reduced bandwidth. Further cost reduction can be gained by reducing the maximal transmission power [2] to simplify the integration of power amplifier (PA). However, it reduces the network coverage in uplink as a trade-off. Besides, since some mMTC devices are deployed in deep indoor scenarios, an extra penetration loss up to 20 dB can be foreseen [4]. In order to maintain the uplink coverage, both narrow band transmis...

Network slicing has been considered as a promising candidate to provide customized services for vehicular applications that have extremely high requirements of latency and reliability. However, the high mobility of vehicles poses significant challenges to resource management in such a stochastic vehicular environment with time-varying service demands. In this paper, we develop an online network slicing scheduling strategy for joint resource block (RB) allocation and power control in vehicular networks. The long-term time-averaged total system capacity is maximized while guaranteeing strict ultra-reliable and lowlatency requirements of vehicle communication links, subject to stability constraints of task queues. The formulated problem is a mixed integer nonlinear stochastic optimization problem, which is decoupled into three subproblems by leveraging Lyapunov optimization. In order to tackle this problem, we propose an online algorithm, namely JRPSV, to obtain the optimal RB allocation and power control at each time slot according to the current network state. Furthermore, rigorous theoretical analysis is conducted for the proposed JRPSV algorithm, indicating that the system capacity and the system average latency obey a [O (1/V ) , O (V )] trade-off with the control parameter V . Extensive simulation results are provided to validate the theoretical analysis and demonstrate the effectiveness of JRPSV as well as the impacts of various parameters.

Abstract-Compared with today's 4G wireless communication network, the next generation of wireless system should be able to provide a wider range of services with different QoS requirements. One emerging new service is to exploit cooperative driving to actively avoid accidents and improve traffic efficiency. A key challenge for cooperative driving is on vehicle-to-vehicle (V2V) communication which requires a high reliability and a low end-to-end (E2E) latency. In order to meet these requirements, 5G should be evaluated by new key performance indicators (KPIs) rather than the conventional metric, as throughput in the legacy cellular networks. In this work, we exploit network controlled direct V2V communication for information exchange among vehicles. This communication process refers to packet transmission directly among vehicles without the involvement of network infrastructure in U-plane. In order to have a network architecture to enable direct V2V communication, the architecture of the 4G network is enhanced by deploying a new central entity with specific functionality for V2V communication. Moreover, a resource allocation scheme is also specifically designed to adapt to traffic model and service requirements of V2V communication. Last but not least, different technologies are considered and simulated in this work to improve the performance of direct V2V communication.

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With increasing consumption, plastic mulch benefits agriculture by promoting crop quality and yield, but the environmental and soil pollution is becoming increasingly serious. Therefore, research on the monitoring of plastic mulched farmland (PMF) has received increasing attention. Plastic mulched farmland in unmanned aerial vehicle (UAV) remote images due to the high resolution, shows a prominent spatial pattern, which brings difficulties to the task of monitoring PMF. In this paper, through a comparison between two deep semantic segmentation methods, SegNet and fully convolutional networks (FCN), and a traditional classification method, Support Vector Machine (SVM), we propose an end-to-end deep-learning method aimed at accurately recognizing PMF for UAV remote sensing images from Hetao Irrigation District, Inner Mongolia, China. After experiments with single-band, three-band and six-band image data, we found that deep semantic segmentation models built via single-band data which only use the texture pattern of PMF can identify it well; for example, SegNet reaching the highest accuracy of 88.68% in a 900 nm band. Furthermore, with three visual bands and six-band data (3 visible bands and 3 near-infrared bands), deep semantic segmentation models combining the texture and spectral features further improve the accuracy of PMF identification, whereas six-band data obtains an optimal performance for FCN and SegNet. In addition, deep semantic segmentation methods, FCN and SegNet, due to their strong feature extraction capability and direct pixel classification, clearly outperform the traditional SVM method in precision and speed. Among three classification methods, SegNet model built on three-band and six-band data obtains the optimal average accuracy of 89.62% and 90.6%, respectively. Therefore, the proposed deep semantic segmentation model, when tested against the traditional classification method, provides a promising path for mapping PMF in UAV remote sensing images.