Energy Management and Time Scheduling for Heterogeneous IoT Wireless-Powered Backscatter Networks

© 2019 IEEE. In this paper, we propose a novel approach to jointly address energy management and network throughput maximization problems for heterogeneous IoT low-power wireless communication networks. In particular, we consider a low-power communication network in which the IoT devices can harvest energy from a dedicated RF energy source to support their transmissions or backscatter the signals of the RF energy source to transmit information to the gateway. Different IoT devices may have dissimilar hardware configurations, and thus they may have various communications types and energy requirements. In addition, the RF energy source may have a limited energy supply source which needs to be minimized. Thus, to maximize the network throughput, we need to jointly optimize energy usage and operation time for the IoT devices under different energy demands and communication constraints. However, this optimization problem is non-convex due to the strong relation between energy supplied by the RF energy source and the IoT communication time, and thus obtaining the optimal solution is intractable. To address this problem, we study the relation between energy supply and communication time, and then transform the non-convex optimization problem to an equivalent convex-optimization problem which can achieve the optimal solution. Through simulation results, we show that our solution can achieve greater network throughputs (up to five times) than those of other conventional methods, e.g., TDMA. In addition, the simulation results also reveal some important information in controlling energy supply and managing low-power IoT devices in heterogeneous wireless communication networks

Integration of SWAT and QUAL2K for water quality modeling in a data scarce basin of Cau River basin in Vietnam

© 2019 European Regional Centre for Ecohydrology of the Polish Academy of Sciences Water quality modeling in a river basin often faces the problem of having a large number of parameters yet limited available data. The important inputs to the water quality model are pollution concentrations and discharge from river tributaries, lateral inflows and related pollution load from different sources along the river. In general, such an extensive data set is rarely available, especially for data scarce basins. This makes water quality modeling more challenging. However, integration of models may be able to fill this data gap. Selection of models should be made based on the data that is available for the river basin. For the case of Cau River basin, the SWAT and QUAL2K models were selected. The outputs of SWAT model for lateral inflows and discharges of ungauged tributaries, and the observed pollutant concentrations data and estimated pollution loads of sub-watersheds were used as inputs to the water quality model QUAL2K. The resulting QUAL2K model was calibrated and validated using recent water quality data for two periods in 2014. Four model performance ratings PBIAS, NSE, RSR and R2 were used to evaluate the model results. PBIAS index was chosen for water quality model evaluation because it more adequately accounted for the large uncertainty inherent in water quality data. In term of PBIAS, the calibration and validation results for Cau River water quality model were in the “very good” performance range with ǀPBIASǀ < 15%. The obtained results could be used to support water quality management and control in the Cau River basin

ALMA Observations of Quasar Host Galaxies at z ≃ 4.8

We present ALMA band-7 data of the [C ii] 157:74 m emission line and underlying far-infrared (FIR) continuum for twelve luminous quasars at z ' 4:8 powered by fast-growing supermassive black holes (SMBHs). Our total sample consists of eighteen quasars, twelve of which are presented here for the rst time. The new sources consists of six Herschel/SPIRE detected systems, which we de ne as "FIR-bright" sources, and six Herschel/SPIRE undetected systems, which we de ne as "FIR-faint" sources. We determine dust masses for the quasars hosts of Mdust 0:225:0 108M , implying ISM gas masses comparable to the dynamical masses derived from the [C ii] kinematics. It is found that on average the Mgii line is blueshifted by 500kms1 with respect to the [C ii] emission line, which is also observed when complementing our observations with data from the literature. We nd that all of our "FIR-bright" subsample and most of the "FIR-faint" objects lie above the main sequence of star forming galaxies at z 5. We detect companion sub-millimeter galaxies (SMGs) for two sources, both FIR-faint, with a range of projected distances of 20 60 kpc and with typical velocity shifts of j vj < 200kms1 from the quasar hosts. Of our total sample of eighteen quasars, 5/18 are found to have dust obscured starforming companions.ERC; STF

Recovering the initial distribution for strongly damped wave equation

We study for the first time the inverse backward problem for the strongly damped wave equation. First, we show that the problem is severely ill-posed in the sense of Hadamard. Then, under the a priori assumption on the exact solution belonging to a Gevrey space, we propose the Fourier truncation method for stabilizing the ill-posed problem. A stability estimate of logarithmic type is established

Blockchain-based Secure Platform for Coalition Loyalty Program Management

In this paper, we propose a novel blockchain-based platform for the coalition loyalty program management. The platform allows the customers to freely exchange loyalty points from different existing blockchain-based loyalty programs by utilizing the sidechain technology. Moreover, by adopting the Proof-of-Stake consensus mechanism, we can further increase customer engagement by allowing the customers to participate in the consensus process to earn additional tokens. However, this might lead to situations where the customers centralize all tokens to a single chain/loyalty program if the chain offers more rewards for consensus participation. Through security and performance analyses, we show that such centralization of stakes poses a threat to the security and performance of the platform. Therefore, we develop a non-cooperative game model to analyze the rational behavior of the users. We reveal that the consensus participation rewards govern the user behavior and the decentralization of the system. Numerical experiments confirm our analytical results and show that the ratios between the consensus rewards have a significant impact on the system’s security and performance

Fast or Slow: An Autonomous Speed Control Approach for UAV-assisted IoT Data Collection Networks

Unmanned Aerial Vehicles (UAVs) have been emerging as an effective solution for IoT data collection networks thanks to their outstanding flexibility, mobility, and low operation costs. However, due to the limited energy and uncertainty from the data collection process, speed control is one of the most important factors while optimizing the energy usage efficiency and performance for UAV collectors. This work aims to develop a novel autonomous speed control approach to address this issue. To that end, we first formulate the dynamic speed control task of a UAV as a Markov decision process taking into account its energy status and location. In this way, the Q-learning algorithm can be adopted to obtain the optimal speed control policy for the UAV. To further improve the system performance, we develop a highly-effective deep dueling double Q-learning algorithm utilizing outstanding features of the deep neural networks as well as advanced dueling architecture to quickly stabilize the learning process and obtain the optimal policy. Through simulations, we show that our proposed solution can achieve up to 40% greater performance, i.e., an average throughput of the system, compared with other conventional methods. Importantly, the simulation results also reveal significant impacts of UAV’s energy and charging time on the system performance

Relationship between structural changes, hydrogen content and annealing in stacks of ultrathin Si/Ge amorphous layers

Amorphous Si, Ge and SiGe alloys are often doped with H in order to passivate the dangling bonds. However, H is not stable against light soaking and heat treatments yielding degradation of the electrical-optical properties. We present results on the structural instability, as a function of annealing, caused by H in multilayers (MLs) of alternating 3 nm thick a-Si and a-Ge layers deposited by sputtering. H was added at flow rates of 0.4, 0.8, 1.5, 3 and 6 ml/min. By ERDA it was seen that for flow rates &#8805;1.5 ml/min the effective H content incorporated in the samples saturates at &#8764;16 at. %. IR optical absorbance shows that mostly Si and Ge monohydrides form. Annealing was done at 673 K for times of 1 to 10 h. The evolution of the properties of the MLs as a function of annealing and H content was followed by IR optical absorbance, TEM, AFM, ERDA. With increasing annealing time/temperature and H content the surface morphology degrades with formation of bubbles and craters whose size and density increase up to 9 &#956;m and 6.7x105 cm-2 for a H flow rate of 6 ml/min. The signal of Ge-H and Si-H complexes almost completely vanish in the IR absorbance spectra upon annealing indicating that H is released to the lattice. This supports the conclusion that it is the released H that produces the bubbles and the craters when the H bubbles blow up because of a too high internal pressure. ERDA experiments performed on single layers of a-Si and a-Ge, showing a faster H released from a-Si than from a-Ge, and energy filtered TEM (EFTEM) maps, showing larger broadening of the a-Si layers in the ML structure, suggest that upon annealing H is first released from a-Si layers. This is in agreement with published data reporting on the lower binding energy of Si-H with respect to Ge-H in amorphous materials

Production of Biogas with Two-Stage Fermentation of Cow Dung-Palm Oil Mill Effluent

In this research, biogas is produced from Palm Oil Mill Effluent (POME) by fermentation of cow dung using a stirred reactor and purified by various CO2 and H2S removal techniques. The variables in this study were: composition of cow dung (55%, 60%, 65%, 70%, 75%, 80% w/w), amino acid composition (0.5%, 1%, 1.5% w/w) and length of fermentation time (2, 6, 10, 14, 16 days). The fixed variables were stirring speed (100 rpm), temperature (30oC) and reactor volume (100 L). This research also investigated the effect of using a lime packed reactor on the purity of methane gas. From the results of first stage of fermentation, it was found that the optimum composition of cow dung-POME was at 60% and the fermentation time was 14 days. In the second stage of fermentation using optimum results at first stage compared to fermentation of cow dung without POME, the results of measuring the gas pressure produced in 60% cow dung-POME fermentation were 17.5 Psig greater than fermentation of cow dung without POME of 15 Psig

Delays in the diagnosis and treatment of tuberculosis patients in Vietnam: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Treatment delay is an important indicator of access to tuberculosis diagnosis and treatment. Analyses of patient delay (i.e. time interval between onset of symptoms and first consultation of a health care provider) and health care delay (i.e. time interval between first consultation and start of treatment) can inform policies to improve access. This study assesses the patient, health care provider and total delay in diagnosis and treatment of new smear-positive pulmonary tuberculosis patients, and the risk factors for long delay, in Vietnam.</p> <p>Methods</p> <p>A cross-sectional survey of new patients treated by the National Tuberculosis Control Programme was conducted in 70 randomly selected districts in Vietnam. All consecutively registered patients in one quarter of 2002 were interviewed using a pre-coded structured questionnaire.</p> <p>Results</p> <p>Median (range) delay was 4 weeks (1–48) for total, 3 (1–48) weeks for patient and 1 (0–25) week for health care delay. Patients with long total delay (≥ 12 weeks, 15%) accounted for 49% of the cumulative number of delay-weeks. Independent risk factors (p < 0.05) for long total delay were female sex, middle age, remote setting, residence in the northern or central area, and initial visit to the private sector. For long patient delay (≥ 6 weeks) this was female sex, belonging to an ethnic minority, and living at > 5 km distance from a health facility or in the northern area. For long health care delay (≥ 6 weeks) this was urban setting, residence in the central area and initial visit to a communal health post, TB hospital or the private sector.</p> <p>Conclusion</p> <p>Analyses of patient and treatment delays can indicate target groups and areas for health education and strengthening of the referral system, in particular between the private sector and the NTP.</p