Minimize end-to-end delay through cross-layer optimization in multi-hop wireless sensor networks

End-to-end delay plays a very important role in wireless sensor networks. It refers to the total time taken for a single packet to be transmitted across a network from source to destination. There are many factors could affect the end-to-end delay, among them the routing path and the interference level along the path are the two basic elements that could have significant influence on the result of the end-to-end delay. This thesis presents a transmission scheduling scheme that minimizes the end-to-end delay when the node topology is given. The transmission scheduling scheme is designed based on integer linear programming and the interference modeling is involved. By using this scheme, we can guarantee that no conflicting transmission will appear at any time during the transmission. A method of assigning the time slot based on the given routing is presented. The simulation results show that the link scheduling scheme can significantly reduce the end-to-end delay. Further, this article also shows two methods which could directly addresses routing and slot assignment, one is MI+MinDelay algorithm and the other is called One-Phase algorithm. A comparison was made between the two and the simulation result shows the latter one leads to smaller latency while it takes much more time to be solved. Besides, due to the different routing policy, we also demonstrate that the shortest path routing does not necessarily result in minimum end-to-end delay --Abstract, page ii

The Design and Implementation of “Four-in-One” Blended Learning Model in Digital Media Technology Classroom

 In a digitally driven world, many educators are using face-to-face and online methods to improve teaching and learning because of blendedlearning’s availability and convenience. This study serves two purposes. The main purpose of this research was to identify the academic achievement difference between students who studied through “Four-in-One” blended learning model (official website, WeChat official account platform, official Weibo, and cloud database) and students who studied through traditional learning. And second, to investigate the opinions and feedbacks about “Fourin-One” blended learning model. The mixed methods both qualitative and quantitative methods including quasi-experimental research, direct observation and semi-structured interview were applied. The participants were 88 undergraduate students (67 males and 21 females) from a Chinese Computer Application Technology Department. The subjects were selected by purposive sampling and separated into a control group and an experimental group. The data were analyzed by using mean, standard deviation, dependent t-test, and independent t-test. The outcomes of research showed that the experimental group had higher academic achievement scores than the control group. The results of research also revealed that blended learning outcomes related to tailored teaching, technology readiness level, scaffolding learning, and self-regulated learning

Recent trends in vegetation greenness in China significantly altered annual evapotranspiration and water yield

There has been growing evidence that vegetation greenness has been increasing in many parts of the northern middle and high latitudes including China during the last three to four decades. However, the effects of increasing vegetation greenness particularly afforestation on the hydrological cycle have been controversial. We used a process-based ecosystem model and a satellite-derived leaf area index (LAI) dataset to examine how the changes in vegetation greenness affected annual evapotranspiration (ET) and water yield for China over the period from 2000 to 2014. Significant trends in vegetation greenness were observed in 26.1% of China\u27s land area. We used two model simulations driven with original and detrended LAI, respectively, to assess the effects of vegetation \u27greening\u27 and \u27browning\u27 on terrestrial ET and water yield. On a per-pixel basis, vegetation greening increased annual ET and decreased water yield, while vegetation browning reduced ET and increased water yield. At the large river basin and national scales, the greening trends also had positive effects on annual ET and had negative effects on water yield. Our results showed that the effects of the changes in vegetation greenness on the hydrological cycle varied with spatial scale. Afforestation efforts perhaps should focus on southern China with larger water supply given the water crisis in northern China and the negative effects of vegetation greening on water yield. Future studies on the effects of the greenness changes on the hydrological cycle are needed to account for the feedbacks to the climate

Active vehicle obstacle avoidance based on integrated horizontal and vertical control strategy

In this paper, an integrated control method is proposed which is based on a planning of vehicle’s path and speed with respect to obstacles and a model predictive control for tracking this path. The planning layer builds a model predictive control framework based on the vehicle kinematics model; based on the potential field theory, comprehensively considers the vehicle’s state information and the relative position and velocity information of the obstacles, establishes the potential field function, introduces the optimization objective function, and optimizes vehicle’s path and speed. The tracking layer builds a model predictive control framework based on the vehicle dynamics model, establishes an optimized objective function that takes the optimal front wheel rotation angle and optimal longitudinal acceleration as inputs, and constrains the lateral acceleration and yaw angular velocity to achieve the vehicle’s obstacle avoidance path track. A co-simulation platform of CarSim and Matlab/Simulink was built to analyse the performance of the vehicle under static and dynamic obstacles under different initial speed conditions. The results show that the vehicle can track the reference path and reference speed smoothly, realize the horizontal and vertical comprehensive control of active obstacle avoidance, and verify the effectiveness of the proposed control method

An Emergency Disposal Decision-making Method with Human--Machine Collaboration

Rapid developments in artificial intelligence technology have led to unmanned systems replacing human beings in many fields requiring high-precision predictions and decisions. In modern operational environments, all job plans are affected by emergency events such as equipment failures and resource shortages, making a quick resolution critical. The use of unmanned systems to assist decision-making can improve resolution efficiency, but their decision-making is not interpretable and may make the wrong decisions. Current unmanned systems require human supervision and control. Based on this, we propose a collaborative human--machine method for resolving unplanned events using two phases: task filtering and task scheduling. In the task filtering phase, we propose a human--machine collaborative decision-making algorithm for dynamic tasks. The GACRNN model is used to predict the state of the job nodes, locate the key nodes, and generate a machine-predicted resolution task list. A human decision-maker supervises the list in real time and modifies and confirms the machine-predicted list through the human--machine interface. In the task scheduling phase, we propose a scheduling algorithm that integrates human experience constraints. The steps to resolve an event are inserted into the normal job sequence to schedule the resolution. We propose several human--machine collaboration methods in each phase to generate steps to resolve an unplanned event while minimizing the impact on the original job plan.Comment: 15 pages, 16 figure