A Novel Cost Optimization Strategy for SDN-Enabled UAV-Assisted Vehicular Computation Offloading

Vehicular computation offloading is a well-received strategy to execute delay-sensitive and/or compute-intensive tasks of legacy vehicles. The response time of vehicular computation offloading can be shortened by using mobile edge computing that offers strong computing power, driving these computation tasks closer to end users. However, the quality of communication is hard to guarantee due to the obstruction of dense buildings or lack of infrastructure in some zones. Unmanned Aerial Vehicles (UAVs), therefore, have become one of the means to establish communication links for the two ends owing to its characteristics of ignoring terrain and flexible deployment. To make a sensible decision of computation offloading, nevertheless vehicles need to gather offloading-related global information, in which Software-Defined Networking (SDN) has shown its advances in data collection and centralized management. In this paper, thus, we propose an SDN-enabled UAV-assisted vehicular computation offloading optimization framework to minimize the system cost of vehicle computing tasks. In our framework, the UAV and the Mobile Edge Computing (MEC) server can work on behalf of the vehicle users to execute the delay-sensitive and compute-intensive tasks. The UAV, in a meanwhile, can also be deployed as a relay node to assist in forwarding computation tasks to the MEC server. We formulate the offloading decision-making problem as a multi-players computation offloading sequential game, and design the UAV-assisted Vehicular Computation Cost Optimization (UVCO) algorithm to solve this problem. Simulation results demonstrate that our proposed algorithm can make the offloading decision to minimize the Average System Cost (ASC)

A new inductive debris sensor based on dual-excitation coils and dual-sensing coils for online debris monitoring

Lubricants are of key importance for mechanical processing, and exist in nearly every mechanical system. When the equipment is in operation, debris particles will be generated in mechanical lubricants. The detection of debris particles can indicate the wear degree of machinery components, and provide prognosis warning for the system before the fault occurs. In this work, a novel type of inductive debris sensor consisting of two excitation coils and two sensing coils is proposed for online debris monitoring. The developed sensor was proven to be of high sensitivity through experimental verification. The testing results show that, using the designed sensor, ferrous metal debris with a size of 115 μm and nonferrous metal debris with a size of 313 μm in a pipe with an inner diameter of 12.7 mm can be effectively detected. Moreover, the proposed inductive debris sensor structure has better sensitivity at higher throughput and its design provides a useful insight into the development of high-quality sensors with superior performances

A systematic tomography framework for thickness mapping of pipes using helical guided waves

Pipe wall loss caused by corrosion is of growing interest in the petrochemical industry. A systematic tomography framework using helical guided waves is developed in this paper to conduct a thickness mapping. In this work, the thickness under investigation is reconstructed using an objective function derived from the acoustic Helmholtz equation. The main approach consists of two parts. Firstly, the parametric dictionary is designed to separate the overlapped guided waves travelling in helical paths. After that, the scattering field can be extracted as the input of the distorted born iteration method. The imaging result is exemplified numerically and experimentally, with the strengths and drawbacks explained thoroughly. Remarkably, the thickness error of the simple defect is still within 0.5 mm when the input data is poor. A clear qualitative description of complex defects can be achieved through iterations even in the absence of an initial objective function. The framework established in this paper contributes a comprehensive imaging algorithm and the corresponding signal processing approach, all of which are conducive to providing some reference for engineering applications in nondestructive testing and structural health monitoring

On investigation of frequency characteristics of a novel inductive debris sensor

Lubricants have the ability to reduce frictions, prevent wear, convey metal debris particles and increase the efficiency of heat transfer, therefore they have been widely used in mechanical systems. To assess the safety and reliability of the machine under operational conditions, the development of inductive debris sensors for online monitoring of debris particles in lubricants has been paid more attention by researchers. To achieve a high-precision high-efficiency sensor for accurate prediction on the degree of wear, the equivalent circuit model of the sensor coil has been established, and its equations discovering the relationship between the induced voltage and excitation frequency have been derived. Furthermore, the influence of excitation frequencies and metal debris on the magnetic flux density has been analyzed throughout the simulations to determine the sensor magnetic field. In order to identify a frequency range suitable for detecting both ferrous and non-ferrous materials with a high level of sensitivity, the analytical analysis and experiments have been conducted to investigate the frequency characteristics of the developed inductive debris sensor prototype and its improved inspection capability. Moreover, the developed inductive debris sensor with the noticeable frequency characteristics has been assessed and its theoretical model has been also validated throughout experimental tests. Results have shown that the detection sensitivity of non-ferrous debris by the developed sensor increases with the excitation frequency in the range of 50 kHz to 250 kHz, while the more complex results for the detection of ferrous debris have been observed. The detection sensitivity decreases as the excitation frequency increases from 50 kHz to 300 kHz, and then increases with the excitation frequency from 300 kHz to 370 kHz. This leads to the effective selection of the excitation frequency in the process of inspection. Summarily, the investigation on frequency characteristics of the proposed novel inductive debris sensor has enabled its broad applications and also provided a theoretical basis and valuable insights into the development of inductive debris sensors with improved detection sensitivity

Exploring root system architecture and anatomical variability in alfalfa (Medicago sativa L.) seedlings

Abstract Background The growth of alfalfa (Medicago sativa L.) is significantly hampered by drought and nutrient deficiencies. The identification of root architectural and anatomical characteristics holds paramount importance for the development of alfalfa genotypes with enhanced adaptation to adverse environmental conditions. In this study, we employed a visual rhizobox system to investigate the variability in root system architecture (including root depth, root length, root tips number, etc.), anatomical features (such as cortical traits, total stele area, number and area of vessel, etc.), as well as nitrogen and phosphorus uptake across 53 alfalfa genotypes during the seedling stage. Results Out of the 42 traits measured, 21 root traits, along with nitrogen (N) and phosphorus (P) uptake, displayed higher coefficients of variation (CVs ≥ 0.25) among the tested genotypes. Local root morphological and anatomical traits exhibited more significant variation than global root traits. Twenty-three traits with CVs ≥ 0.25 constituted to six principal components (eigenvalues > 1), collectively accounting for 88.0% of the overall genotypic variation. Traits such as total root length, number of root tips, maximal root depth, and others exhibited positive correlations with shoot dry mass and root dry mass. Additionally, total stele area and xylem vessel area showed positive correlations with N and P uptake. Conclusions These root traits, which have demonstrated associations with biomass and nutrient uptake, may be considered for the breeding of alfalfa genotypes that possess efficient resource absorption and increased adaptability to abiotic stress, following validation during the entire growth period in the field

Genetic diversity and signatures of selection in BoHuai goat revealed by whole-genome sequencing

Background: Cross breeding is an important way to improve livestock performance. As an important livestock and poultry resource in Henan Province of China, Bohuai goat was formed by crossing Boer goat and Huai goat. After more than 20 years of breeding, BoHuai goats showed many advantages, such as fast growth, good reproductive performance, and high meat yield. In order to better develop and protect Bohuai goats, we sequenced the whole genomes of 30 BoHuai goats and 5 Huai goats to analyze the genetic diversity, population structure and genomic regions under selection of BoHuai goat. Furthermore, we used 126 published genomes of world-wide goat to characterize the genomic variation of BoHuai goat. Results: The results showed that the nucleotide diversity of BoHuai goats was lower and the degree of linkage imbalance was higher than that of other breeds. The analysis of population structure showed that BoHuai goats have obvious differences from other goat breeds. In addition, the BoHuai goat is more closely related to the Boer goat than the Huai goat and is highly similar to the Boer goat. Group by selection signal in the BoHuai goat study, we found that one region on chromosome 7 shows a very strong selection signal, which suggests that it could well be the segment region under the intense artificial selection results. Through selective sweeps, we detected some genes related to important traits such as lipid metabolism (LDLR, STAR, ANGPTL8), fertility (STAR), and disease resistance (CD274, DHPS, PDCD1LG2). Conclusion: In this paper, we elucidated the genomic variation, ancestry composition, and selective signals related to important economic traits in BoHuai goats. Our studies on the genome of BoHuai goats will not only help to understand the characteristics of the crossbred but also provide a basis for the improvement of cross-breeding programs

Additional file 1 of Exploring root system architecture and anatomical variability in alfalfa (Medicago sativa L.) seedlings

Supplementary Material