Energy Optimization in Multi-UAV-Assisted Edge Data Collection System

In the IoT(Internet of Things)system,the introduction of UAV(Unmanned Aerial Vehicle)as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy.However,the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system.In this work,to deal with the problem,a deployment model of a mobile edge computing(MEC)system based on multi-UAV is proposed.The goal of the model is to minimize the energy consumption of the system in the process of data transmission by optimizing the deployment of UAVs.The DEVIPSK(differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means)is proposed to solve the model.In DEVIPSK,the population is initialized by K-Means to obtain better initial positions of UAVs.Besides,considering the limitation of the fixed mutation strategy in the traditional evolutionary algorithm,a mutation strategy pool is used to update the positions of UAVs.The experimental results show the superiority of the DEVIPSK and provide guidance for the deployment of UAVs in the field of edge data collection in the IoT system.

Macro-Analysis on Across-Fault Crustal Deformation Measurement Data Along the Northern Edge of the Qinghai-Xizang Block

Based on the arrangement of the across-fault measurement data along the northern edge of the Qinghai-Xizang block,we divide the deformation into different types and probe the nature of various fault movements based on these types.The recent situation of tectonic movement of main structural belts and seismicity in this area are expounded.From the above,it is concluded that across-fault measurement can reflect not only the conditions of fault movement nearby but also the change of regional stress fields; not only is this a method to obtain regional seismogenic information and to conduct short-term prediction but it is also involved with large scale space-time prediction of moderate and strong earthquakes on the basis of the macro characteristics of fractures.

Within-Project and Cross-Project Software Defect Prediction Based on Improved Transfer Naive Bayes Algorithm

With the continuous expansion of software scale,software update and maintenance have become more and more important.However,frequent software code updates will make the software more likely to introduce new defects.So how to predict the defects quickly and accurately on the software change has become an important problem for software developers.Current defect prediction methods often cannot reflect the feature information of the defect comprehensively,and the detection effect is not ideal enough.Therefore,we propose a novel defect prediction model named ITNB(Improved Transfer Naive Bayes)based on improved transfer Naive Bayesian algorithm in this paper,which mainly considers the following two aspects:(1)Considering that the edge data of the test set may affect the similarity calculation and final prediction result,we remove the edge data of the test set when calculating the data similarity between the training set and the test set;(2)Considering that each feature dimension has different effects on defect prediction,we construct the calculation formula of training data weight based on feature dimension weight and data gravity,and then calculate the prior probability and the conditional probability of training data from the weight information,so as to construct the weighted bayesian classifier for software defect prediction.To evaluate the performance of the ITNB model,we use six datasets from large open source projects,namely Bugzilla,Columba,Mozilla,JDT,Platform and PostgreSQL.We compare the ITNB model with the transfer Naive Bayesian(TNB)model.The experimental results show that our ITNB model can achieve better results than the TNB model in terms of accurary,precision and pd for within-project and cross-project defect prediction.