IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

In this paper,we consider mobile edge computing(MEC)networks against proactive eavesdropping.To maximize the transmission rate,IRS assisted UAV communications are applied.We take the joint design of the trajectory of UAV,the transmitting beamforming of users,and the phase shift matrix of IRS.The original problem is strong non-convex and difficult to solve.We first propose two basic modes of the proactive eavesdropper,and obtain the closed-form solution for the boundary conditions of the two modes.Then we transform the original problem into an equivalent one and propose an alternating optimization(AO)based method to obtain a local optimal solution.The convergence of the algorithm is illustrated by numerical results.Further,we propose a zero forcing(ZF)based method as sub-optimal solution,and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes.

Energy Efficiency Maximization in Mobile Edge Computing Networks via IRS assisted UAV Communications

In this paper,we investigate the energy efficiency maximization for mobile edge computing(MEC)in intelligent reflecting surface(IRS)assisted unmanned aerial vehicle(UAV)communications.In particular,UAVcan collect the computing tasks of the terrestrial users and transmit the results back to them after computing.We jointly optimize the users’transmitted beamforming and uploading ratios,the phase shift matrix of IRS,and the UAV trajectory to improve the energy efficiency.The formulated optimization problem is highly non-convex and difficult to be solved directly.Therefore,we decompose the original problem into three sub-problems.We first propose the successive convex approximation(SCA)based method to design the beamforming of the users and the phase shift matrix of IRS,and apply the Lagrange dual method to obtain a closed-form expression of the uploading ratios.For the trajectory optimization,we propose a block coordinate descent(BCD)based method to obtain a local optimal solution.Finally,we propose the alternating optimization(AO)based overall algorithmand analyzed its complexity to be equivalent or lower than existing algorithms.Simulation results show the superiority of the proposedmethod compared with existing schemes in energy efficiency.

Development of IoT Based Mobile Robot for Automated Guided Vehicle Application

Mobile robot has been one of the researches focuses in this era due to the demands in automation.Many industry players have been using mobile robot in their industrial plant for the purpose of reducing manual labour as well as ensuring more efficient and systematic process.The mobile robot for industrial usage is typically called as Automated Guided Vehicle(AGV).The advances in the navigation technology allows the AGV to be used for many tasks such as for carrying load to pre-determined locations sent from mobile app,stock management and pallet handling.More recently,the concept of Industry 4.0 has been widely practiced in the industries,where important process data are exchange over the internet for an improved management.This paper will therefore discuss the development of Internet of Things(IoT)bases mobile robot for AGV application.In this project a mobile robot platform is designed and fabricated.The robot is controlled to navigate from one location to another using line following mechanism.Mobile App is designed to communicate with the robot through the Internet of Things(IoT).RFID tags are used to identify the locations predetermined by user.The results show that the prototype is able to follow line and go to any location that was preregistered from the App through the IoT.The mobile robot is also able to avoid collision and any obstacles that exist on its way to perform any task inside the workplace.

Resource Load Prediction of Internet of Vehicles Mobile Cloud Computing

Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.

Unmanned aerial vehicle based intelligent triage system in mass-casualty incidents using 5G and artificial intelligence

BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly depend on the UAV operator’s experience.We used UAVs and artificial intelligence(AI)to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.METHODS:This was a preliminary experimental study.We developed an intelligent triage system based on two AI algorithms,namely OpenPose and YOLO.Volunteers were recruited to simulate the MCI scene and triage,combined with UAV and Fifth Generation(5G)Mobile Communication Technology real-time transmission technique,to achieve triage in the simulated MCI scene.RESULTS:Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs.Eight volunteers participated in the MCI simulation scenario.The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.CONCLUSION:The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.

Electric Vehicles Analysis inside Electric Mobility Looking for Energy Efficient and Sustainable Metropolis

This paper aims to study and evaluate electric mobility over time, focusing on the development of the electric car. Methodologically, in order to accomplish this intent, the characterization of the electric vehicle (EV) is made based on the variables which determine its performance, such as: assessment of speeds, distance traveled, analysis of facts related to the energy source (electro-chemical accumulators) and analysis of the determining system of electric mobility (the electric engine as a function of power (W) and voltage (V)). This way, to demonstrate the effects of time, this process will be analyzed from the beginning of the 20th century (1930s) to the present (the first decade of the 21st century), methodologically structured in 4 cycles that show the performance of the EV. The results show the existence of vulnerabilities and of electric mobility potential, as well as the nuances of the development of the electric vehicle along the years and along the transformations in what is considered state-of-the-art. Thus, in the case of batteries, it is evident that the lithium-ion type used nowadays reveals better results due to its higher specific efficient energy, which maximizes energy autonomy to 200 km. In the beginning, the insertion of the electric vehicle was commercially harmed by the fundamental limitations of batteries as a power source. Conclusively, on certain occasions there have been improvements in the aerodynamics, engines, weight and size of the batteries, demonstrating the maturity of EVs.

基于MOBILE6.2的北京市出租车排放污染物分析

利用MOBILE6.2模型计算了2000年、2005年及2008年北京市出租车的排放因子,同时,计算了北京市出租车2005年更换部分旧车以及2008年更换全部旧车以后,相对于2000年污染物的降低总量。结果表明,排放污染物随更换车型大大下降。HC排放物的总量下降了1779.4T,CO排放物的总量下降了13304.3T,NOx排放物的总量下降了684.4T。2008年完成出租车全部更换后,相对于2000年,HC排放物的总量下降3097.9T,CO排放物的总量下降17483.5T,NOx排放物的总量下降了1211.8T。同时,还估算了2000年和2008年其他类型机动车的排放因子及污染物的排放总量,并计算了各车型对排放的贡献率。

MOBILE计算中国机动车CO排放因子时的计算年确定

在使用MOBILE计算机动车排放因子时,一个较为重要的计算参数——计算年,在美国是根据实际年份来确定的,但是由于我国机动车排放发展水平与美国的不同,其值的确定是比较困难的关键问题。文章利用我国的实测机动车排放因子数据,在同等条件下使用MOBILE计算排放因子,并与道路实测数据对比,得到不同测试年份对应的MOBILE计算年,从而回归出使用MOBILE计算机动车排放因子的计算年公式。在今后利用MOBILE计算机动车排放时,可以方便的使用公式得到对应的MOBILE计算年,从而提高计算的精确度。

基于Mobile GIS的车辆导航系统开发研究

简要的介绍了MobileGIS发展状况、关键技术,对基于MobileGIS的车辆导航系统在总体结构、功能模块等方面进行了设计,并分析了地图显示、GPS数据接收、模块通信等各个环节及其实现方法,通过嵌入式开发语言开发了系统的主要功能模块,实现了集电子地图、GPS移动定位、路径规划、智能导航等多项功能于一体的车辆导航系统MobileMap,对系统实际应用中存在的问题进行了分析并提出相应的解决方案。

基于MOBILE 6.2模型的武汉市机动车污染物排放特征分析

结合武汉市环境参数以及机动车保有量情况,将MOBILE 6.2模型用于武汉市机动车污染物排放特征的本地化研究,计算得到不同车型的排放因子,并核算出武汉市机动车分车型的污染物排放量和分担率。结果表明,2014年武汉市机动车污染物总HC、CO和NO_x分别为7.78万t、59.61万t和6.32万t.八类车型中汽油轿车排放的总HC和CO分别占总量的69.71%和82.86%,是这两类污染物的主要贡献车型。这主要是由于近年来汽油轿车尤其是私家车数量的高速增长。对于NO_x的排放,其主要贡献车型为重型柴油车,仅有5.44%的保有量但其排放分担率却高达约60%,因此严格控制重型柴油车可以有效减少机动车污染物NO_x的排放。同样的由于汽油轿车的总量较大,其NO_x排放的分担率也达到31.65%.

Analysis of reflected signal of quad rotor UAV based on model fitting in mobile communication system

In view of the many scenes of unmanned aerial vehicle(UAV)detection,a third-party signal source is used to design a receiver to monitor the UAV.It is of great significance to understand the reflection of the signal illuminating the UAV.Taking the communication base station(BS)signal as the third-party signal source,and considering the complete transmission link,reflection changes and loss fading of the communication signal,this study conducts model fitting for irregular UAV targets,simplifying complex targets into a combination of simple targets.Furthermore,the influence of the dielectric constant of the target surface and the signal irradiation angle on the signal reflection is analyzed.The analysis shows that the simulation results of this model fitting method are consistent with the results of other literature,which provides theoretical support for the detection of low and slow small targets such as UAVs.

Parameter Identification and Application of Slippage Kinematics for Tracked Mobile Robots

A new parameter identification method is proposed to solve the slippage problem when tracked mobile robots execute turning motions.Such motion is divided into two states in this paper:pivot turning and coupled turning between angular velocity and linear velocity.In the processing of pivot turning,the slippage parameters could be obtained by measuring the end point in a square path.In the process of coupled turning,the slippage parameters could be calculated by measuring the perimeter of a circular path and the linear distance between the start and end points.The identification results showed that slippage parameters were affected by velocity.Therefore,a fuzzy rule base was established with the basis on the identification data,and a fuzzy controller was applied to motion control and dead reckoning.This method effectively compensated for errors resulting in unequal tension between the left and right tracks,structural dimensions and slippage.The results demonstrated that the accuracy of robot positioning and control could be substantially improved on a rigid floor.

A Reliable Routing Algorithm with Network Coding in Internet of Vehicles

The intelligent vehicle network uses advanced information technology to establish an efficient integrated vehicle transport system, which has received great attention in industry and academia, lnternet of Vehicles （loV） in an urban environment is operated in a wireless environment with high bit error rate and interference. In addition, the wireless link between vehicles is likely to be lost. All of this makes it an important challenge to provide reliable mobile routing in an urban traffic environment. In this paper, a reliable routing algorithm with network coding （RR_ NC） is proposed to solve the above problems. A routing node sequence is discovered in IoV from source to destination by multi-metric ant colony optimization algorithm （MACO）, and then clusters are formed around every node in the sequence. By adding linear encoding into the transmission of data between vehicle＇s clusters, the RR_NC provides much more reliable transmission and can recover the original message in the event of disorder and loss of message. Simulations are taken under different scenarios, and the results prove that this novel algorithm can deliver the information more reliably between vehicles in real-time with lower data loss and communication overhead.

A Robust Hybrid Multisource Data Fusion Approach for Vehicle Localization

In this paper, an innovative collaborative data fusion approach to ego-vehicle localization is presented. This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low cost GPS, an INS, an Odometer and a Steering wheel angle encoder. The OKS is developed addressing the challenge of managing reactivity and robustness during a real time ego-localization process. For ego-vehicle localization, especially for highly dynamic on-road maneuvers, a filter needs to be robust and reactive at the same time. In these situations, the balance between reactivity and robustness concepts is crucial. The OKS filter represents an intelligent cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO). It combines advantages coming from two filters: Particle Filter (PF) and Extended Kalman filter (EKF). The OKS is tested using real embedded sensors data collected in the Satory’s test tracks. The OKS is also compared with both the well-known EKF and the Particle Filters (PF). The results show the efficiency of the OKS for a high dynamic driving scenario with damaged and low quality GPS data.

Computation offloading and resource allocation for UAV-assisted IoT based on blockchain and mobile edge computing

Recently,Internet of Things(IoT)have been applied widely and improved the quality of the daily life.However,the lightweight IoT devices can hardly implement complicated applications since they usually have limited computing resource and just can execute some simple computation tasks.Moreover,data transmission and interaction in IoT is another crucial issue when the IoT devices are deployed at remote areas without manual operation.Mobile edge computing(MEC)and unmanned aerial vehicle(UAV)provide significant solutions to these problems.In addition,in order to ensure the security and privacy of data,blockchain has been attracted great attention from both academia and industry.Therefore,an UAV-assisted IoT system integrated with MEC and blockchain is pro-posed.The optimization problem in the proposed architecture is formulated to achieve the optimal trade-off between energy consumption and computation latency through jointly considering computa-tion offloading decision,spectrum resource allocation and computing resource allocation.Consider-ing this complicated optimization problem,the non-convex mixed integer problem can be transformed into a convex problem,and a distributed algorithm based on alternating direction multiplier method(ADMM)is proposed.Simulation results demonstrate the validity of this scheme.

The Performance Analysis for Distributed Collaborative Beamforming Transmission of Clustered Mobile Sensor Network

Sensor nodes cannot directly communicate with the distant unmanned aerial vehicle( UAV) for their low transmission power. Distributed collaborative beamforming from sensor nodes within a cluster is proposed to provide high speed data transmission to the distant UAV. The bit error ratio( BER) closed-form expression of distributed collaborative beamforming transmission with mobile sensor nodes has been derived. Furthermore,based on the theoretical BER analysis and the numerical results,we have analyzed the impacts of nodes 'mobility,number of sensor nodes,transmission power and the elevation angle of UAV on the BER performance of collaborative beamforming. And we come to the following conclusions: the mobility of sensor nodes largely decreases the BER performance; when the position deviation radius is large,incensement in power cannot improve BER anymore; the size of cluster should be bigger than 10 for the purpose of achieving good BER performance in Rayleigh fading channel.

IoT-Cloud Empowered Aerial Scene Classification for Unmanned Aerial Vehicles

Recent trends in communication technologies and unmanned aerial vehicles(UAVs)find its application in several areas such as healthcare,surveillance,transportation,etc.Besides,the integration of Internet of things(IoT)with cloud computing environment offers several benefits for the UAV communication.At the same time,aerial scene classification is one of the major research areas in UAV-enabledMEC systems.In UAV aerial imagery,efficient image representation is crucial for the purpose of scene classification.The existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average results.Therefore,the current research work introduces a new DL-enabled aerial scene classificationmodel forUAV-enabledMECsystems.The presented model enables theUAVs to capture aerial imageswhich are then transmitted to MEC for further processing.Next,CapsuleNetwork(CapsNet)-based feature extraction technique is applied to derive a set of useful feature vectors from the aerial image.It is important to have an appropriate hyperparameter tuning strategy,since manual parameter tuning of DL model tend to produce several configuration errors.In order to achieve this and to determine the hyperparameters of CapsNetmodel,Shuffled Shepherd Optimization(SSO)algorithm is implemented.Finally,Backpropagation Neural Network(BPNN)classification model is applied to determine the appropriate class labels of aerial images.The performance of SSO-CapsNet model was validated against two openly-accessible datasets namely,UC Merced(UCM)Land Use dataset andWHU-RS dataset.The proposed SSO-CapsNet model outperformed the existing state-of-the-art methods and achieved maximum accuracy of 0.983,precision of 0.985,recall of 0.982,and F-score of 0.983.

The Development of Self-Balancing Controller for One-Wheeled Vehicles

The purpose of this study is to develop a self-balancing controller (SBC) for one-wheeled vehicles (OWVs). The composition of the OWV system includes: a DSP motion card, a wheel motor, and its driver. In addition, a tilt and a gyro, for sensing the angle and angular velocity of the body slope, are used to realize self-balancing controls. OWV, a kind of unicycle robot, can be dealt with as a mobile-inverted-pendulum system for its instability. However, for its possible applications in mobile carriers or robots, it is worth being further developed. In this study, first, the OWV system model will be derived. Next, through the simulations based on the mathematical model, the analysis of system stability and controllability can be evaluated. Last, a concise and realizable method, through system pole-placement and linear quadratic regulator (LQR), will be proposed to design the SBC. The effectiveness, reliability, and feasibility of the proposal will be con- firmed through simulation studies and experimenting on a physical OWV.

The On-Vehicle Inspection System Based on Passive UHF RFID

This article introduces a design theory of vehicle-related management in forms of system linkage in a certain close environment.It analyses the technology advantages,working principles,system structures and design solutions of the scene inspection system based on passive UHF RFID technology,which has functions of data capturing,image collection,wireless data transmission and provision of warning alerts.The system enables scene disposal of vehicle-related management in a specific environment,people management in large-scale events and management of important materials.The system has the capability of rapid network connection and scene inspection especially in emergencies and public security affairs,in which advance deployment is normally inefficient.The system has been successfully applied in the vehicle safety monitoring system in the 2010 Shanghai World Expo Park.

OKPS: A Reactive/Cooperative Multi-Sensors Data Fusion Approach Designed for Robust Vehicle Localization

This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle.