A UAV-Assisted V2X Network Architecture with Separated Data Transmission and Network Control

With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a great challenge. In this paper, we combine the advantages of centralized networks and distributed networks approaches for vehicular networks with the aid of Unmanned Aerial Vehicle(UAV), and propose a Center-controlled Multihop Wireless(CMW) networking scheme consisting of data transmission plane performed by vehicles and the network control plane implemented by the UAV.Besides, we jointly explore the advantages of Medium Access Control(MAC) protocols in the link layer and routing schemes in the network layer to facilitate the multi-hop data transmission for the ground vehicles.Particularly, the network control plane in the UAV can manage the whole network effectively via fully exploiting the acquired network topology information and traffic requests from each vehicle, and implements various kinds of control based on different traffic demands, which can enhance the networking flexibility and scalability significantly in vehicular networks.Simulation results validate the advantages of the proposed scheme compared with existing methods.

A Transmission Design in Dynamic Heterogeneous V2V Networks Through Multi-Agent Deep Reinforcement Learning

In highly dynamic and heterogeneous vehicular communication networks,it is challenging to efficiently utilize network resources and ensure demanding performance requirements of safetyrelated applications.This paper investigates machinelearning-assisted transmission design in a typical multi-user vehicle-to-vehicle(V2V)communication scenario.The transmission process proceeds sequentially along the discrete time steps,where several source nodes intend to deliver multiple different types of messages to their respective destinations within the same spectrum.Due to rapid movement of vehicles,real-time acquirement of channel knowledge and central coordination of all transmission actions are in general hard to realize.We consider applying multi-agent deep reinforcement learning(MADRL)to handle this issue.By transforming the transmission design problem into a stochastic game,a multi-agent proximal policy optimization(MAPPO)algorithm under a centralized training and decentralized execution framework is proposed such that each source decides its own transmission message type,power level,and data rate,based on local observations of the environment and feedback,to maximize its energy efficiency.Via simulations we show that our method achieves better performance over conventional methods.

An Anonymous Authentication Scheme for Plugin Electric Vehicles Joining to Charging/Discharging Station in Vehicle-to-Grid(V2G) Networks

Incorporating electric vehicles into smart grid,vehicle-to-Grid(V2G) makes it feasible to charge for large-scale electric vehicles,and in turn support electric vehicles,as mobile and distributed storage units,to discharge to smart grid.In order to provide reliable and efficient services,the operator of V2 G networks needs to monitor realtime status of every plug-in electric vehicle(PEV) and then evaluate current electricity storage capability.Anonymity,aggregation and dynamic management are three basic but crucial characteristics of which the services of V2 G networks should be.However,few of existing authentication schemes for V2 G networks could satisfy them simultaneously.In this paper,we propose a secure and efficient authentication scheme with privacy-preserving for V2 G networks.The scheme makes the charging/discharging station authenticate PEVs anonymously and manage them dynamically.Moreover,the monitoring data collected by the charging/discharging station could be sent to a local aggregator(LAG)in batch mode.In particular,time overheads during verification stage are independent with the number of involved PEVs,and there is no need to update the membership certificate and key pair before PEV logs out.

Real-time instance segmentation based on contour learning

Instance segmentation plays an important role in image processing.The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial contour.To solve these problems,a real-time instance segmentation algorithm based on contour learning was proposed.Firstly,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution kernel.Secondly,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more comprehensively.Thirdly,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real contour.Finally,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance contour.The experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s−1 when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti GPU.The proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.

Novel Sum-of-Sinusoids Simulation Channel Modeling for 6G Multiple-Input Multiple-Output Vehicle-to-Everything Communications

In this paper,a statistical cluster-based simulation channel model with a finite number of sinusoids is proposed for depicting the multiple-input multiple-output(MIMO)communications in vehicleto-everything(V2X)environments.In the proposed sum-of-sinusoids(SoS)channel model,the waves that emerge from the transmitter undergo line-of-sight(LoS)and non-line-of-sight(NLoS)propagation to the receiver,which makes the model suitable for describing numerous V2X wireless communication scenarios for sixth-generation(6G).We derive expressions for the real and imaginary parts of the complex channel impulse response(CIR),which characterize the physical propagation characteristics of V2X wireless channels.The statistical properties of the real and imaginary parts of the complex CIRs,i.e.,autocorrelation functions(ACFs),Doppler power spectral densities(PSDs),cross-correlation functions(CCFs),and variances of ACFs and CCFs,are derived and discussed.Simulation results are generated and match those predicted by the underlying theory,demonstrating the accuracy of our derivation and analysis.The proposed framework and underlying theory arise as an efficient tool to investigate the statistical properties of 6G MIMO V2X communication systems.

Redundancy Elimination in GPRS network

The mechanisms of TCP’s retransmission and reset will result in redundant packets. These redundant packets are often sent unnecessarily to the user over a slow last-hop link delaying useful traffic. This is a problem for wide-area wireless links, such as General Packet Radio Service (GPRS), because unnecessary transmissions waste already limited radio bandwidth, battery power at the mobile terminal and incurs monetary cost due to charging by data volume. The paper first describes a GPRS model, then discusses how to eliminate the redundant packets in GPRS network and presents the simulation results in Network Simulation 2 (NS 2). The more traffic is, the more the network can benefit. In heavy traffic, it can even get more than 30% improvement in throughput. Average delay and loss percent are also lowered.

Image recognition and empirical application of desert plant species based on convolutional neural network

In recent years,deep convolution neural network has exhibited excellent performance in computer vision and has a far-reaching impact.Traditional plant taxonomic identification requires high expertise,which is time-consuming.Most nature reserves have problems such as incomplete species surveys,inaccurate taxonomic identification,and untimely updating of status data.Simple and accurate recognition of plant images can be achieved by applying convolutional neural network technology to explore the best network model.Taking 24 typical desert plant species that are widely distributed in the nature reserves in Xinjiang Uygur Autonomous Region of China as the research objects,this study established an image database and select the optimal network model for the image recognition of desert plant species to provide decision support for fine management in the nature reserves in Xinjiang,such as species investigation and monitoring,by using deep learning.Since desert plant species were not included in the public dataset,the images used in this study were mainly obtained through field shooting and downloaded from the Plant Photo Bank of China(PPBC).After the sorting process and statistical analysis,a total of 2331 plant images were finally collected(2071 images from field collection and 260 images from the PPBC),including 24 plant species belonging to 14 families and 22 genera.A large number of numerical experiments were also carried out to compare a series of 37 convolutional neural network models with good performance,from different perspectives,to find the optimal network model that is most suitable for the image recognition of desert plant species in Xinjiang.The results revealed 24 models with a recognition Accuracy,of greater than 70.000%.Among which,Residual Network X_8GF(RegNetX_8GF)performs the best,with Accuracy,Precision,Recall,and F1(which refers to the harmonic mean of the Precision and Recall values)values of 78.33%,77.65%,69.55%,and 71.26%,respectively.Considering the demand factors of hardware equipment and inference time,Mobile NetworkV2 achieves the best balance among the Accuracy,the number of parameters and the number of floating-point operations.The number of parameters for Mobile Network V2(MobileNetV2)is 1/16 of RegNetX_8GF,and the number of floating-point operations is 1/24.Our findings can facilitate efficient decision-making for the management of species survey,cataloging,inspection,and monitoring in the nature reserves in Xinjiang,providing a scientific basis for the protection and utilization of natural plant resources.

Machine learning in vehicular networking:An overview

As vehicle complexity and road congestion increase,combined with the emergence of electric vehicles,the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential.The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks.Particularly,5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities.Machine Learning(ML),a powerful methodology for adaptive and predictive system development,has emerged in both vehicular and conventional wireless networks.Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions,such as traditional control loop design and optimization techniques.This article provides a short survey of ML applications in vehicular networks from the networking aspect.Research topics covered in this article include network control containing handover management and routing decision making,resource management,and energy efficiency in vehicular networks.The findings of this paper suggest more attention should be paid to network forming/deforming decision making.ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies,such as mobile edge computing for real-world deployment.Research datasets,simulation environment standardization,and method interpretability also require more research attention.

A new algorithm for wireless sensor network based on NS-2

Considering wireless sensor network characteristics,this paper uses network simulator,version2(NS-2)algorithm to improve Ad hoc on-demand distance vector(AODV)routing algorithm,so that it can be applied to wireless sensor networks.After studying AODV routing protocol,a new algorithm called Must is brought up.This paper introduces the background and algorithm theory of Must,and discusses the details about how to implement Must algorithm.At last,using network simulator(NS-2),the performance of Must is evaluated and compared with that of AODV.Simulation results show that the network using Must algorithm has perfect performance.

Mathematical Model Validation of Search Protocols in MP2P Networks

Broadcasting is a basic technique in Mobile ad-hoc network(MANET),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive flooding technique,floods the network with query messages,while the random walk technique operates by contacting the subsets of every node’s neighbors at each step,thereby restricting the search space.One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource.Many earlier works have mainly focused on the simulation-based analysis of flooding,and its variants under a wired network.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of P2P systems running over MANET.In this paper,we describe how P2P resource discovery protocols perform badly over MANETs.To address the limitations,we propose a new protocol named ABRW(Address Broadcast Random Walk),which is a lightweight search approach,designed considering the underlay topology aimed to better suit the unstructured architecture.We provide the mathematical model,measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques.Further,we also derive three relevant search performance metrics,i.e.,mean no.of steps needed to find a resource,the probability of finding a resource,and the mean no.of message overhead.We validated the analytical expressions through simulations.The simulation results closely matched with our analyticalmodel,justifying our findings.Our proposed search algorithm under such highly dynamic self-evolving networks performed better,as it reduced the search latency,decreased the overall message overhead,and still equally had a good success rate.

Research on saponin active compounds of Tuchao Baibiandouren for the treatment of type-2 diabetes based on UHPLC-Q-Exactive Orbitrap MS and network pharmacology

Objective To explore the pharmacological mechanism of active saponin compounds of Tuchao Baibiandouren(Lablab Semen Album fried with earth,TCBBDR)in treating type 2 diabetes(T2DM)using UHPLC-Q-Exactive Orbitrap MS and network pharmacology.Methods UHPLC-Q-Exactive Orbitrap MS was used for a qualitative analysis of saponin compounds in TCBBDR.PharmMapper and CTD were used to screen drug active compounds and disease targets,and an active compound-target network was constructed via Cytoscape 3.8.0.Molecular docking was applied with the drug active compounds and key targets using AutoDock Vina 1.1.2,and a trajectory for the molecular dynamics simulation was completed by GROMACS 2019-3.Results Sixteen saponin compounds were identified from TCBBDR,along with 292 saponin compoud targets and 792 T2DM targets.Through Venn analysis of target saponin constituents and T2DM related targets,a total of 91 intersection targets were screened out in the treatment of T2DM with saponin.The mean values of degree,betweenness centrality and closeness centrality were taken as the thresholds to screen out 22 key genes,among which 4 key proteins namely MAPK1,IGF1 EGFR,PIK3R1 were selected in the top 10 key genes.On this basis,the saponin active constituent-target-signaling pathway network was established.The Gene Ontology(GO)enrichment analysis showed that the related biological modules included activity of steroid hormone receptor,steroid binding,and insulin receptor binding,etc.;the related signaling pathways were EGFR,PI3K-Akt and MAPK,etc.;regulating signaling pathways like MAPK could induce the proliferation,inhibition and apoptosis of pancreaticβcells,increase the quantity of pancreaticβcells,improve the functions of pancreaticβcells and stimulate the insulin secretion.Docking experiment analysis showed that all selected saponin compounds could enter the active sites of targets and form 3–14 hydrogen bonds with residues of the active sites.Moreover,van der Waals forces were present between chemical compounds and active sites.By combining the docking binding energy,we determined that the chemical compounds showed strong binding energy to the targets.Conclusion TCBBDR exerts therapeutic effects on diabetes through multi-compound and multi-target collaboration.Specifically,saponin components mediate pathways including inflammatory reaction and signal transduction to treat T2DM by regulating several key proteins that interact with EGFR and a series of signaling pathways related to disease development.

TIME-CRITICAL COMMUNICATION AND COMPUTATION FOR INTELLIGENT VEHICULAR NETWORKS

Vehicular networks are expected to empower auto mated driving and intelligent transportation via vehicle-to-everything(V2X)communications and edge/cloud-assisted computation,and in the meantime Cellular V2X(C-V2X)is gaining wide support from the global industrial ecosystem.The 5G NR-V2X technology is the evolution of LTE-V2X,which is expected to provide ultra-Reliable and Low-Latency Communications(uRLLC)with 1ms latency and 99.999%reliability.Nevertheless,vehicular networks still face great challenges in supporting many emerging time-critical applications,which comprise sensing,communication and computation as closed-loops.

Space Division Multiple Access for Cellular V2X Communications

Vehicular communication is the backbone of future Intelligent Transportation Systems(ITS).It offers a network-based solution for vehicle safety,cooperative awareness,and traffic management applications.For safety applications,Basic Safety Messages(BSM)containing mobility information is shared by the vehicles in their neighborhood to continuously monitor other nearby vehicles and prepare a local traffic map.BSMs are shared using mode 4 of Cellular V2X(C-V2X)communications in which resources are allocated in an ad hoc manner.However,the strict packet transmission requirements of BSM and hidden node problem causes packet collisions in a vehicular network,thus reducing the reliability of safety applications.Moreover,as vehicles choose the transmission resources in a distributed manner in mode 4 of CV2X,the packet collision problem is further aggravated.This paper presents a novel solution in the form of a Space Division Multiple Access(SDMA)protocol that intelligently schedules BSM transmissions using vehicle position data to reduce concurrent transmissions from hidden node interferers.The proposed protocol works by dividing road segments into clusters and subclusters.Several sub-frames are allocated to a cluster and these sub-frames are reused after a certain distance.Within a cluster,sub-channels are allocated to sub-clusters.We implement the proposed SDMA protocol and evaluate its performance in a highway vehicular network.Simulation results show that the proposed SDMA protocol outperforms standard Sensing-Based Semi Persistent Scheduling(SB-SPS)in terms of safety range and packet delay.

Epidemiological modeling of passive worm propagation on the P2P file-sharing network

The peer-to-peer（P2P） file-sharing network as a vehicle of disseminating files has become very popular. The appearance of dozens of kinds of passive worms on this network has, however, made it unsecured. This problem has been paid attention and a few of models for passive worm propagation has been presented. Unfortunately, the dynamic properties of this network are ignored in these models. Given the fact, the characteristics of both this network and the passive worm are identified, and on this basis a new mathematical model of passive worm propagation on the P2P network is presented in applying epidemiology in this paper. Note that the dynamic properties of this network are considered in the presented model. The model has been validated by large scale simulation experiments, which demonstrates that the presented model may be used for analyzing the behaviors of passive worms and predicting the trend of their propagation.

Smart Home Networking: Lessons from Combining Wireless and Powerline Networking

Integrating the power grid technology with renewable power generation technologies, Demand Response (DR) programs enabled by the Advanced Metering Infrastructure (AMI) were introduced into the power grid in the interest of both utilities and residents. They help to achieve load balance and increase the grid reliability by encouraging residents to reduce their power usage during peak load periods in return for incentives. To automate this process, appliances, in-house sensors, and the AMI controller need to be networked together. In this paper, we compare mainstream network technologies applicable to home appliance control and propose a solution combining Power Line Communication (PLC) with wireless communication in smart homes for the purpose of energy saving. We extended NS-2, a popular network simulator, to model such combined network scenarios. Using a number of different routing strategies, we then model and evaluate the network performance of DR programs in smart homes in such a combined network.

Modeling active worm propagation on the P2P network

This paper analyzes the characteristics of the Peer-to-Peer （P2P） active worm and its attacking mechanism, and then proposes a mathematical model of propagation of the P2P active worm applying Epidemiology. Based on the analysis on the protocols of realistic P2P systems, a software which can be used to simulate the P2P network environment and the propagation of P2P active worm is imple- mented in this paper. A large number of simulation experiments are performed using the developed simulation software. The results from these simulation experiments validate the proposed model, which means that the model can be used to analyze the spreading behaviors of the P2P active worm and predict its trend.

Review of the Security Issues in Vehicular Ad Hoc Networks (VANET)

There is a significant increase in the rates of vehicle accidents in countries around the world and also the casualties involved ever year. New technologies have been explored relating to the Vehicular Ad Hoc Network (VANET) due to the increase in vehicular traffic/congestions around us. Vehicular communication is very important as technology has evolved. The research of VANET and development of proposed systems and implementation would increase safety among road users and improve the comfort for the corresponding passengers, drivers and also other road users, and a great improvement in the traffic efficiency would be achieved. This research paper investigates the current and existing security issues associated with the VANET and exposes any slack amongst them in order to lighten possible problem domains in this field.

A Multi-Mode Public Transportation System Using Vehicular to Network Architecture

The number of accidents in the campus of Suranaree University of Technology(SUT)has increased due to increasing number of personal vehicles.In this paper,we focus on the development of public transportation system using Intelligent Transportation System(ITS)along with the limitation of personal vehicles using sharing economy model.The SUT Smart Transit is utilized as a major public transportation system,while MoreSai@SUT(electric motorcycle services)is a minor public transportation system in this work.They are called Multi-Mode Transportation system as a combination.Moreover,a Vehicle toNetwork(V2N)is used for developing theMulti-Mode Transportation system in the campus.Due to equipping vehicles with On Board Unit(OBU)and 4G LTE modules,the real time speed and locations are transmitted to the cloud.The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival(ETA).In terms of vehicle classifications and counts,we deployed CCTV cameras,and the recorded videos are analyzed by using You Only Look Once(YOLO)algorithm.The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed.Contrary to the existing researches,the proposed system is implemented in the real environment.The final results unveil the attractiveness and satisfaction of users.Also,due to the proposed system,the CO_(2) gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.

Performance Analysis under MAC Layer Misbehavior Attack in Mobile Ad-Hoc Networks

This work presents a multi-criteria analysis of the MAC （media access control） layer misbehavior of the IEEE （Institute of Electrical and Electronics Engineers） 802.11 standard, whose principle is to cheat at the protocol to increase the transmission rate by greedy nodes at the expense of the other honest nodes. In fact, IEEE 802.11 forces nodes for access to the channel to wait for a back off interval, randomly selected from a specified range, before initiating a transmission. Greedy nodes may wait for smaller back-off intervals than honest nodes, and then obtaining an unfair assignment. In the first of our works a state of art on the research on IEEE 802.11 MAC layer misbehavior are presented. Then the impact of this misbehavior at the reception is given, and we will generalize this impact on a large scale. An analysis of the correlation between the throughput and the inter-packets time is given. Afterwards, we will define a new metric for measuring the performance and capability of the network.

Intelligent Management Functionality for Emergency Medical Applications Based on Cognitive Networking Principles

Telecommunications and information technology rapidly migrate towards the Future Internet (FI) era, which is characterized by powerful and complex network infrastructures, advanced applications, services and content, efficient power management as well as extensions in the business model. One of the main application areas that find prosper ground in the FI era, is medicine. In particular, latest advances in medical sciences are reflected on their capability to approach previously past-cure diseases, as well as to prevent the appearance and evolution of unpleasant situations. Those advances are often derived from interdisciplinary solutions to complex medical problems, supported by communications and electronics, which target fast, reliable and stable solutions to problems that are demanding in terms of velocity and accuracy. The goal of this paper is to present intelligent, knowledge-based management functionality capable of supporting emergency medical applications. An indicative emergency medical scenario is provided, along with extensive simulation results using the Network Simulator-2 (NS-2), for evaluating the performance of the proposed functionality.