Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

One of the key challenges in ad-hoc networks is the resource discovery problem.How efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),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 ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search space.Many earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network scenario.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P networks.In this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT architecture.We provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed technique.We analytically derive 3 relevant key performance measures,i.e.,the avg.number of steps needed to nd a resource,the probability of locating a resource,and the avg.number of messages generated during the entire search process.

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.

Mobile Ad Hoc网络与Internet互联的综合网关发现方法

大量基于adhoc网络的移动应用得以开展以及被认可的一大挑战就在于其能否提供足够好Internet连接,本文实现了一种MobileAdHoc网络与Internet互联时的综合网关发现方法,由于综合了主动与被动网关发现方法的优点,同时允许中间移动节点解析全局连接信息并在必要的时候以之回复请求节点,因此在合理的开销下改善了移动节点多跳访问Internet的性能,用NS2进行的仿真结果说明了综合方法的有效性。

A novel nondestructive detection approach for seed cotton lint percentage using deep learning

Background The lint percentage of seed cotton is one of the most important parameters for evaluating seed cotton quality and affects its price.The traditional measuring method of lint percentage is labor-intensive and time-consuming;thus,an efficient and accurate measurement method is needed.In recent years,classification-based deep learning and computer vision have shown promise in solving various classification tasks.Results In this study,we propose a new approach for detecting the lint percentage using MobileNetV2 and transfer learning.The model is deployed on a lint percentage detection instrument,which can rapidly and accurately determine the lint percentage of seed cotton.We evaluated the performance of the proposed approach using a dataset comprising 66924 seed cotton images from different regions of China.The results of the experiments showed that the model with transfer learning achieved an average classification accuracy of 98.43%,with an average precision of 94.97%,an average recall of 95.26%,and an average F1-score of 95.20%.Furthermore,the proposed classification model achieved an average accuracy of 97.22%in calculating the lint percentage,showing no significant difference from the performance of experts(independent-sample t-test,t=0.019,P=0.860).Conclusion This study demonstrated the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton.The proposed approach is a promising alternative to traditional methods,providing a rapid and accurate solution for the industry.

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.

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.

Mobility Management in Small Cell Cluster of Cellular Network

The installation of small cells in a 5G network extends the maximum coverage and provides high availability.However,this approach increases the handover overhead in the Core Network(CN)due to frequent handoffs.The variation of user density and movement inside a region of small cells also increases the handover overhead in CN.However,the present 5G system cannot reduce the handover overhead in CN under such circumstances because it relies on a traditionally rigid and complex hierarchical sequence for a handover procedure.Recently,Not Only Stack(NO Stack)architecture has been introduced for Radio Access Network(RAN)to reduce the signaling during handover.This paper proposes a system based on NO Stack architecture and solves the aforementioned problem by adding a dedicated local mobility controller to the edge cloud for each cluster.The dedicated cluster controller manages the user mobility locally inside a cluster and also maintains the forwarding data of a mobile user locally.To reduce the latency for X2-based handover requests,an edge cloud infrastructure has been also developed to provide high-computing for dedicated controllers at the edge of a cellular network.The proposed system is also compared with the traditional 3GPP architecture and other works in the context of overhead and delay caused by X2-based handover requests during user mobility.Simulated results show that the inclusion of a dedicated local controller for small clusters together with the implementation of NO Stack framework reduces the significant amount of overhead of X2-based handover requests at CN.

Performance Analysis of Grid Based AODV Routing Algorithm for AD Hoc Wireless Networks

In many traditional On Demand routing algorithms in Ad hoc wireless networks, a simple flooding mechanism is used to broadcast route request (RREQ) packets when there is a need to establish a route from a source node to a destination node. The broadcast of RREQ may lead to high channel contention, high packet collisions, and thus high delay to establish the routes, especially with high density networks. Ad hoc on Demand Distance Vector Routing Protocol (AODV) is one among the most effective Reactive Routing Protocols in MANETs which use simple flooding mechanism to broadcast the RREQ. It is also used in Wireless Sensor Networks (WSN) and in Vehicular Ad hoc Networks (VANET). This paper proposes a new modified AODV routing protocol EGBB-AODV where the RREQ mechanism is using a grid based broadcast (EGBB) which reduces considerably the number of rebroadcast of RREQ packets, and hence improves the performance of the routing protocol. We developed a simulation model based on NS2 simulator to measure the performance of EGBB-AODV and compare the results to the original AODV and a position-aware improved counter-based algorithm (PCB-AODV). The simulation experiments that EGBB-AODV outperforms AODV and PCB-AODV in terms of end-to-end delay, delivery ratio and power consumption, under different traffic load, and network density conditions.

Cooperative Layer-2 Based Routing Approach for Hybrid Wireless Mesh Networks

In a Wireless Mesh Network(WMN),the convenience of a routing strategy strongly depends on the mobility of the intermediate nodes that compose the paths.Taking this behaviour into account,this paper presents a routing scheme that works differently accordingly to the node mobility.In this sense,a proactive routing scheme is restricted to the backbone to promote the use of stable routes.Conversely,the reactive protocol is used for searching routes to or from a mobile destination.Both approaches are simultaneously implemented in the mesh nodes so that the routing protocols share routing information that optimises the network performance.Aimed at guaranteeing the IP compatibility,the combination of the two protocols in the core routers is carried out in the Medium Access Control(MAC)layer.In contrast to the operation in the IP layer where two routing protocols cannot work concurrently,the transfer of the routing tasks to the MAC layer enables the use of multiple independent forwarding tables.Simulation results show the advantage of the proposal in terms of packet losses and data delay.

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.

动态场景下仓储机器人的视觉定位与建图

针对目前大多数室内仓储机器人的视觉定位与建图算法(SLAM)是假设机器人处在静态的环境,但是当场景中出现移动的物体时,机器人自身定位的准确性和稳定性易受到巨大影响,而其他室内定位技术比如超宽带、蓝牙等必须在无线信号覆盖的条件下工作的问题,提出一种面向室内仓储机器人在动态场景下的视觉SLAM算法:在基于旋转不变特征点的定位与建图改进算法(ORB-SLAM2)基础上,用几何对应神经网络(GCNv2)来替换基于图像金字塔的特征点提取算法;添加目标检测(YOLOv4)的语义线程,并且使用光流法来追踪特征点;然后通过运动一致性检测来识别图像中潜在的动态物体;最后剔除动态特征点后进行位姿估计。实验结果表明,在高度动态的室内场景下,提出的算法相对于ORB-SLAM2算法的绝对轨迹误差可减小95.56%~98.21%,能够有效解决ORB-SLAM2在动态场景下定位不准确的问题。

NS-2移动代理网络仿真功能扩展设计和实现

在分析网络仿真工具NS-2特点的基础上,提出了移动代理模型的建立以及数据包、节点和代理扩展技术,通过修改原有的数据结构和增加新的网络模块扩展NS-2的功能以实现移动代理网络的仿真,并据此设计实现了移动代理网络模拟软件。利用该软件,可以有效地分析移动代理系统的网络性能,为其设计和评估提供可靠的依据。

改进的随机早期检测非线性算法

主动队列管理是目前的研究热点,随机早期检测(RED)算法是一种经典的队列管理算法。线性RED算法虽然简单且容易计算,但队列位于最小阈值和最大阈值附近时的丢包概率都不太合理。在论证了平均队列长度和丢包概率间为非线性性质后,提出了一种改进非线性RED算法——JRED。利用NS2对改进的算法进行仿真,结果表明,JRED算法提高了平均吞吐量,降低了丢包概率,增强了网络稳定性和可靠性。

基于NS-2的MANET路由协议仿真及性能评估

简单介绍MANET(Mobile Ad hoc Network)路由协议后,提出定量评估MANET路由协议性能的六个基本指标。基于网络仿真器NS-2阐述了评估和测试路由协议性能的仿真模型及数据结果的分析方法,并给出仿真实例及其分析。结果表明,模型仿真结果接近理论分析和实际情况,该性能评估方法有较强的实用性和通用性。

大规模无线移动自组织网络混合模拟测试方法

针对已有无线移动自组织网络(MANET)测试方法存在模型简单、成本高、不易重复等问题,提出一种基于NS2的大规模MANET混合模拟测试方法(LHEN)。使用NS2的模拟功能,利用Tap代理完成虚拟分组与实际分组间的嵌套和解嵌,通过网络对象及NS2实时调度器实现虚拟环境和真实环境的通信;采用控制网络无线信号强度的方式模拟真实节点移动,完成对真实网络环境的构建。使用混合模拟和仿真两种方法分别建立大规模MANET网络进行实验对比,所测性能指标变化趋势基本一致,平均差值低于18.7%。实验结果表明,LHEN可以用于大规模MANET网络各项性能指标测试及验证。

基于J2ME的移动网络游戏设计与实现

基于J2ME技术,介绍了泡泡堂的游戏框架与游戏原理,采用游戏状态机、对象池、多线程、精灵、地图等技术,选取J2ME中的Midlet框架,通过程序优化及兼容性设计,开发出适用于单屏地图的移动网络游戏引擎。详细阐述了游戏菜单与游戏场景的设计,测试并分析了游戏运行性能。测试结果表明,基于Java的特性使该系统具有较高的可移植性,可在主流手机平台中正常运行。

无线传感器网络通用平台的研发

介绍了无线传感器网络的背景、基本概念及其广泛的研究价值和应用价值。结合无线传感器网络系统的主要问题和系统结构,说明了进行无线传感器网络系统仿真的意义;在分析开放源码网络仿真平台NS2特点、基本组成和体系结构的基础上,给出了系统仿真的基本流程和方法。针对无线传感器网络节点的主要问题和性能指标,给出了节点的设计思想和技术路线。实际应用表明,该无线传感器网络系统平台及其节点设计的合理性、实用性。

基于J2ME技术的手机网络游戏研究与开发

探讨了一种能将移动手机网络游戏管理平台和内容相互分离,并在应用中根据需要进行组合的通用移动网络游戏系统的设计。手机游戏开发基于J2ME技术基础,以牌类游戏为实例,采用了MVC框架模式进行设计,提高了系统的可维护性,有利于系统的二次开发;基于Java的特性使该系统具有较高的可移植性,可在主流手机平台中正常运行,减少移植成本。

路由优化对移动IPv6网络性能的影响

移动IPv6中引入路由优化的目的是缩短移动节点与通信节点之间的端到端延时。为了观察路由优化对网络性能产生的影响,采用NS-2网络模拟软件创建了较为复杂的移动IPv6网络环境,通过切换延时、分组丢失、端到端延时等典型的移动IPv6网络性能参数影响实验,经过对仿真结果的分析,提出了一个切换延时和分组丢失的计算公式,并指出,路由优化可能会导致增加切换延时和分组丢失。给出了一种对移动IPv6网络性能影响极小的路由优化策略。结果表明,基于路由优化对移动IPv6网络性能是有影响的。