面向遥感图像场景分类的LAG-MANet模型

遥感图像分类过程中,局部信息与全局信息至关重要。目前,遥感图像分类的方法主要包括卷积神经网络(CNN)及Transformer。CNN在局部信息提取方面具有优势,但在全局信息提取方面有一定的局限性。相比之下,Transformer在全局信息提取方面表现出色,但计算复杂度高。为提高遥感图像场景分类性能,降低复杂度,设计了LAG-MANet纯卷积网络。该网络既关注局部特征,又关注全局特征,并且考虑了多尺度特征。输入图像被预处理后,首先采用多分支扩张卷积模块(MBDConv)提取多尺度特征;然后依次进入网络的4个阶段,在每个阶段采用并行双域特征融合模块(P2DF)分支路提取局部、全局特征并进行融合;最后先经过全局平均池化、再经过全连接层输出分类标签。LAG-MANet在WHU-RS19数据集、SIRI-WHU数据集及RSSCN7数据集上的分类准确率分别为97.76%、97.04%、97.18%。试验结果表明,在3个具有挑战性的公开遥感数据集上,LAG-MANet更具有优越性。

Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and Friedman.It articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility patterns.The study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network scenarios.The findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network requirements.This paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

MANET中的时间敏感多流路由研究

MANET移动自组织网络中,节点持续移动,链路传输容量有限,容易出现链路过载现象。重点研究多数据流且网络拓扑高频变动情况下,基础路由技术导致链路过载而路由失效时,如何计算新的路由路径。解决链路过载问题的同时考虑路由时间敏感度高的要求,倾向选择连接生存期较长的路由,提出基于序列树的路径部署策略。和现有优化方法相比,有效缩短路径规划时长,快速高效计算路径的同时保证网络连接的稳定性,综合评估最优。

一种改进的MANET加权分簇算法

移动自组网(Mobile Ad Hoc Network, MANET)主要应用于军事活动、灾后救援等大规模的活动中,随着节点数的增加、移动速度的加快,网络拓扑变得更加复杂,网络稳定性和性能也随之下降。频繁的网络拓扑变化会导致簇结构变得不稳定并且控制开销也会增加。为了解决这一问题,提出了一种改进的加权分簇算法,通过仿真表明,该算法可以有效地提高大规模移动自组网的性能。

Deep Reinforcement Learning-Based Collaborative Routing Algorithm for Clustered MANETs

Flexible adaptation to differentiated quality of service(QoS)is quite important for future 6G network with a variety of services.Mobile ad hoc networks(MANETs)are able to provide flexible communication services to users through self-configuration and rapid deployment.However,the dynamic wireless environment,the limited resources,and complex QoS requirements have presented great challenges for network routing problems.Motivated by the development of artificial intelligence,a deep reinforcement learning-based collaborative routing(DRLCR)algorithm is proposed.Both routing policy and subchannel allocation are considered jointly,aiming at minimizing the end-to-end(E2E)delay and improving the network capacity.After sufficient training by the cluster head node,the Q-network can be synchronized to each member node to select the next hop based on local observation.Moreover,we improve the performance of training by considering historical observations,which can improve the adaptability of routing policies to dynamic environments.Simulation results show that the proposed DRLCR algorithm outperforms other algorithms in terms of resource utilization and E2E delay by optimizing network load to avoid congestion.In addition,the effectiveness of the routing policy in a dynamic environment is verified.

EsECC_SDN:Attack Detection and Classification Model for MANET

Mobile Ad Hoc Networks(MANET)is the framework for social networking with a realistic framework.In theMANETenvironment,based on the query,information is transmitted between the sender and receiver.In the MANET network,the nodes within the communication range are involved in data transmission.Even the nodes that lie outside of the communication range are involved in the transmission of relay messages.However,due to the openness and frequent mobility of nodes,they are subjected to the vast range of security threats inMANET.Hence,it is necessary to develop an appropriate security mechanism for the dataMANET environment for data transmission.This paper proposed a security framework for the MANET network signature escrow scheme.The proposed framework uses the centralised Software Defined Network(SDN)with an ECC cryptographic technique.The developed security framework is stated as Escrow Elliptical Curve Cryptography SDN(EsECC_SDN)for attack detection and classification.The developed EsECC-SDN was adopted in two stages for attack classification and detection:(1)to perform secure data transmission between nodes SDN performs encryption and decryption of the data;and(2)to detect and classifies the attack in theMANET hyper alert based HiddenMarkovModel Transductive Deep Learning.Furthermore,the EsECC_SDN is involved in the assignment of labels in the transmitted data in the database(DB).The escrow handles these processes,and attacks are evaluated using the hyper alert.The labels are assigned based on the k-medoids attack clustering through label assignment through a transductive deep learning model.The proposed model uses the CICIDS dataset for attack detection and classification.The developed framework EsECC_SDN’s performance is compared to that of other classifiers such as AdaBoost,Regression,and Decision Tree.The performance of the proposed EsECC_SDN exhibits∼3%improved performance compared with conventional techniques.

Enhanced Energy Efficient with a Trust Aware in MANET for Real-Time Applications

Mobile ad hoc networks (MANETs) are subjected to attack detectionfor transmitting and creating new messages or existing message modifications.The attacker on another node evaluates the forging activity in themessage directly or indirectly. Every node sends short packets in a MANETenvironment with its identifier, location on the map, and time through beacons.The attackers on the network broadcast the warning message usingfaked coordinates, providing the appearance of a network collision. Similarly,MANET degrades the channel utilization performance. Performancehighly affects network performance through security algorithms. This paperdeveloped a trust management technique called Enhanced Beacon TrustManagement with Hybrid Optimization (EBTM-Hyopt) for efficient clusterhead selection and malicious node detection. It tries to build trust amongconnected nodes and may improve security by requiring every participatingnode to develop and distribute genuine, accurate, and trustworthy materialacross the network. Specifically, optimized cluster head election is done periodicallyto reduce and balance the energy consumption to improve the lifetimenetwork. The cluster head election optimization is based on hybridizingParticle Swarm Optimization (PSO) and Gravitational Search OptimizationAlgorithm (GSOA) concepts to enable and ensure reliable routing. Simulationresults show that the proposed EBTM-HYOPT outperforms the state-of-thearttrust model in terms of 297.99 kbps of throughput, 46.34% of PDR, 13%of energy consumption, 165.6 kbps of packet loss, 67.49% of end-to-end delay,and 16.34% of packet length.

Optimized Resource Allocation and Queue Management for Traffic Control in MANET

A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks(MANETs).Offering better communication services among the users in a centralized organization is the primary objective of the MANET.Due to the features of MANET,this can directly End-to-End Delay(EED)the Quality of Service(QoS).Hence,the implementation of resource management becomes an essential issue in MANETs.This paper focuses on the efficient Resource Allocation(RA)for many types of Traffic Flows(TF)in MANET.In Mobile Ad hoc Networks environments,the main objective of Resource Allocation(RA)is to process consistently available resources among terminals required to address the service requirements of the users.These three categories improve performance metrics by varying transmission rates and simulation time.For solving that problem,the proposed work is divided into Queue Management(QM),Admission Control(AC)and RA.For effective QM,this paper develops a QM model for elastic(EL)and inelastic(IEL)Traffic Flows.This research paper presents an AC mechanism for multiple TF for effective AC.This work presents a Resource Allocation Using Tokens(RAUT)for various priority TF for effective RA.Here,nodes have three cycles which are:Non-Critical Section(NCS),Entry Section(ES)and Critical Section(CS).When a node requires any resources,it sends Resource Request Message(RRM)to the ES.Elastic and inelastic TF priority is determined using Fuzzy Logic(FL).The token holder selects the node from the inelastic queue with high priority for allocating the resources.Using Network Simulator-2(NS-2),simulations demonstrate that the proposed design increases Packet Delivery Ratio(PDR),decrease Packet Loss Ratio(PLR),minimise the Fairness and reduce the EED.

Intrusion Detection System Through Deep Learning in Routing MANET Networks

Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in promi-nence.For reasons including node mobility,due to MANET’s potential to provide small-cost solutions for real-world contact challenges,decentralized management,and restricted bandwidth,MANETs are more vulnerable to security threats.When protecting MANETs from attack,encryption and authentication schemes have their limits.However,deep learning(DL)approaches in intrusion detection sys-tems(IDS)can adapt to the changing environment of MANETs and allow a sys-tem to make intrusion decisions while learning about its mobility in the environment.IDSs are a secondary defiance system for mobile ad-hoc networks vs.attacks since they monitor network traffic and report anything unusual.Recently,many scientists have employed deep neural networks(DNNs)to address intrusion detection concerns.This paper used MANET to recognize com-plex patterns by focusing on security standards through efficiency determination and identifying malicious nodes,and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network(CBPNN),Feedforward-Neural-Network(FNN),and Cascading-Back-Propagation-Neural-Network(CBPNN)(FFNN).In addition to Convolutional-Neural-Network(CNN),these primary forms of deep neural network(DNN)building designs are widely used to improve the performance of intrusion detection systems(IDS)and the use of IDS in conjunction with machine learning(ML).Further-more,machine learning(ML)techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to differ-ent environments.Compared with another current model,The proposed model has better average receiving packet(ARP)and end-to-end(E2E)performance.The results have been obtained from CBP,FFNN and CNN 74%,82%and 85%,respectively,by the time(27,18,and 17 s).

A Novel Gradient Boosted Energy Optimization Model(GBEOM)for MANET

Mobile Ad Hoc Network(MANET)is an infrastructure-less network that is comprised of a set of nodes that move randomly.In MANET,the overall performance is improved through multipath multicast routing to achieve the quality of service(quality of service).In this,different nodes are involved in the information data collection and transmission to the destination nodes in the network.The different nodes are combined and presented to achieve energy-efficient data transmission and classification of the nodes.The route identification and routing are established based on the data broadcast by the network nodes.In transmitting the data packet,evaluating the data delivery ratio is necessary to achieve optimal data transmission in the network.Furthermore,energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery rate.In this paper,a Gradient-Based Energy Optimization model(GBEOM)for the route in MANET is proposed to achieve an improved data delivery rate.Initially,the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing(WMC-SMLB)technique is utilized for obtaining energy efficiency and load balancing routing.The WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in MANET.Load balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth estimation.Next,the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing efficiency.The proposed GBEOM exhibits∼4%improved performance in MANET routing.

Avoid Suspicious Route of Blackhole Nodes in MANET’s:Using A Cooperative Trapping

Mobile Ad hoc Network(MANET)is decentralized wireless network and can communicate without existing infrastructure in many areas.MANET is vulnerable to various attacks that affect its performance such as blackhole attack.Blackhole attacker,inject fault routing information to persuade the source node to select the path with malicious node as the shortest path.To eliminate malicious nodes from launching any collaborative attack.A cooperative Trapping Approach(CTA)was proposed based on modifying Ad-hoc On-demand Distance Vector(AODV)routing protocol and trapping the malicious nodes by responding to the trap request message.The approach aims to eliminate and rule out both single and collaborative malicious blackhole nodes from any attack.The approach realizes a backward tracking mechanism to perform the elimination process.The proposed algorithm(CTA)was executed using NS-2 network simulator.The performance metrics that has been considered to evaluate the performance of the proposed algorithm such as throughput,end to end delay,packet delivery ratio,and consuming energy.The experimental results have shown the performance metrics of the proposed approach outperformed other state of at algorithms.

Improving QoS Using Mobility-Based Optimized Multipath Routing Protocol in MANET

Mobile Ad-hoc Networks(MANETs)connect numerous nodes to communicate data from the sender node to the target node.Due to the lack of an infrastructure network,mobile nodes communicate through wireless without an access point.MANET does not have a centralized controller and has a dynamic network topology,which increases link failure and energy consumption resulting in excessive path delay,loss of Quality of service(QoS),and reduced throughput during data communication.Congestion is a significant problem when the QoS of the link carrying the data is degraded.Routing is one of the vital challenges of MANET due to the very dynamic and distributed nature of MANET.This article introduces a Mobility-Based Optimized Multipath Routing Protocol(MBOMRP)and an Efficient Reliable Link-State Transmission(ERLST)algorithm to overcome these problems.The proposed Mobility-Based Optimized Multipath Routing Protocol(MBOMRP)is utilized for route discovery and maintenance to efficiently avoid traffic and sleeping nodes.ERLST algorithm is used for efficient data transmission to increase QoS measurement parameters like throughput,Packet Delivery Ratio(PDR),and minimize the latency performance.The proposed MBOMRP-ERLST algorithm improves data communication network lifetime,avoids link failures,and provides efficient results compared with previous algorithms.

Region Centric GL Feature Approximation Based Secure Routing for Improved QoS in MANET

Secure routing in Mobile Adhoc Network(Manet)is the key issue now a day in providing secure access to different network services.As mobile devices are used in accessing different services,performing secure routing becomes a challenging task.Towards this,different approaches exist whichfind the trusted route based on their previous transmission details and behavior of different nodes.Also,the methods focused on trust measurement based on tiny information obtained from local nodes or with global information which are incomplete.How-ever,the adversary nodes are more capable and participate in each transmission not just to steal the data also to generate numerous threats in degrading QoS(Quality of Service)parameters like throughput,packet delivery ratio,and latency of the network.This encourages us in designing efficient routing scheme to max-imize QoS performance.To solve this issue,a two stage trust verification scheme and secure routing algorithm named GL-Trust(Global-Local-Trust)is presented.The method involves in route discovery as like popular AODV(Adaptive On-demand Distance Vector)which upgrades the protocol to collect other information like transmission supported,successful transmissions,energy,mobility,the num-ber of neighbors,and the number of alternate route to the same destination and so on.Further,the method would perform global trust approximation to measure the value of global trust and perform local trust approximation to measure local trust.Using both the measures,the method would select a optimal route to perform routing.The protocol is designed to perform localized route selection when there is a link failure which supports the achievement of higher QoS performance.By incorporating different features in measuring trust value towards secure routing,the proposed GL-Trust scheme improves the performance of secure routing as well as other QoS factors.

Design of Fuzzy Logic Control Framework for QoS Routing in MANET

Wireless networks with no infrastructure arise as a result of multiple wireless devices working together.The Mobile Ad hoc Network(MANET)is a system for connecting independently located Mobile Nodes(MNs)via wireless links.A MANET is self-configuring in telecommunications,while MN produces non-infrastructure networks that are entirely decentralized.Both the MAC and routing layers of MANETs take into account issues related to Quality of Service(QoS).When culling a line of optical discernment communication,MANET can be an effective and cost-saving route cull option.To maintain QoS,however,more or fewer challenges must be overcome.This paper proposes a Fuzzy Logic Control(FLC)methodology for specifying a probabilistic QoS guaranteed for MANETs.The framework uses network node mobility to establish the probabil-istic quality of service.Fuzzy Logic(FL)implementations were added to Network Simulator-3(NS-3)and used with the proposed FLC framework for simulation.Researchers have found that for a given node’s mobility,the path’s bandwidth decreases with time,hop count,and radius.It is resolutely based on this fuzzy rule that the priority index for a packet is determined.Also,by avoiding sending pack-ets(PKT)out of source networks when there are no beneficial routes,bandwidth is not wasted.The FLC outperforms the scheduling methods with a wide range of results.To improve QoS within MANETs,it is therefore recommended that FLC is used to synchronize packets.Thus,using these performance metrics,the QoS-responsible routing can opt for more stable paths.Based on network simulation,it is evident that incorporating QoS into routing protocols is meant to improve traf-fic performance,in particular authentic-time traffic.

An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization.The primary aim of MANETs is to extendflexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing system.An Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/violations.An IDS is a software program or hardware system that monitors network/security traffic for malicious attacks,sending out alerts whenever it detects malicious nodes.The impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research article.The Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET environment.MANET system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node trustworthiness.Furthermore,KF is used to eliminate synchronisation errors that arise during the sending and receiving data.In order to provide an energy-efficient solution and to minimize network traffic,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in nodes.According to the researchfindings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

Timer Entrenched Baited Scheme to Locate and Remove Attacks in MANET

The Mobile Ad-hoc Network(MANET)is a dynamic topology that provides a variety of executions in various disciplines.The most sticky topic in organizationalfields was MANET protection.MANET is helpless against various threats that affect its usability and accessibility.The dark opening assault is considered one of the most far-reaching dynamic assaults that deteriorate the organi-zation's execution and reliability by dropping all approaching packages via the noxious node.The Dark Opening Node aims to deceive any node in the company that wishes to connect to another node by pretending to get the most delicate ability to support the target node.Ad-hoc On-demand Distance Vector(AODV)is a responsive steering convention with no corporate techniques to locate and destroy the dark opening center.We improved AODV by incorporating a novel compact method for detecting and isolating lonely and collaborative black-hole threats that utilize clocks and baits.The recommended method allows MANET nodes to discover and segregate black-hole network nodes over dynamic changes in the network topology.We implement the suggested method's performance with the help of Network Simulator(NS)-3 simulation models.Furthermore,the proposed approach comes exceptionally near to the original AODV,absent black holes in terms of bandwidth,end-to-end latency,error rate,and delivery ratio.

Improved QoS-Secure Routing in MANET Using Real-Time Regional ME Feature Approximation

Mobile Ad-hoc Network(MANET)routing problems are thoroughly studied several approaches are identified in support of MANET.Improve the Quality of Service(QoS)performance of MANET is achieving higher performance.To reduce this drawback,this paper proposes a new secure routing algorithm based on real-time partial ME(Mobility,energy)approximation.The routing method RRME(Real-time Regional Mobility Energy)divides the whole network into several parts,and each node’s various characteristics like mobility and energy are randomly selected neighbors accordingly.It is done in the path discovery phase,estimated to identify and remove malicious nodes.In addition,Trusted Forwarding Factor(TFF)calculates the various nodes based on historical records and other characteristics of multiple nodes.Similarly,the calculated QoS Support Factor(QoSSF)calculating by the Data Forwarding Support(DFS),Throughput Support(TS),and Lifetime Maximization Support(LMS)to any given path.One route was found to implement the path of maximizing MANET QoS based on QoSSF value.Hence the proposed technique produces the QoS based on real-time regional ME feature approximation.The proposed simulation implementation is done by the Network Simulator version 2(NS2)tool to produce better performance than other methods.It achieved a throughput performance had 98.5%and a routing performance had 98.2%.

MANET中TCP改进研究综述

传统TCP(transmission control protocol)本是为有线网络设计,它假设包丢失全是由网络拥塞引起,这个假设不能适应于MANET (mobile ad hoc network),因为MANET 中除了拥塞丢包以外,还存在由于较高比特误码率、路由故障等因素引起的丢包现象.当出现非拥塞因素丢包时,传统 TCP 将错误地触发拥塞控制,从而引起TCP 性能低下.任何改进机制都可以分为发现问题和解决问题两个阶段.首先概括了 MANET 中影响 TCP 性能的若干问题;然后针对发现问题和解决问题两个阶段,详细地对每一阶段中存在的各种可行方法进行了分类、分析和比较;最后指出了 MANET 中 TCP 性能优化的研究方向.

一种基于机器学习的MANET网络入侵检测性能评估方法研究

移动Ad hoc网络(MANET,Mobile Ad hoc Networks)正得到越来越广泛的应用,相应的网络安全问题也开始得到广泛的关注。研究MANET网络可能遭遇的攻击方式,提出基于机器学习技术的入侵检测性能评估模型,并提出一个综合评价指标,比较了7种机器学习算法在MANET网络入侵检测中的性能表现,对于构建安全有效的MANET网络具有重要的意义。使用GloMoSim仿真工具对MANET网络正常行为及黑洞、洪水、丢包3种入侵行为进行模拟,并详细分析了各种攻击情况下,7种机器学习算法的性能表现。分析结果显示,该评估模型能较好地反映出各种机器学习算法的性能,其中,多层感知器、逻辑回归和支持向量机具有较高的检测率及较低的误报率。