MTCR-CR Routing Strategy for Connection-Oriented Routing over Satellite Networks

The high-speed movement of satellites makes it not feasible to directly apply the mature routing scheme on the ground to the satellite network.DT-DVTR in the snapshot-based connectionoriented routing strategy is one of the representative solutions,but it still has room for improvement in terms of routing stability.In this paper,we propose an improved scheme for connection-oriented routing strategy named the Minimal Topology Change Routing based on Collaborative Rules(MTCR-CR).The MTCR-CR uses continuous time static topology snapshots based on satellite status to search for intersatellite link(ISL)construction solutions that meet the minimum number of topology changes to avoid route oscillations.The simulation results in Beidou-3 show that compared with DT-DVTR,MTCR-CR reduces the number of routing changes by about 92%,the number of path changes caused by routing changes is about38%,and the rerouting time is reduced by approximately 47%.At the same time,in order to show our algorithm more comprehensively,the same experimental index test was also carried out on the Globalstar satellite constellation.

基于DSR理论-综合云理论的中俄冻土区管道保温层效果失效类型研究

冻土区管道保温层效果与评价指标变化的随机性密切相关,以往冻土区管道保温层效果评价模型存在一定的局限性与不足。为了提升冻土区管道保温层效果模型的应用效果,本文基于驱动力-状态-响应(Driving force-State-Response,DSR)理论、综合云理论和迭代运算方法相结合的综合评价方法,构建适用于中俄冻土区管道保温层效果的评价模型。为了验证冻土区管道保温层效果评价模型的科学性,以中俄管道沿线(漠河—大庆段)四处典型监测区域MDX007、MDX113、MDX304和MDX364为实例,应用评价模型对管道保温层效果的失效类型和失效可能性概率进行了综合分析。结果表明:运用评价模型可以科学指导中俄原油管道保温层效果的评估,并依据相应的保温层效果失效类型结果给出对应的措施建议。综合云模型能够结合不同理论模型的优势,保证冻土工程环境评价的科学性,通过置信度值验证,具有良好的应用前景。研究成果可为中俄原油管线安全稳定运营提供战略性的科学支撑。

Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and convergence.In this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain collaboration.Due to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward functions.Regarding the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the flows.In addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+MADDPG.Introducing a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network environment.The LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

QoS Routing Optimization Based on Deep Reinforcement Learning in SDN

To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQuality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanismsin accommodating such extensive data flows. In response to the imperative of handling a substantial influx of datarequests promptly and alleviating the constraints of existing technologies and network congestion, we present anarchitecture forQoS routing optimizationwith in SoftwareDefinedNetwork (SDN), leveraging deep reinforcementlearning. This innovative approach entails the separation of SDN control and transmission functionalities, centralizingcontrol over data forwardingwhile integrating deep reinforcement learning for informed routing decisions. Byfactoring in considerations such as delay, bandwidth, jitter rate, and packet loss rate, we design a reward function toguide theDeepDeterministic PolicyGradient (DDPG) algorithmin learning the optimal routing strategy to furnishsuperior QoS provision. In our empirical investigations, we juxtapose the performance of Deep ReinforcementLearning (DRL) against that of Shortest Path (SP) algorithms in terms of data packet transmission delay. Theexperimental simulation results show that our proposed algorithm has significant efficacy in reducing networkdelay and improving the overall transmission efficiency, which is superior to the traditional methods.

An SDN-Based Algorithm for Caching,Routing,and Load Balancing in ICN

One of the challenges of Informationcentric Networking(ICN)is finding the optimal location for caching content and processing users’requests.In this paper,we address this challenge by leveraging Software-defined Networking(SDN)for efficient ICN management.To achieve this,we formulate the problem as a mixed-integer nonlinear programming(MINLP)model,incorporating caching,routing,and load balancing decisions.We explore two distinct scenarios to tackle the problem.Firstly,we solve the problem in an offline mode using the GAMS environment,assuming a stable network state to demonstrate the superior performance of the cacheenabled network compared to non-cache networks.Subsequently,we investigate the problem in an online mode where the network state dynamically changes over time.Given the computational complexity associated with MINLP,we propose the software-defined caching,routing,and load balancing(SDCRL)algorithm as an efficient and scalable solution.Our evaluation demonstrates that the SDCRL algorithm significantly reduces computational time while maintaining results that closely resemble those achieved by GAMS.

Towards Blockchain-Based Secure BGP Routing,Challenges and Future Research Directions

Border Gateway Protocol(BGP)is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different destinations.The BGP protocol exhibits security design defects,such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes,easily triggering prefix hijacking,path forgery,route leakage,and other BGP security threats.Meanwhile,the traditional BGP security mechanism,relying on a public key infrastructure,faces issues like a single point of failure and a single point of trust.The decentralization,anti-tampering,and traceability advantages of blockchain offer new solution ideas for constructing secure and trusted inter-domain routing mechanisms.In this paper,we summarize the characteristics of BGP protocol in detail,sort out the BGP security threats and their causes.Additionally,we analyze the shortcomings of the traditional BGP security mechanism and comprehensively evaluate existing blockchain-based solutions to address the above problems and validate the reliability and effectiveness of blockchain-based BGP security methods in mitigating BGP security threats.Finally,we discuss the challenges posed by BGP security problems and outline prospects for future research.

Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management efficiency.Revolutionary advanced technology,such as Intelligent Transportation Systems(ITS),enables improved traffic management,helps eliminate congestion,and supports a safer environment.ITS provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road users.However,ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks(VANETs).This is because each vehicle plays the role of a router in this network,which leads to a complex vehicle communication network,causing issues such as repeated link breakages between vehicles resulting from the mobility of the network and rapid topological variation.This may lead to loss or delay in packet transmissions;this weakness can be exploited in routing attacks,such as black-hole and gray-hole attacks,that threaten the availability of ITS services.In this paper,a Blockchain-based smart contracts model is proposed to offer convenient and comprehensive security mechanisms,enhancing the trustworthiness between vehicles.Self-Classification Blockchain-Based Contracts(SCBC)and Voting-Classification Blockchain-Based Contracts(VCBC)are utilised in the proposed protocol.The results show that VCBC succeeds in attaining better results in PDR and TP performance even in the presence of Blackhole and Grayhole attacks.

Secure and Reliable Routing in the Internet of Vehicles Network:AODV-RL with BHA Attack Defense

Wireless technology is transforming the future of transportation through the development of the Internet of Vehicles(IoV).However,intricate security challenges are intertwinedwith technological progress:Vehicular ad hoc Networks(VANETs),a core component of IoV,face security issues,particularly the Black Hole Attack(BHA).This malicious attack disrupts the seamless flow of data and threatens the network’s overall reliability;also,BHA strategically disrupts communication pathways by dropping data packets from legitimate nodes altogether.Recognizing the importance of this challenge,we have introduced a new solution called ad hoc On-Demand Distance Vector-Reputation-based mechanism Local Outlier Factor(AODV-RL).The significance of AODVRL lies in its unique approach:it verifies and confirms the trustworthiness of network components,providing robust protection against BHA.An additional safety layer is established by implementing the Local Outlier Factor(LOF),which detects and addresses abnormal network behaviors.Rigorous testing of our solution has revealed its remarkable ability to enhance communication in VANETs.Specifically,Our experimental results achieve message delivery ratios of up to 94.25%andminimal packet loss ratios of just 0.297%.Based on our experimental results,the proposedmechanismsignificantly improves VANET communication reliability and security.These results promise a more secure and dependable future for IoV,capable of transforming transportation safety and efficiency.

A new quantum key distribution resource allocation and routing optimization scheme

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

Tunable Multichannel Quantum Routing of Single Photons Based on Single-Mode Microresonators

We have suggested a novel multiport quantum router of single photons with reflection feedback, which is formed by three waveguides coupled with four single-mode microresonators. The single-photon routing probabilities of four channels in the coupled system are studied theoretically by applying the real-space approach. Numerical results indicate that unidirectional routing in these output channels can be effectively implemented, and the router is tunable to route desired frequencies into the output ports, by varying the inter-resonator detunings via spinning resonator technology. Therefore, the proposed multichannel system can provide potential applications in optical quantum communication.

POWER-AWARE ROUTING BASED ON DSR FOR MOBILE AD HOC NETWORKS

Energy consumption is a crucial design concern in Mobile Ad hoc NETworks (MANETs) since nodes are powered by batteries with limited energy, whereas Dynamic Source Routing (DSR) protocol does not take the energy limitation of MANET nodes into account. This paper proposes an energy-saving routing algorithm based on DSR: Power Aware Routing protocol based on DSR (PAR-DSR). The design objective of PAR-DSR is to select energy-efficient paths. The main features of PAR-DSR are: (1) Nodes use the Signal Attenuation Rate (SAR) to conduct power control operations; (2) Minimum path cost as metric to balance the traffic and energy consumption of wireless nodes. The simulation results show that PAR-DSR can greatly reduce the energy consumption of MANET nodes. The average node lifetime of PAR-DSR is 50%-77% longer than that of DSR.

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.

Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.

基于DSR-AHP法的电网企业“双碳”审计风险评价指标体系及应用研究

电网企业是推动能源清洁低碳转型的中坚力量,在碳达峰、碳中和工作中扮演着重要角色,非常有必要对电网企业的“双碳”工作落实情况进行审计,从而更好地落实国家“双碳”政策,服务国民经济发展。本文基于电网企业“双碳”工作执行情况,创新性运用DSR模型和AHP法,提出“双碳”审计风险评价指标,构建三层结构23个评价指标体系,对电网企业“双碳”工作情况进行综合科学评价,对风险点进行详细分析,并以Z市电网企业为例进行详细说明,以期为电网企业“双碳”工作审计提供研究参考和借鉴。本文理论研究和整体框架同样适用于社会其他行业“双碳”工作审计,具有极强的推广性。

基于DSR-云模型的斜拉桥施工安全风险评估研究

针对斜拉桥施工安全建立一个有效、合理的评价指标体系。采用DSR模型建立一个以环境驱动力、指标状态、响应措施为闭环的指标体系;利用层次分析法与熵权法的重组得到综合权重,与云模型理论相结合,将得到的评价标准云、评价指标云、综合评价云的数字特征值生成云图;将此评价指标体系运用至真实案例中。结果表明:依据DSR模型所建的评价指标体系较为全面反映斜拉桥施工时会面临的风险;综合权重由层次分析法与熵权法结合得到更为合理、可信;云模型的参与将整个评价过程中的随机性与模糊性降低,使得评价结果更为客观。

Metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks

Recently,vehicular ad hoc networks(VANETs)finds applicability in different domains such as security,rescue operations,intelligent transportation systems(ITS),etc.VANET has unique features like high mobility,limited mobility patterns,adequate topologymodifications,and wireless communication.Despite the benefits of VANET,scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques.It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes.The main drawback of VANET network is the network unsteadiness that results in minimum lifetime.In order to avoid reduced network lifetime in VANET,this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization(EMCMHR-LM)in VANET.The presented EMCMHR-LM model involves the procedure of arranging clusters,cluster head(CH)selection,and route selection appropriate for VANETs.The presentedEMCMHR-LMmodel uses slime mold optimization based clustering(SMO-C)technique to group the vehicles into clusters.Besides,an enhanced wild horse optimization based multihop routing(EWHO-MHR)protocol by the optimization of network parameters.The presented EMCMHR-LMmodel is simulated usingNetwork Simulator(NS3)tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.

GRADE: Deep learning and garlic routing-based secure data sharing framework for IIoT beyond 5G

The rise of automation with Machine-Type Communication(MTC)holds great potential in developing Industrial Internet of Things(IIoT)-based applications such as smart cities,Intelligent Transportation Systems(ITS),supply chains,and smart industries without any human intervention.However,MTC has to cope with significant security challenges due to heterogeneous data,public network connectivity,and inadequate security mechanism.To overcome the aforementioned issues,we have proposed a blockchain and garlic-routing-based secure data exchange framework,i.e.,GRADE,which alleviates the security constraints and maintains the stable connection in MTC.First,the Long-Short-Term Memory(LSTM)-based Nadam optimizer efficiently predicts the class label,i.e.,malicious and non-malicious,and forwards the non-malicious data requests of MTC to the Garlic Routing(GR)network.The GR network assigns a unique ElGamal encrypted session tag to each machine partaking in MTC.Then,an Advanced Encryption Standard(AES)is applied to encrypt the MTC data requests.Further,the InterPlanetary File System(IPFS)-based blockchain is employed to store the machine's session tags,which increases the scalability of the proposed GRADE framework.Additionally,the proposed framework has utilized the indispensable benefits of the 6G network to enhance the network performance of MTC.Lastly,the proposed GRADE framework is evaluated against different performance metrics such as scalability,packet loss,accuracy,and compromised rate of the MTC data request.The results show that the GRADE framework outperforms the baseline methods in terms of accuracy,i.e.,98.9%,compromised rate,i.e.,18.5%,scalability,i.e.,47.2%,and packet loss ratio,i.e.,24.3%.

基于DSR的沥青砂浆粘弹特性研究

沥青砂浆是沥青混合料的重要组成部分,位于集料-沥青界面与粗集料之间,研究其粘弹特性对进一步分析沥青混合料粘弹力学特性具有重要意义。研究介绍了沥青砂浆组成比例的确定方法,沥青砂浆的制备方法,基于DSR开展了沥青砂浆粘弹特性的研究。结果表明:沥青砂浆复数模量主曲线平行性良好,沥青砂浆复数模量及储存模量均随频率增大而逐渐增大,损耗模量随着频率的增加先增加后减小,相位角随着频率的增加同样呈现先增加后减小的趋势,储存模量随着损耗模量的增加先增加后降低。

A comprehensive survey on Segment Routing Traffic Engineering

Traffic Engineering(TE)enables management of traffic in a manner that optimizes utilization of network resources in an efficient and balanced manner.However,existing TE solutions face issues relating to scalability and complexity.In recent years,Segment Routing(SR)has emerged as a promising source routing paradigm.As one of the most important applications of SR,Segment Routing Traffic Engineering(SR-TE),which enables a headend to steer traffic along specific paths represented as ordered lists of instructions called segment lists,has the capability to overcome the above challenges due to its flexibility and scalability.In this paper,we conduct a comprehensive survey on SR-TE.A thorough review of SR-TE architecture is provided in the first place,reviewing the core components and implementation of SR-TE such as SR Policy,Flexible Algorithm and SR-native algorithm.Strengths of SR-TE are also discussed,as well as its major challenges.Next,we dwell on the recent SR-TE researches on routing optimization with various intents,e.g.,optimization on link utilization,throughput,QoE(Quality of Experience)and energy consumption.Afterwards,node management for SR-TE are investigated,including SR node deployment and candidate node selection.Finally,we discuss the existing challenges of current research activities and propose several research directions worth of future exploration.

Opportunistic Routing with Multi-Channel Cooperative Neighbour Discovery

Due to the scattered nature of the network,data transmission in a dis-tributed Mobile Ad-hoc Network(MANET)consumes more energy resources(ER)than in a centralized network,resulting in a shorter network lifespan(NL).As a result,we build an Enhanced Opportunistic Routing(EORP)protocol architecture in order to address the issues raised before.This proposed routing protocol goal is to manage the routing cost by employing power,load,and delay to manage the routing energy consumption based on theflooding of control pack-ets from the target node.According to the goal of the proposed protocol techni-que,it is possible to manage the routing cost by applying power,load,and delay.The proposed technique also manage the routing energy consumption based on theflooding of control packets from the destination node in order to reduce the routing cost.Control packet exchange between the target and all the nodes,on the other hand,is capable of having an influence on the overall efficiency of the system.The EORP protocol and the Multi-channel Cooperative Neighbour Discovery(MCCND)protocol have been designed to detect the cooperative adja-cent nodes for each node in the routing route as part of the routing path discovery process,which occurs during control packet transmission.While control packet transmission is taking place during the routing path discovery process,the EORP protocol and the Multi-channel Cooperative Neighbour Discovery(MCCND)protocol have been designed to detect the cooperative adjacent nodes for each node in the routing.Also included is a simulation of these protocols in order to evaluate their performance across a wide range of packet speeds using Constant Bit Rate(CBR).When the packet rate of the CBR is 20 packets per second,the results reveal that the EORP-MCCND is 0.6 s quicker than the state-of-the-art protocols,according to thefindings.Assuming that the CBR packet rate is 20 packets per second,the EORP-MCCND achieves 0.6 s of End 2 End Delay,0.05 s of Routing Overhead Delay,120 s of Network Lifetime,and 20 J of Energy Consumption efficiency,which is much better than that of the state-of-the-art protocols.