Software Defined Networking Based On-Demand Routing Protocol in Vehicle Ad-Hoc Networks

This paper comes up with a SDN Based Vehicle Ad-Hoc On-Demand Routing Protocol(SVAO),which separates the data forwarding layer and network control layer,as in software defined networking(SDN),to enhance data transmission efficiency within vehicle ad-hoc networks(VANETs).The roadside service unit plays the role of local controller and is in charge of selecting vehicles to forward packets within a road segment.All the vehicles state in the road.Correspondingly,a two-level design is used.The global level is distributed and adopts a ranked query scheme to collect vehicle information and determine the road segments along which a message should be forwarded.On the other hand,the local level is in charge of selecting forwarding vehicles in each road segment determined by the global level.We implement two routing algorithms of SVAO,and compare their performance in our simulation.We compare SVAO with popular ad-hoc network routing protocols,including Optimized Link State Routing(OLSR),Dynamic Source Routing(DSR),Destination Sequence Distance Vector(DSDV),and distance-based routing protocol(DB)via simulations.We consider the impact of vehicle density,speed on data transmission rate and average packet delay.The simulation results show that SVAO performs better than the others in large-scale networks or with high vehicle speeds.

Route maintenance of on-demand routing protocol in mobile ad hoc networks

In mobile ad hoc networks （MANETs）, if a mnltihop route breaks, route maintenance is typically performed to establish a new route which causes high control overhead and long packet delay. To decrease the time of recovery and the overhead of maintenance, a new route maintenance mechanism in which the maintenance is confined to the vicinity of the broken link is proposed. This mechanism patches broken route through some other nodes in which only nodes near the broken link（s） may need to be substituted and the rest of nodes can be retained on the route. The broken link can be repaired without considering its relative position in the whole path, so the abihty of dealing with hnk failure is improved obviously. Simulation results show that the proposed strategy improves network performance significantly, such as in merits of control overhead and packet delay.

Comparative Review of QoS-Aware On-Demand Routing in Ad Hoc Wireless Networks

In this paper, a representative set of QoS models and QoS-aware on-demand routing protocols are reviewed with emphasis on their ability to support QoS in mobile ad-hoc networks (MANETs) possibly used in WSNs. In particular IntServ, DiffServ, FQMM, and SWAN QoS models are reviewed followed by different QoS-aware on-demand routings in MANETs from different perspectives such as the challenges, classifications, algorithmic aspects in QoS provisions. Tradeoff in providing support to real time (RT) and best effort (BE) traffic is highlighted. Finally, a detailed and comprehensive comparison table is provided for better understanding of QoS provision in MANETs.

Ants-Based On-Demand Routing Algorithm for Mobile Ad Hoc Networks

An ants-based on-demand routing algorithm （AORA） specialized for mobile ad hoc networks is proposed. AORA measures the network＇s traffic information including delivery time, route energy etc. by the continuous delivery of data packets, then calculates the compositive parameter for each route which can be seen as the stigmity and uses it to choose the comparatively optimal route in real time. To adjust the weight of each traffic information, the algorithm can meet the different demand of the network＇s user. Multipath source self repair routing （MSSRR） algorithm and dynamic source routing （DSR） can be seen as the special samples of AORA. The routing overhead is not increased in this algorithm. By using simulation, it can be seen that the performance of AORA is better than that of DSR in all scenarios obviously, especially the delivery fraction is increased by more than 100 96.

Topology Control for Ad-Hoc Networks: A Comprehensive Review for Table Driven and On-Demand Routing Protocols

Routing on ad-hoc network has become a major research issue among the networking communities due to its increasing complexity and the surge of challenging problems. One major factor contributing to this tendency is that every terminal of an ad-hoc network is also functioning as a network router. In this paper we provide a comprehensive review about the principles and mechanisms of routing protocols used in ad-hoc networks. For comparison purposes, we discuss some relevant technical issues of two well-known routing strategies, namely On-Demand (Proactive routing) and Table-Driven (Reactive routing). In particular, focus our attention on two major and well-known routing protocols: AODV (Ad-hoc On-Demand Distance Vector Protocol) and OLSR (Optimized Link State Routing Protocol). Our study has no intention to suggest any definite solution for any ad-hoc network, because it is the case depending on dictated by the nature and varying factors of networks. Instead, we demonstrate our major perception and describe general models that may assist us while modeling a given network.

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.

CoopAI-Route: DRL Empowered Multi-Agent Cooperative System for Efficient QoS-Aware Routing for Network Slicing in Multi-Domain SDN

The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale.Network slicing is crucial in delivering services for different,demanding vertical applications in this context.Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations.However,the existing IP(Internet Protocol)over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators.Conventional inter-domain routing methods,like Border Gateway Protocol(BGP),cannot make routing decisions based on performance,which frequently results in traffic flowing across congested paths that are never optimal.To address these issues,we propose CoopAI-Route,a multi-agent cooperative deep reinforcement learning(DRL)system utilizing hierarchical software-defined networks(SDN).This framework enforces network slicing in multi-domain networks and cooperative communication with various administrators to find performance-based routes in intra-and inter-domain.CoopAI-Route employs the Distributed Global Topology(DGT)algorithm to define inter-domain Quality of Service(QoS)paths.CoopAI-Route uses a DRL agent with a message-passing multi-agent Twin-Delayed Deep Deterministic Policy Gradient method to ensure optimal end-to-end routes adapted to the specific requirements of network slicing applications.Our evaluation demonstrates CoopAI-Route’s commendable performance in scalability,link failure handling,and adaptability to evolving topologies compared to state-of-the-art methods.

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.

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.

Improving Network Availability through Optimized Multipath Routing and Incremental Deployment Strategies

Currently,distributed routing protocols are constrained by offering a single path between any pair of nodes,thereby limiting the potential throughput and overall network performance.This approach not only restricts the flow of data but also makes the network susceptible to failures in case the primary path is disrupted.In contrast,routing protocols that leverage multiple paths within the network offer a more resilient and efficient solution.Multipath routing,as a fundamental concept,surpasses the limitations of traditional shortest path first protocols.It not only redirects traffic to unused resources,effectively mitigating network congestion,but also ensures load balancing across the network.This optimization significantly improves network utilization and boosts the overall performance,making it a widely recognized efficient method for enhancing network reliability.To further strengthen network resilience against failures,we introduce a routing scheme known as Multiple Nodes with at least Two Choices(MNTC).This innovative approach aims to significantly enhance network availability by providing each node with at least two routing choices.By doing so,it not only reduces the dependency on a single path but also creates redundant paths that can be utilized in case of failures,thereby enhancing the overall resilience of the network.To ensure the optimal placement of nodes,we propose three incremental deployment algorithms.These algorithms carefully select the most suitable set of nodes for deployment,taking into account various factors such as node connectivity,traffic patterns,and network topology.By deployingMNTCon a carefully chosen set of nodes,we can significantly enhance network reliability without the need for a complete overhaul of the existing infrastructure.We have conducted extensive evaluations of MNTC in diverse topological spaces,demonstrating its effectiveness in maintaining high network availability with minimal path stretch.The results are impressive,showing that even when implemented on just 60%of nodes,our incremental deployment method significantly boosts network availability.This underscores the potential of MNTC in enhancing network resilience and performance,making it a viable solution for modern networks facing increasing demands and complexities.The algorithms OSPF,TBFH,DC and LFC perform fast rerouting based on strict conditions,while MNTC is not restricted by these conditions.In five real network topologies,the average network availability ofMNTCis improved by 14.68%,6.28%,4.76%and 2.84%,respectively,compared with OSPF,TBFH,DC and LFC.

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.

Node Sociability Based Intelligent Routing for Post-Disaster Emergency Networks

In a post-disaster environment characterized by frequent interruptions in communication links,traditional wireless communication networks are ineffective.Although the“store-carry-forward”mechanism characteristic of Delay Tolerant Networks(DTNs)can transmit data from Internet of things devices to more reliable base stations or data centres,it also suffers from inefficient data transmission and excessive transmission delays.To address these challenges,we propose an intelligent routing strategy based on node sociability for post-disaster emergency network scenarios.First,we introduce an intelligent routing strategy based on node intimacy,which selects more suitable relay nodes and assigns the corresponding number of message copies based on comprehensive utility values.Second,we present an intelligent routing strategy based on geographical location of nodes to forward message replicas secondarily based on transmission utility values.Finally,experiments demonstrate the effectiveness of our proposed algorithm in terms of message delivery rate,network cost ratio and average transmission delay.

A Traffic-Aware and Cluster-Based Energy Efficient Routing Protocol for IoT-Assisted WSNs

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart agriculture.Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes.This paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks.The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies.The protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base station.The performance of the proposed protocol is evaluated through the network simulator NS3.40.For different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network lifetime.For 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.

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.

In-Depth Study of Potential-Based Routing and New Exploration of Its Scheduling Integration

Industrial wireless mesh networks(WMNs)have been widely deployed in various industrial sectors,providing services such as manufacturing process monitoring,equipment control,and sensor data collection.A notable characteristic of industrial WMNs is their distinct traffic pattern,where the majority of traffic flows originate from mesh nodes and are directed towards mesh gateways.In this context,this paper adopts and revisits a routing algorithm known as ALFA(autonomous load-balancing field-based anycast routing),tailored specifically for anycast(one-to-one-of-many)networking in WMNs,where traffic flows can be served through any one of multiple gateways.In essence,the scheme is a hybrid-type routing strategy that leverages the advantages of both back-pressure routing and geographic routing.Notably,its novelty lies in being developed by drawing inspiration from another field,specifically from the movement of charges in an electrostatic potential field.Expanding on the previous work,this paper explores further in-depth discussions that were not previously described,including a detailed description of the analogy between an electrostatic system and a WMN system based on precise mapping perspectives derived from intensive analysis,as well as discussions on anycast,numerical methods employed in devising the ALFA scheme,its characteristics,and complexity.It is worth noting that this paper addresses these previously unexplored aspects,representing significant contributions compared to previous works.As a completely new exploration,a new scheduling strategy is proposed that is compatible with the routing approach by utilizing the potential-based metric not only in routing but also in scheduling.This assigns higher medium access priority to links with a larger potential difference.Extensive simulation results demonstrate the superior performance of the proposed potential-based joint routing and scheduling scheme across various aspects within industrial WMN scenarios.

Segment routing for traffic engineering and effective recovery in low-earth orbit satellite constellations

Low-Earth Orbit Satellite Constellations(LEO-SCs)provide global,high-speed,and low latency Internet access services,which bridges the digital divide in the remote areas.As inter-satellite links are not supported in initial deployment(i.e.the Starlink),the communication between satellites is based on ground stations with radio frequency signals.Due to the rapid movement of satellites,this hybrid topology of LEO-SCs and ground stations is time-varying,which imposes a major challenge to uninterrupted service provisioning and network management.In this paper,we focus on solving two notable problems in such a ground station-assisted LEO-SC topology,i.e.,traffic engineering and fast reroute,to guarantee that the packets are forwarded in a balanced and uninterrupted manner.Specifically,we employ segment routing to support the arbitrary path routing in LEO-SCs.To solve the traffic engineering problem,we proposed two source routings with traffic splitting algorithms,Delay-Bounded Traffic Splitting(DBTS)and DBTS+,where DBTS equally splits a flow and DBTS+favors shorter paths.Simu-lation results show that DBTS+can achieve about 30%lower maximum satellite load at the cost of about 10%more delay.To guarantee the fast recovery of failures,two fast reroute mechanisms,Loop-Free Alternate(LFA)and LFA+,are studied,where LFA pre-computes an alternate next-hop as a backup while LFA+finds a 2-segment backup path.We show that LFA+can increase the percentage of protection coverage by about 15%.

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.