Efficient Network Routing Research Articles

ER-OCN: Toward efficient network routing in ocean city based on deep reinforcement learning

Smart ocean cities are crucial to future city development, and offshore networks are an essential part of smart ocean cities. Nowadays, communication in offshore networks is becoming increasingly important. However, the offshore networks are highly heterogeneous, with low reliability, narrow bandwidth, and dramatic dynamics. Offshore networks are more challenging to manage than traditional terrestrial networks, and the standard quality of service routing faces significant challenges. Currently, artificial intelligence provides a way to meet the requirements. This paper proposes ER-OCN (Efficient Routing in Ocean City Networks), a two-stage routing optimization scheme driven by artificial intelligence to cope with the complex offshore networks environment. The first stage will calculate the flow paths based on Lagrangian relaxation. Besides, the ocean environment is under dramatic dynamics, leading to unpredictable network topology or link bandwidth changes. Thus, the second stage deployed a deep reinforcement learning algorithm to monitor the environment in real-time and update the routing strategy when needed. We conducted experiments based on actual network topology and traffic data sets of offshore platforms’ network systems. The results prove that our scheme improves communication cost performance by 36.61% compared to traditional methods.

Bridging theory and application: An in-depth study of the greatest common divisor and least common multiple in mathematics and real-world settings

The study of the Least Common Multiple (LCM) and the Greatest Common Divisor (GCD) explores the fundamental field of number theory. In addition to being essential to theoretical number theory, these mathematical ideas have extensive applications in computer science, algebra, and real-world settings like network routing and scheduling. Using techniques like the Euclidean algorithm for GCD calculation and prime factorization for LCM, the study starts by examining the fundamental concepts and notations of GCD and LCM. Then the author goes further into the detail about how these ideas are used to polynomial equations, highlighting how they may be used to solve challenging issues and simplify algebraic expressions. The studys practical applications, which demonstrate how GCD and LCM optimize scheduling tasks and boost network routing efficiency, are presented in the conclusion. The results of this study highlight the broad impact and adaptability of LCM and GCD in theoretical and applied mathematics, providing valuable insights for further research in these domains.

Intelligent edge network routing architecture with blockchain for the IoT

The demand for the Internet of Everything has slowed down network routing efficiency. Traditional routing policies rely on manual configuration, which has limitations and adversely affects network performance. In this paper, we propose an Internet of Things (IoT) Intelligent Edge Network Routing (ENIR) architecture. ENIR uses deep reinforcement learning (DRL) to simulate human learning of empirical knowledge and an intelligent routing closed-loop control mechanism for real-time interaction with the network environment. According to the network demand and environmental conditions, the method can dynamically adjust network resources and perform intelligent routing optimization. It uses blockchain technology to share network knowledge and global optimization of network routing. The intelligent routing method uses the deep deterministic policy gradient (DDPG) algorithm. Our simulation results show that ENIR provides significantly better link utilization and transmission delay performance than various routing methods (e.g., open shortest path first, routing based on Q-learning and DRL-based control framework for traffic engineering).

Should drivers cooperate? Performance evaluation of cooperative navigation on simulated road networks using network DEA

For drivers, traffic congestion causes enormous delays and increases energy consumption. For a city’s traffic management team, traffic congestion challenges economic growth and increases pollution. Many drivers rely on a GPS navigation system to choose the fastest route to reach their destination. The GPS algorithms generally work for a single driver, while ignoring their collective resulting impact on the road network. If there exists a cooperative centralised routing algorithm that minimises the whole network congestion, the city can greatly benefit from congestion reduction, though some drivers may suffer from such a cooperative routing algorithm. These drivers may opt-out from the cooperative centralised routing, which, in turn, impacts the whole network routing efficiency. So, does having higher proportions of cooperative drivers on the road network always work better? In this paper, we take a heuristic cooperative routing algorithm which minimises the network congestion. We simulate a road network and measure its performance under various proportions of cooperative drivers. We measure both the congestion and the average drivers’ perceived road network performance. Network data envelopment analysis (network DEA) technique is used to see which proportions of cooperative drivers can best benefit the city and drivers collectively.

A Novel Energy Efficient Network Routing Protocol for Lifetime Improvements in Wireless Sensor Networks

A Novel Energy Efficient Network Routing Protocol for Lifetime Improvements in Wireless Sensor Networks

Increased network routing efficiency through coordinated Fibbing

SummaryFor finding the best route in a network, distributed routing offers robustness but has poor flexibility while central control of software‐defined networking is just the opposite. The Fibbing architecture can run distributed routing protocols on software‐defined networking and has both robustness and flexibility. The 2 main steps of Fibbing's process are (1) compute a route that is available for the network according to the network topology and the flow request and (2) add fake nodes and fake links to augment the network topology in conformity with the distributed routing protocol and the route computed in the first step. Both of the 2 steps affect the performance of the network, but Fibbing does not consider them coordinately, and the cost of choosing the routes can be reduced further. In this paper, a coordinated algorithm for Fibbing is proposed to determine a lowest‐cost route. In the process to calculate an available route, our algorithm accommodates not only the network topology and the flow request but also the fake nodes and the fake links. The experiments on random topologies and classic topologies show that our algorithm can reduce the numbers of the fake nodes and the costs of the chosen routes, with the improved flow request acceptance ratios achieved.

Application-aware network: network route management using SDN based on application characteristics

Software-Defined Network (SDN) and OpenFlow enable more granular network route management. SDN-assisted routing has become a prominent technique for improving network routing efficiency. A standard approach in SDN-assisted routing involves optimizing the routing strategy for a targeted class of applications or a certain situation. However, many such approaches also require client-side modifications. In this paper, application-aware routing is proposed to address these issues. A network with application-aware routing is called an application-aware network. Application-aware routing “routes” each application individually by aligning the application’s communication preferences and paths with corresponding properties. This method allows multiple routing strategies to be applied simultaneously, effectively expanding the range of applications the network can support. Overseer, an OpenFlow controller for implementing an application-aware network, was developed as a reference implementation. The applicability of an application-aware network was validated by evaluating the feasibility and practicality of the technology. Feasibility was evaluated by comparing the performance of an application-aware network and a traditional network in critical situations. Practicality was evaluated by testing an application-aware network with a real-world application and a wide-area network. This paper also discusses some issues with application-aware networks and presents possible mitigations for these issues. Based on this work, application-aware routing is a promising technology for improved network routing efficiency.

Using Inspiration from Synaptic Plasticity Rules to Optimize Traffic Flow in Distributed Engineered Networks.

Controlling the flow and routing of data is a fundamental problem in many distributed networks, including transportation systems, integrated circuits, and the Internet. In the brain, synaptic plasticity rules have been discovered that regulate network activity in response to environmental inputs, which enable circuits to be stable yet flexible. Here, we develop a new neuro-inspired model for network flow control that depends only on modifying edge weights in an activity-dependent manner. We show how two fundamental plasticity rules, long-term potentiation and long-term depression, can be cast as a distributed gradient descent algorithm for regulating traffic flow in engineered networks. We then characterize, both by simulation and analytically, how different forms of edge-weight-update rules affect network routing efficiency and robustness. We find a close correspondence between certain classes of synaptic weight update rules derived experimentally in the brain and rules commonly used in engineering, suggesting common principles to both.

The critical role of structural hole in forming trust for Securing Wireless Sensor Networks

The security issues in wireless sensor networks (WSNs) are crucial and the limitation of computing resources and distributed properties of WSN make the traditional security mechanisms no longer feasible in protecting WSN as in the wired networks. There is an emerging research area i.e., trust and reputation mechanisms, has been developed, as an alternative solution to tackle the security issues in WSNs. While the recent studies on trust are mainly focusing on the trust modelling so as to effectively detect and avoid malicious activities. In this paper, we propose to use adaptive network approach to investigate the interplay between the network entities' trust behaviour and their underlying topological connectivity. We have found that the structural hole has its unique position and efficiency on connecting different regional sub-networks, but on the other side, from the security perspective, it has risks of fragile in the network. The extensive simulation studies have confirmed that structure hole plays a critical role on both for securing WSNs and also enable network routing efficiency.

Intelligent QoS routing algorithm based on improved AODV protocol for Ad Hoc networks

Mobile Ad Hoc Networks were playing an increasingly important part in disaster reliefs, military battlefields and scientific explorations. However, networks routing difficulties are more and more outstanding due to inherent structures. This paper proposed an improved cuckoo searching-based Ad hoc On-Demand Distance Vector Routing protocol (CSAODV). It elaborately designs the calculation methods of optimal routing algorithm used by protocol and transmission mechanism of communication-package. In calculation of optimal routing algorithm by CS Algorithm, by increasing QoS constraint, the found optimal routing algorithm can conform to the requirements of specified bandwidth and time delay, and a certain balance can be obtained among computation spending, bandwidth and time delay. Take advantage of NS2 simulation software to take performance test on protocol in three circumstances and validate the feasibility and validity of CSAODV protocol. In results, CSAODV routing protocol is more adapt to the change of network topological structure than AODV protocol, which improves package delivery fraction of protocol effectively, reduce the transmission time delay of network, reduce the extra burden to network brought by controlling information, and improve the routing efficiency of network.

An Improved Decision Strategy of Network Routing Based on Ant Colony Algorithm

-Aiming at getting a high efficient network routing decision strategy, to settle problems of slow convergence speed and easily to fall into local optimal, this paper proposes a new decision strategy based on ant colony algorithm. Memory device to record each time pheromone value and pheromone differences value of the adjacent times to decide follow the former route or find a new one are the focus of this paper. The new decision technology accelerates the convergence rate, improves network utilization rate and accuracy of network routing.

New Variants of Ant Colony Optimization for Network Routing

This paper suggests new variants of Ant Colony Optimization(ACO)Techniques for Network Routing. There are three existing variants of ACO based on pheromone deposit calculation. In our earlier work we suggested three different heuristics for selecting the next node at each step of iteration. Incorporation of these heuristics in each of the above three variants result into nine variations. In this paper the performance of these nine variations has been studied. Moreover, we have modified the pheromone deposit calculation considering the transmission time of each successful packet(ant) and incorporated this new pheromone update formula in each of the nine variants. As a result, we have obtained nine new variants of ACO. The performance of these new nine variants has been compared with previous ones with respect to the speed of execution, throughput and the number of successful packets. The experiments have been performed over two different network topologies. In one of the variant a tabu list has been incorporated. The length of the tabu list plays a vital role in improving the performance of the routing algorithm. In this paper it has been observed that the new variations of ACO have outperformed the previous ones. These new variants can perform efficient network routing in an environment having variable transmission time along the paths due to congestion or poor link quality.

A Hill-Area-Restricted Geographic Routing Protocol for Mobile Ad Hoc and Sensor Networks

In mobile ad hoc and sensor networks, greedy-face-greedy (GFG) geographic routing protocols have been a topic of active research in recent years. Greedy routing is applied first and may fail at concave nodes, in which case, face routing is applied to recover from the greedy fail. This may cause extra hops in routing and decrease the routing efficiency in networks containing voids. In this paper, we propose a Hill-Area-Restricted (HAR) routing protocol. It avoids the extra hops as in the original GFG routing, which makes it more efficient in hop count. Compared with the previous work of Node Elevation Ad hoc Routing (NEAR), the proposed HAR guarantees the packet delivery and decreases the communication cost largely. Our simulation results show that the communication cost in bytes of all messages sent by each node in the proposed HAR is 99.2% less than that in NEAR. This makes the HAR more desirable for mobile ad hoc and sensor networks.

Multicast delivery using explicit multicast over IPv6 networks

This article presents an alternative scheme, called Xcast6+, which is an extension of explicit multicast (Xcast) for an efficient delivery of multicast datagrams over IPv6 networks. The mechanism incorporates MLDv2 and a new control plane into existing Xcast6 (Xcast for IPv6) and not only does it provide the transparency of traditional multicast schemes to sources and recipients, but it also enhances the routing efficiency in networks. Since intermediate routers do not have to maintain any multicast states, it results in a more efficient and scalable mechanism to deliver traditional multicast datagrams. Furthermore, the seamless integration of Xcast6+ in Mobile IPv6 can support multicast efficiently for mobile nodes over IPv6 networks by avoiding tunnel avalanches and tunnel convergence. Our simulation results show distinct performance improvements of our approach. This approach can reduce network resources in many medium size groups multicast, particularly as the number of recipients in a subnet increases (i.e., subnet-dense groups).

Explicit Multicast Extension (Xcast+) for Efficient Multicast Packet Delivery

In this letter, we propose a new multicast scheme, named Xcast+, which is an extension of Explicit Multicast (Xcast) for an efficient delivery of multicast packets. The mechanism incorporates the host group model and a new control plane into existing Xcast, and not only does it provide the transparency of traditional multicast schemes to senders and receivers, but it also enhances the routing efficiency in networks. Since intermediate routers do not have to maintain any multicast states, it results in a more efficient and scalable mechanism to deliver multicast packets. Our simulation results show distinct performance improvements of our approach compared to Xcast, particularly as the number of receivers in a subnet increases.