Routing Optimization In Networks Research Articles

Hybrid Salp Swarm and Improved Whale Optimization Algorithm‐based clustering scheme for improving network lifespan in wireless sensor networks

SummaryWireless sensor networks (WSNs) represent the collection of restricted energy sensor nodes that are deployed in an area of target for gathering potential environment data for decision‐making with respect to their objective of application. However, the implementation of energy‐effective data gathering strategies in large‐scale WSNs is the most challenging due to the limited energy resources. Clustering‐based data gathering strategies are identified to be quite effective for energy saving that directly attributes to extended network lifetime. Moreover, optimal path amid the cluster head (CH) and sink node needs to be selected for sustaining energy efficiency and improving network lifespan. In this article, Hybrid Salp Swarm and Improved Whale Optimization Algorithm (HSSIWOA)‐based clustering scheme is proposed for improving the network lifetime and routing optimization with maximized energy efficacy. It integrated the exploration capability of Salp Swarm Optimization Algorithm (SSOA) with exploitation benefits of Improved Whale Optimization Algorithm (IWOA) for balancing the trade‐off between the rate of exploration and exploitation during CH selection process. It utilized the parameters of residual energy, load balance, intra‐cluster distance, inter‐cluster distance, and node centrality into account during the process of fitness evaluation. It performed well by constructing an optimized number of clusters, such that energy stability and network lifetime are maintained in the network. The experimental results of the proposed HSSIWOA scheme confirmed extended network lifetime of 21.64%, minimized energy utilization of 23.42%, and maximized throughput of 18.56%, better than the baseline approaches.

Joint Network Function Placement and Routing Optimization in Dynamic Software-Defined Satellite-Terrestrial Integrated Networks

Joint Network Function Placement and Routing Optimization in Dynamic Software-Defined Satellite-Terrestrial Integrated Networks

Dynamic routing optimization in software-defined networking based on a metaheuristic algorithm

Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

Network Routing Optimization Using Tabu Search Algorithm in Dynamic Routing

Internet penetration is increasing along with the need for data packages for communication such as social media, chatting, video conferencing and others. On large-scale networks such as the Internet, dynamic routing is used to build routing protocol information in the routing table automatically. Currently, Djikstra's algorithm is used to solve the shortest path problem in dynamic routing. In this research, the optimization of the algorithm is carried out in determining the best path or trajectory. One of the optimization algorithms is the Tabu Search Algorithm which can guide heuristic local search procedures to explore the solution area outside the local optimum point. This optimization is assessed from the test parameters measured from the smallest cost. The data analyzed is in the form of bandwidth and topological flow. From the results of tracing the path of data packets sent through 9 routers using the Tabu Search algorithm with the parameters namely number of Neighbor Solutions = 50, Length of tabu list = 10, Maximum Number of Iterations = 100, the result of the path matrix value is 180.9676. The path taken is router 0-2-4-8-9

Wireless sensor network routing optimization based on improved ant colony algorithm in the Internet of Things

The Internet of Things (IoT) connects devices, enabling real-time data acquisition, automation, and collaboration. Wireless sensor networks are one of the important components of the Internet of Things, consisting of many wireless sensor nodes distributed in space. These nodes can perceive environmental information and transmit it to other nodes through wireless communication. In wireless sensor network routing optimization methods, improved ant colony algorithm can be used to find the optimal routing scheme. Ant colony algorithm simulates the behavior of ants in the process of searching for food, and optimizes factors such as transfer probability and pheromone concentration to enable ants to find the shortest path. In wireless sensor networks, node positions can be used as reference nodes and anchor nodes, combined with the objective function of wireless sensor network routing optimization, and improved ant colony algorithm can be used to solve the optimal path, thus obtaining the optimal wireless sensor network routing optimization scheme. Through experimental results, it can be found that the proposed method performs well in terms of energy consumption, transmission delay, number of dead nodes, and network throughput. These optimization results have positive implications for the sustainable development and practical application of the Internet of Things, which can improve the development of the digital economy and enhance the construction of smart cities.

Jaya clustering-based algorithm for multiobjective IoV network routing optimization

Jaya clustering-based algorithm for multiobjective IoV network routing optimization

Fuzzy clustering and optimal routing in wireless sensor network

A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively.

Optimal sensor network routing with secure network monitoring using deep learning architectures

Optimal sensor network routing with secure network monitoring using deep learning architectures

Multi-airport System Route Network Optimization Based on Differentiated Positioning

Air routes in China’s existing multi-airport system are unreasonable and homogeneous, leading to a significant gap in utilization between different airports and a waste of airport resources. One differentiated positioning model is proposed to optimize the air route network to solve the above problems. Balanced development of airports and passenger costs are considered in the model. Finally, the model is validated using the 2019 route network dataset in the Beijing-Tianjin-Hebei multi-airport system (BTH MAS). The optimization results for the multi-airport system show an average increase of 15% in airports utilization, an average increase of 6% in the differentiated realization rate, an 8% reduction in the average cost of passenger travel fees, and a 17% reduction in route repetition with no reduction in the number of connections to destination airports.

Multisource–Multidestination Optimal Energy Routing in Static and Time-Varying Vehicular Energy Network

A vehicular energy network (VEN) uses electric vehicles (EVs) to transport energy across a wide geographical area. EVs can charge and discharge wirelessly at road junctions while moving and, thus, transfer energy between junctions. In this article, we propose a method to optimally route energy from multiple energy-supplying junctions to multiple energy-demanding junctions while minimizing energy loss in four different VEN scenarios: 1) a time-invariant VEN; 2) a VEN with time-varying vehicular flows and energy demands; 3) a VEN with time-varying vehicular flows and time-averaged demands; and 4) a VEN with significant storage capacities at junctions. The method employed to solve these scenarios is to model the VEN as a graph specific to each scenario, and then solve the generalized flow problem on the graph. Simulations are performed on all four scenarios and show that transmission efficiency improves when a small number of long vehicular routes are introduced, and when storage capacities at junctions increase. The error in approximating a time-varying VEN as a static VEN is also investigated.

A Routing Optimization Method for Software-Defined Optical Transport Networks Based on Ensembles and Reinforcement Learning

Optical transport networks (OTNs) are widely used in backbone- and metro-area transmission networks to increase network transmission capacity. In the OTN, it is particularly crucial to rationally allocate routes and maximize network capacities. By employing deep reinforcement learning (DRL)- and software-defined networking (SDN)-based solutions, the capacity of optical networks can be effectively increased. However, because most DRL-based routing optimization methods have low sample usage and difficulty in coping with sudden network connectivity changes, converging in software-defined OTN scenarios is challenging. Additionally, the generalization ability of these methods is weak. This paper proposes an ensembles- and message-passing neural-network-based Deep Q-Network (EMDQN) method for optical network routing optimization to address this problem. To effectively explore the environment and improve agent performance, the multiple EMDQN agents select actions based on the highest upper-confidence bounds. Furthermore, the EMDQN agent captures the network's spatial feature information using a message passing neural network (MPNN)-based DRL policy network, which enables the DRL agent to have generalization capability. The experimental results show that the EMDQN algorithm proposed in this paper performs better in terms of convergence. EMDQN effectively improves the throughput rate and link utilization of optical networks and has better generalization capabilities.

Transit Network Optimization – Minimizing Transfers and Optimizing Route Directness

This paper presents a mathematical methodology for transit route network optimization. The goal is to provide an effective computational tool for the optimization of large-scale transit route networks. The objectives are to minimize transfers and optimize route directness while maximizing service coverage. The formulation of the methodology consists of three parts: (1) representation of transit route network solution space; (2) representation of transit route and network constraints; and (3) solution search schemes. The methodology has been implemented as a computer program and has been tested using previously published results. Results of these tests and results from the application of the methodology to a large-scale realistic network optimization problem in Miami-Dade County, Florida are presented.

Multi-Objective Routing Optimization for 6G Communication Networks Using a Quantum Approximate Optimization Algorithm.

Sixth-generation wireless (6G) technology has been focused on in the wireless research community. Global coverage, massive spectrum usage, complex new applications, and strong security are among the new paradigms introduced by 6G. However, realizing such features may require computation capabilities transcending those of present (classical) computers. Large technology companies are already exploring quantum computers, which could be adopted as potential technological enablers for 6G. This is a promising avenue to explore because quantum computers exploit the properties of quantum states to perform certain computations significantly faster than classical computers. This paper focuses on routing optimization in wireless mesh networks using quantum computers, explicitly applying the quantum approximate optimization algorithm (QAOA). Single-objective and multi-objective examples are presented as robust candidates for the application of quantum machine learning. Moreover, a discussion about quantum supremacy estimation for this problem is provided.

GDR: A Game Algorithm Based on Deep Reinforcement Learning for Ad Hoc Network Routing Optimization

Ad Hoc networks have been widely used in emergency communication tasks. For dynamic characteristics of Ad Hoc networks, problems of node energy limited and unbalanced energy consumption during deployment, we propose a strategy based on game theory and deep reinforcement learning (GDR) to improve the balance of network capabilities and enhance the autonomy of the network topology. The model uses game theory to generate an adaptive topology, adjusts its power according to the average life of the node, helps the node with the shortest life to decrease the power, and prolongs the survival time of the entire network. When the state of the node changes, reinforcement learning is used to automatically generate routing policies to improve the average end-to-end latency of the network. Experiments show that, under the condition of ensuring connectivity, GDR has smaller residual energy variance, longer network lifetime, and lower network delay. The delay of the GDR model is 10.5% higher than that of existing methods on average.

Multi‐objective moth swarm based sailfish model for optimal routing in wireless sensor network

SummaryIn recent years, the WSN are emerging swiftly since it finds applications in various domains including weather monitoring, attack detection, industrial monitoring, monitoring of submarine organisms, patient monitoring as well as the monitoring of ecological disorders. But WSN is also influenced by various other factors like network lifetime and energy consumption. It is necessary to provide an energy effective protocol to conquer certain troubles that includes packet delivery ratio, network lifetime, residual energy as well as effective routing in WSN. Therefore, this article aims to propose a novel protocol to enhance the energy efficiency of the network thereby providing an optimal routing path. This can be achieved by selecting an optimal cluster head that maintains communication between the base station and the sensor node. In this article, a novel multi‐objective moth swarm based sailfish (MOMS‐SF) technique is employed in selecting an optimal cluster head. The proposed MOMS‐SF technique enhances the network lifetime and minimizes the energy consumption of the network. Finally, the evaluation results are conducted to determine the network performances of the proposed MOMS‐SF approach. Also, a comparative analysis is carried out and the graphical analyzes for various parameters are made for various approaches to determine the effectiveness of the proposed system.

Secure Routing Optimization in Hierarchical Cluster-Based Wireless Sensor Networks

Popularity of wireless sensor networks (WSNs) is increasing continuously in different domains of daily life, as they provide efficient method of collecting valuable data from the surroundings for use in different applications. Routing in WSNs is the vital functionality that allows the flow of information generated by sensor nodes to the base station, while considering the severe energy constraint and the limitations of computational and storage resources. Indeed, this functionality may be vulnerable and must be in itself secured, since conventional routing protocols in WSNs provide efficient routing techniques with low power consumption, but they do not take into account the possible attacks. As sensor nodes may be easily captured and compromised, the classical cryptographic solutions become insufficient to provide optimal routing security, especially, for cluster-based WSNs, where cluster heads can be still among the compromised nodes. In this work, we propose a hierarchical, robust and well-adapted intrusion detection system, named THIDS, which is intended to be integrated into the secure hierarchical cluster-based routing protocols. We have chosen the protocol RLEACH to be equipped with the proposed IDS. The results of simulation performed under NS2 simulator show that the resulting protocol ORLEACH is much more resistant to compromised nodes exercising the most dangerous attacks.

Collection tree-oriented mesh routing optimization for extending the lifetime of wireless sensor networks

Routing optimization in wireless sensor networks facilitates to reduce the overhead of the maintaining of wireless sensor networks and extend the lifetime of wireless sensor networks. Collection tree-based routing protocol, which does not require route discovery, has been widely used for low overheads of calculation and storage. However, with collection tree-based routing protocol, some nodes easily become the bottleneck points and quickly run out of the energy. To deal with this drawback, this article proposes a collection tree-oriented mesh routing strategy with cooperatively consuming the residual energy among the neighboring sensor nodes. The collection tree-oriented mesh routing is formulated into a linear programming problem with the purpose to maximize the network lifetime. By solving the optimization problem, the optimal mesh routing and data forwarding scheme is derived. Experimental simulations show that the proposed collection tree-oriented mesh routing optimization strategy can extend the network lifetime by more than 20%.

Automatic Repair Method for D2D Communication Routing Buffer Overflow Vulnerability in Cellular Network

Repairing D2D communication routing buffer overflow in a cellular network is of great significance in improving communication quality and security. Due to the increase of user usage, the communication data are easy to exceed the boundary of the buffer, resulting in the reduction of covered data information. The traditional repair methods mainly repair through the characteristics of covered data information, ignoring the impact of network topology information transmission delay and packet loss during calculation, resulting in the problem of low communication security. A cellular network routing buffer overflow repair algorithm based on the homomorphic analysis of node residual energy is proposed; the cellular network D2D communication routing protocol is designed; the cellular network D2D communication protocol path index is determined; then, the cellular network D2D communication routing protocol is designed by analyzing node residual energy; and the cellular network D2D communication network routing optimization method based on AHP is designed. Big constructs the energy model of cellular network D2D communication network, solves and sets the routing optimization objective function, realizes the control of network routing, and repairs the buffer overflow. The experiment results show that the improved method can effectively reduce the packet loss rate of communication data, improve the anti-interference ability of the system, and ensure the security of network communication.

Routing Optimization Algorithm of Spatial DTN Network Based on Multiattribute Decision

Delay or stop (DTN) tolerance space network is considered to be a technology that can trade with a limited or restricted field communication area. In the space DTN network architecture, routing is very important. This article is based on the best-in-class multiattribute decision-making DTN network routing optimization algorithm. The goal is to use the optimized DTN network routing algorithm multiattribute decision-making algorithm and conduct a more in-depth study on the field information mechanism multipath decision algorithm and network path. Aiming at the problems of long link delay and frequent link interruption of current deep-space communication characteristics, and solving the impact on communication caused by system performance degradation. This paper adopts the multiattribute decision-making model algorithm to construct simulation tests, introduces the DTN network architecture, and analyzes the four attributes of the multi-attribute decision-making link bandwidth, node data forwarding rate, link establishment delay, and network error rate. The experimental results show that, in the spatial information network, the high-speed movement of nodes and the highly dynamic nature of the network make the deep-space network topology unstable and long communication delay; the change of the link mode and switch connection in the network will cause the routing table to update. This routing update method requires the exchange of topology information between all networks; when the forwarding rates of adjacent nodes are 100%, 90%, 95%, and 70%, nodes can only perform routing independently when they meet. The decision attribute algorithm has obvious changes to the attributes of any node in the spatial DTN network. The most obvious is that the link establishment delay has reached the standard of 5 or more.

Correlative Dynamic Mapping based Optimization (CDMO) for Optimal Allocation in Cognitive Radio Sensor Network

The data communication in the sensor networking system with cognitive radio sensor network increases the speed of packet transmission by the optimal spectrum allocation among available channel. For a large size of network architecture, the routing system needs to manage the packet transmission in an efficient routing path with optimal allocation of channel to the sensor nodes. In these optimal network allocation and routing system, the methods need to be modeled based on the parameters of channel capacity and other routing properties. In this paper, a novel optimization algorithm was proposed to estimate the dynamic change of network parameters. This can be achieved by the enhanced model of Correlative Dynamic Mapping based Optimization (CDMO). In this, it analyse traffic level in a network path and the availability of spectrum channel present at each time instant for the sensor nodes and estimate the availability if channel in it. The proposed system estimates the correlation factor between the sensor nodes and with the weight value of dynamic traffic occurs in the routing path. From these estimated properties, the optimization method selects the best attributes from overall feature set of the node arrangement. The CDMO algorithm performs the prediction of relative feature attributes and form as a clustering architecture to represent the high data transmission by reusing the spectrum of channel. The best fitness value of the convergence graph at every iteration of the optimization algorithm represents the best routing path. From the fitness of the objective function in CDMO, the best routing path and the availability of spectrum for a node can be analysed at each time instant. The experimental result justifies the efficiency of proposed work comparing to other state-of-art methods for routing and channel allocation system.