Routing In Networks Research Articles

Implementasi Jaringan VPN Site-to-Site dan Protocol OSPF Menggunakan Cisco di Sekolah Bina Bangsa

The development of technology is currently very rapid. The need for information is critical, especially in work, this need continues to grow along with users who want fast, safe, and efficient information. Sekolah Bina Bangsa is not directly connected to the network so it is very vulnerable to data leaks and can be considered less secure, so it is necessary to implement VPN (Virtual Private Network) technology. With VPN (Virtual Private Network), data delivery can be more secure because of the encryption method provided by this VPN technology. The OSPF (Open Shortest Path First) protocol is a technology that is applied to help reduce bandwidth in network usage because this protocol can determine the best path in choosing a network route, so it is expected to reduce the existing bandwidth. To prevent foreign devices that are not registered in the network system at school, the author proposes the implementation of Switch-port Security with a violation shutdown configuration. With this configuration, if a foreign device accesses the school network, it will automatically be shut down. Thus, by implementing the existing network system at Bina Bangsa School, it is hoped that data transmission between the Kebon Jeruk Campus and PIK can be carried out safely, can reduce bandwidth in the network, and also the internal network cannot be accessed carelessly by outsiders who are not registered in the network system at Bina Bangsa School.

Routing Protocol for Intelligent Unmanned Cluster Network Based on Node Energy Consumption and Mobility Optimization

Intelligent unmanned clusters have played a crucial role in military reconnaissance, disaster rescue, border patrol, and other domains. Nevertheless, due to factors such as multipath propagation, electromagnetic interference, and frequency band congestion in high dynamic scenarios, unmanned cluster networks experience frequent topology changes and severe spectrum limitations, which hinder the provision of connected, elastic and autonomous network support for data interaction among unmanned aerial vehicle (UAV) nodes. To address the conflict between the demand for reliable data transmission and the limited network resources, this paper proposes an AODV routing protocol based on node energy consumption and mobility optimization (AODV-EM) from the perspective of network routing protocols. This protocol introduces two routing metrics: node energy based on node degree balancing and relative node mobility, to comprehensively account for both the balance of network node load and the stability of network links. The experimental results demonstrate that the AODV-EM protocol exhibits better performance compared to traditional AODV protocol in unmanned cluster networks with dense node distribution and high mobility, which not only improves the efficiency of data transmission, but also ensures the reliability and stability of data transmission.

A Load‐Balancing Enhancement to Schedule‐Aware Bundle Routing

ABSTRACTDelay‐ and disruption‐tolerant networking (DTN) enables communication in networks afflicted by long propagation delays and sporadic connectivity. DTN routing techniques such as schedule‐aware bundle routing (SABR) exist to route data bundles in deterministic networks, such as those found in deep‐space environments, where node contacts are predictable. This article begins with an overview of DTN architecture and SABR. SABR's method of final route selection (forwarding rules) is closely examined. The article then addresses a limitation of SABR whereby the algorithm may overlook parallel channels, leading to network congestion. To mitigate this, an enhancement is proposed. This enhancement aims to optimize data bundle distribution across candidate routes in networks with parallel channels, thus alleviating congestion and enhancing overall network performance. This is achieved with simple modifications to SABR's forwarding rules to avoid the concentration of data bundles on a minority of node contacts. The enhancement is demonstrated through simulations in a reference scenario implemented in DtnSim.

Analysis of intelligent traffic system and its processing algorithm

With the continuous development of modern cities, traffic jams have become a growing problem. This paper analyzes and discusses the Intelligent Traffic System (ITS) produced by solving traffic jams. The purpose of this paper is to study the composition algorithms of Intelligent Traffic System, including path planning algorithm, network routing algorithm, and speed guidance algorithm. These three different algorithms make up the Intelligent Traffic System. In this paper, the above three algorithms are introduced and analyzed, and some typical algorithms are selected for analysis and introduction. The challenges encountered in the practical application of Intelligent Traffic System are analyzed, including the limitations of technology, management and policy. The future application trend of Intelligent Traffic System in urban construction is forecasted. Finally, through the integration and application of Intelligent Traffic Systems, the efficiency and quality of urban construction and development can be significantly improved to support the realization of safer and environmentally sustainable Traffic systems.

Energy-aware and efficient cluster head selection and routing in wireless sensor networks using improved artificial bee Colony algorithm

Energy-aware and efficient cluster head selection and routing in wireless sensor networks using improved artificial bee Colony algorithm

RSSI Enabled Routing in Fiber Wireless Access Network

RSSI Enabled Routing in Fiber Wireless Access Network

Network-aware cache provisioning and request routing in Heterogeneous Cellular Networks

Network-aware cache provisioning and request routing in Heterogeneous Cellular Networks

Iris: Toward Intelligent Reliable Routing for Software-Defined Satellite Networks

Iris: Toward Intelligent Reliable Routing for Software-Defined Satellite Networks

Routing and wavelength assignment in hybrid networks with classical and quantum signals

Routing and wavelength assignment in hybrid networks with classical and quantum signals

Network-aware cache provisioning and request routing in heterogeneous cellular networks

Network-aware cache provisioning and request routing in heterogeneous cellular networks

Next-Gen Routing and Scalability Enhancements in Mobile Ad Hoc Networks

Next-Gen Routing and Scalability Enhancements in Mobile Ad Hoc Networks

Secure Cluster Based Routing Scheme for Neuro‐Fuzzy Assisted MANET in 5G

ABSTRACTThe Fifth Generation (5G) of wireless networks has increasingly focused significantly on energy‐saving methods for mobile communication systems. The objective of combining MANET with 5G communication is to achieve a high data throughout while simultaneously reducing latency and cost. The main issues with MANET are elevated energy use, security, and reduced longevity. The self‐configuring characteristics and dynamic architecture of MANET may lead to frequent changes in topology and resource allocation. This mobility results in frequent connection failures, which increase routing overheads and decrease data transmission dependability. A secured clustering technique is essential to enhance the routing performance and establish reliable routing. In MANET, the cluster mechanism is the prominent way to improve the network lifetime and routing mechanism. The proposed secure cluster routing‐based neuro‐fuzzy logic (SCR‐NFL) establishes a secure cluster mechanism via the use of the Trust value method. It guarantees cluster stability by taking into account node mobility and energy while selecting cluster heads. The important components in the proposed system are Mobility (M), Residual Energy Level (REL), and Trust Value (TV). The neural fuzzy logic system is implemented to handle the data uncertainty and make effective decisions during routing path selections. The proposed SCR‐NFL performance is measured in an NS‐2 simulation environment. The result is compared with the existing Improved Particle Swarm Optimization Algorithm, Trust‐Based Secure Neuro Fuzzy Clustering mechanism and Mobility Aware Energy Efficient Clustering system using Particle Swarm Optimization mechanism. The performance parameters for comparison are packet delivery ratio, average number of clusters, end‐to‐end delay, average number of cluster instances, mobility speed and mobility distance.

Enhancing CNN Weights for Improved Routing in UAV Networks for Catastrophe Relief with MSBO Algorithm

UAVs have become key in various applications lately, from catastrophe relief to environmental monitoring. The plan of powerful and reliable directing protocols in UAV networks is seriously hampered by the dynamic and habitually eccentric mobility patterns of UAVs. This study proposes a novel technique to beat these challenges by utilizing the Modified Smell Bees Optimization (MSBO) algorithm to upgrade the weights of CNNs. This study’s principal objective is to further develop UAV network routing decisions by using CNNs’ ability for design recognition and the Modified SBO’s optimization abilities. Our methodology comprises of randomly relegating CNN weights to a populace of bees at start, evaluating their wellness by fitness of directing performance, and iteratively fine-tuning these weights utilizing local and global search procedures got from bee searching. Broad simulations and performance evaluations show that our recommended approach incredibly expands the general dependability of UAV’s, brings down communication latency, and improves directing productivity. Future exploration in UAV network improvement gives off an impression of being going in a promising direction with the integration of CNNs for pattern recognition and the Modified SBO for weight enhancement. In addition to progressing UAV routing conventions, this work sets out new open doors for machine learning applications of bio-inspired optimization algorithms.

Adaptive Clustering and Trust Aware Routing (ACTAR) for Wireless Sensor Networks in IoT Environments

ABSTRACTThe Internet of Things (IoT) has emerged as a revolutionary technology, connecting a vast number of people to the internet through various devices such as smartphones, laptops, sensors, and more. An essential component of IoT, the wireless sensor network (WSN), enables automation in processes like sensing, node monitoring, and data transmission. However, the full potential of IoT is hindered by cyber threats and unreliable communication. Although several security algorithms exist, they are not suitable for energy‐constrained sensor nodes. To address this issue, this paper proposes an energy‐efficient security mechanism called adaptive clustering and trust aware routing (ACTAR) for IoT‐WSNs. ACTAR operates in three phases: adaptive and hybrid clustering (AHC), multiobjective function‐based cluster head selection (MOCH), and trust aware routing (TAR). First, AHC utilizes a nonuniform clustering mechanism to categorize the network into nearby and distant clusters. Next, the selection of cluster heads is based on four metrics: coverage, communication cost, residual energy, and node proximity. Finally, TAR calculates the trust degree of sensor nodes by evaluating their direct and indirect behavior in terms of communication interactions and energy consumption. The node with the highest trust degree is selected as the next‐hop forwarding node, followed by the route with the highest trust degree. Extensive simulations of ACTAR demonstrate its performance in terms of malicious detection rate, false‐positive rate, residual energy, and packet delivery ratio. Comparative analysis shows that ACTAR outperforms existing methods, proving its superiority.

An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network

AbstractThis paper provides an effective Wireless Sensor Network (WSN) routing solution for Internet of Things (IoT) applications cognizant of congestion, security, and interference. Because several sources try to deliver their packets to a destination simultaneously, which is a common case in IoT applications. The proposed congestion and interference aware safe routing protocol is claimed to work in networks with high traffic. The signal to interference ratio (SINR), congestion level, and survival factor is used in our suggested procedure to estimate the cluster head selection factor first. The adaptive fuzzy c-means clustering method clusters the network nodes based on the cluster head selection factor. After that, data packets are encrypted using Adaptive Quantum Logic-based packet coding. Finally, the Adaptive Krill Herd (AKH) optimization method identifies the least congested corridor, resulting in optimal data transmission routing. The exploratory findings show that the provided strategy outperforms previous methodologies in network performance, end-to-end delay, packet delivery ratio, and node remaining energy level.

Learning-Based Geographic Routing for Delay-Limited Multihop Wireless Networks

Learning-Based Geographic Routing for Delay-Limited Multihop Wireless Networks

Energy-efficient and secure routing strategy for opportunistic data transmission in WSNs

ABSTRACT Driven by the critical importance of routing in Wireless Sensor Networks (WSNs) and the security vulnerabilities present in existing protocols, this research aims to address the key challenges in securing WSNs. Many current routing protocols focus on computational efficiency but fall short of providing strong security measures, leaving them vulnerable to malicious attacks. Reactive protocols, often preferred for their reduced bandwidth usage, heighten security concerns due to their limited resources for maintaining network routes, while proactive alternatives require more resources. Additionally, the ad hoc nature and energy limitations of WSNs make traditional security models, designed for wired and wireless networks, impractical. To overcome these limitations, this paper introduces the Secured Energy-Efficient Opportunistic Routing Scheme for sustainable WSNs. The proposed protocol is designed to enhance security by continuously updating neighbor information and verifying the validity of routing parameters, while also being power-aware, a critical factor given the energy constraints of WSNs. The protocol has been evaluated through simulation experiments, measuring key performance indicators such as throughput, average end-to-end delay (E2 delay), energy consumption, and network lifetime. The results demonstrate that the proposed protocol effectively strengthens WSN security while addressing the unique operational constraints of these networks.

A gradient of hemisphere-specific dorsal to ventral processing routes in parieto-premotor networks.

Networks in the parietal and premotor cortices enable essential human abilities regarding motor processing, including attention and tool use. Even though our knowledge on its topography has steadily increased, a detailed picture of hemisphere-specific integrating pathways is still lacking. With the help of multishell diffusion magnetic resonance imaging, probabilistic tractography, and the Graph Theory Analysis, we investigated connectivity patterns between frontal premotor and posterior parietal brain areas in healthy individuals. With a two-stage node characterization approach, we defined the network role of precisely mapped cortical regions from the Julich-Brain atlas. We found evidence for a third, left-sided, medio-dorsal subpathway in a successively graded dorsal stream, referencing more specialized motor processing on the left. Supplementary motor areas had a strongly lateralized connectivity to either left dorsal or right ventral parietal domains, representing an action-attention dichotomy between hemispheres. The left sulcal parietal regions primarily coupled with areas 44 and 45, mirrored by the inferior frontal junction (IFJ) on the right, a structural lateralization we termed as "Broca's-IFJ switch." We were able to deepen knowledge on gyral and sulcal pathways as well as domain-specific contributions in parieto-premotor networks. Our study sheds new light on the complex lateralization of cortical routes for motor activity in the human brain.

Cognitive network interactions through communication subspaces in large-scale models of the neocortex.

The neocortex-wide neural activity is organized into distinct networks of areas engaged in different cognitive processes. To elucidate the underlying mechanism of flexible network reconfiguration, we developed connectivity-constrained macaque and human whole-cortex models. In our model, within-area connectivity consists of a mixture of symmetric, asymmetric, and random motifs that give rise to stable (attractor) or transient (sequential) heterogeneous dynamics. Assuming sparse low-rank plus random inter-areal connectivity, we show that our model captures key aspects of the cognitive networks' dynamics and interactions observed experimentally. In particular, the anti-correlation between the default mode network and the dorsal attention network. Communication between networks is shaped by the alignment of long-range communication subspaces with local connectivity motifs and is switchable in a bottom-up salience-dependent routing mechanism. Furthermore, the frontoparietal multiple-demand network displays a coexistence of stable and dynamic coding, suitable for top-down cognitive control. Our work provides a theoretical framework for understanding the dynamic routing in the cortical networks during cognition.

LEO: An innovative metric for energy-efficient routing in wireless sensor networks

LEO: An innovative metric for energy-efficient routing in wireless sensor networks