Beacon-Based Hybrid Routing Protocol for Large-Scale Unmanned Vehicle Ad Hoc Network

In this paper, we designed a beacon-based hybrid routing protocol to adapt to the new forms of intelligent warfare, accelerate the application of unmanned vehicles in the military field, and solve the problems such as high maintenance cost, path failure, and repeated routing pathfinding in large-scale unmanned vehicle network communications for new battlefields. This protocol used the periodic broadcast pulses initiated by the beacon nodes to provide synchronization and routing to the network and established a spanning tree through which the nodes communicated with each other. An NS3 platform was used to build a dynamic simulation environment of service data to evaluate the network performance. The results showed that when it was used in a range of 5 ~ 35 communication links, the beacon-based routing protocol’s PDR was approximately 10% higher than that of AODV routing protocol. At 5 ~ 50 communication links, the result was approximately 20% higher than the DSDV routing protocol. The routing load was not related to the number of nodes and communication link data and the protocol had better performance than traditional AODV and DSDV routing protocol, which reduced the cost of the routing protocol and effectively improved the stability and reliability of the network. The protocol we designed is more suitable for the scenarios of large-scale unmanned vehicle network communication in the future AI battlefield.

A Hybrid Communication Pattern in Brain Structural Network Revealed by Evolutionary Computation

The human brain functional connectivity network (FCN) is constrained and shaped by the information communication processes in the structural connectivity network (SCN). The underlying communication model thus becomes a critical issue for understanding structure-function coupling in the human brain. A number of communication models featuring different point-to-point routing strategies have been proposed, with shortest path (SP), diffusion (DIF), and navigation (NAV) as the typical, respectively requiring network global knowledge, local knowledge, and their combination for path seeking. Yet these models all assumed the entire brain to use a uniform routing strategy, which contradicted lumping evidence supporting the wide variety of brain regions in both terms of biological substrates and functional exhibitions. In this study, we developed a novel communication model that allowed each brain region to choose a routing strategy from SP, DIF, and NAV independently. A genetic algorithm was designed to uncover the underlying region-wise hybrid routing strategy (namely HYB) for maximizing the structure-function coupling. The HYB-based model outperformed the three typical models in terms of predicting FCN and supporting robust communication. In HYB, brain regions in lower-order functional modules inclined to choose the routing strategies requiring more global knowledge, while those in higher-order functional components preferred to choose DIF. Additionally, compared to regions using SP and NAV, regions using DIF had denser structural connections, participated in more functional modules, but were less dominant within them. Together, our findings revealed and evidenced the possibility and advantages of hybrid routing underpinning efficient SCN communication.

Underwater Target Signal Classification Using the Hybrid Routing Neural Network

In signal analysis and processing, underwater target recognition (UTR) is one of the most important technologies. Simply and quickly identify target types using conventional methods in underwater acoustic conditions is quite a challenging task. The problem can be conveniently handled by a deep learning network (DLN), which yields better classification results than conventional methods. In this paper, a novel deep learning method with a hybrid routing network is considered, which can abstract the features of time-domain signals. The used network comprises multiple routing structures and several options for the auxiliary branch, which promotes impressive effects as a result of exchanging the learned features of different branches. The experiment shows that the used network possesses more advantages in the underwater signal classification task.

Network Automation using Ansible for EIGRP Network

Network automation has evolved into a solution that emphasizes efficiency in all areas. Furthermore, communication and computer networks rely on a platform that provides the necessary technological infrastructure for packet transfer through the Internet using routing protocols. The Enhanced Interior Gateway Routing Protocol (EIGRP) is a hybrid routing protocol that combines the properties of both distance-vector and link-state routing methods. The traditional technique to configure EIGRP is inefficient and requires repeated processes compared to the network automation concept. Network automation helps to assist network administrators in automating and verifying the EIGRP configuration using scripting. This paper implemented network automation using Ansible to configure EIGRP routing and advanced configuration in the GNS3 environment. This study is focused on automated scripting to configure IP Addresses to the interfaces, EIGRP routing protocol, a default static route and advanced EIGRP configurations. Ansible ran the scripting on Network Automation Docker and pushed the configurations to the routers. The network automation docker communicated with other routers via SSH. In the testing phase, the running configuration between the traditional approach and automation scripting in the Ansible playbook was compared to verify EIGRP configurations' accuracy. The findings show that Ansible has successfully deployed the configuration to the routers with no errors. Ansible can help network administrators minimized human mistakes, reduce time-consuming and enable device visibility across the network environment. Implementing EIGRP authentication and hardening process can enhance the network security level for future study.

An Adaptive Hybrid Routing Scheme For Energy Management In Wireless Sensor Networks

In Wireless Sensor Network (WSN) the life time of nodes and energy management are important issues, because the nodes in WSN required more energy when it is used in different applications. On the other hand, unstable energy consumption among intermediate nodes tends to huge data loss. To address this problem the present research introduced a novel Hybrid Gossip Grey Wolf Ant lion (HGGW-AL) protocol to afford an efficient and better transmission channel. Here, the fitness of grey wolf and ant lion helps to categorize the energy drained node and also, to predict the malicious activities. Furthermore, the novel Rest Awake (RA) is initialized to process the clustering strategy to maintain the residual energy in WSN. Moreover, it enhances the energy level of sensor nodes by increasing its lifetime. Finally, the efficiency of the proposed strategy is compared with the existing works and achieved better performance by reducing the energy consumption of each sensor node.