A method of routing optimization using CHNN in MANET

Hua, Yang; Li, Zhimei; Li, Yong

doi:10.1007/s12652-017-0614-1

Cited by 32 publications

(19 citation statements)

References 30 publications

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“…The technique is exploited for automating the routing decisions based on reinforcement concepts. Continuous Hopfield neural network (CHNN) model was proposed for Dynamic Source Routing (DSR) in [29] to obtain the most stable route to improve the QoS in MANET. Authors in [30] proposed different methods for mobility prediction.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

2021

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Routing protocols in Mobile Ad Hoc Networks (MANETs) play a pivotal role in ensuring quality of service (QoS) and improving network performance. Selection of optimal routing protocol and suitable parameters for a given network scenario is a major task that ultimately affects the behavior of network. This work exploits machine learning (ML) techniques for the selection of adequate routing parameters and protocol by regression of parameters in given network scenario to ensure optimal performance. The network is trained based on parametric setup of expanding ring search mechanism (ERS) and random early detection (RED) technique to estimate network throughput, end to end (E2E) delay and packets delivery ratio (PDR) and is tested via wide-ranging simulations in varying network topologies. Both RED and ERS mechanisms are aimed to control link and node level congestion in the reactive routing protocols and our aim is to select the best suited parameters for given network topologies based on ERS and RED parametric setups and improve performance for ensuring QoS. ML algorithms are trained and tested for their performance in varying network topologies. We have exploited these models with best performance for ERS and RED based routing in given topological arrangements. The performance of the ML algorithms is evaluated on the basis of root mean squared error (RMSE) and mean absolute error (MAE) for regression settings. Prediction models with up to par RMSE and MAE out-turns are attained and exploited for selection of suitable ERS and RED parameters and routing protocols in order to ensure the QoS for given network scenario. Variants of standard routing protocols are devised based on their performance and the ML techniques are exploited for prediction of QoS parameters to decide on the optimal variant that attains significant improvement in performance. Results are shown to confirm that considerable improvement in QoS is attained. INDEX TERMS Ad hoc multi hop wireless networks, congestion control, expanding ring search, machine learning, mobile ad hoc networks, on demand routing protocols, random early detection, regression, quality of service.

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Section: Related Workmentioning

confidence: 99%

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

2021

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“…Therefore, such routes are known as on-demand routing protocols. This type of routing protocol has poor stability in highly dynamic networks and is also less secure, for example, Dynamic Source Routing (DSR) [11][12][13] and ad hoc on-demand distance vector (AODV) [11,14,15].…”

Section: Reactive Routingmentioning

confidence: 99%

An optimization routing protocol for FANETs

Hua

2019

J Wireless Com Network

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With the wide-ranging application of mobile ad hoc networks, flying ad hoc networks (FANETs) have received more and more attention from the industry. Routing technology is a key technology of ad hoc networks. The high-speed mobility of nodes poses a greater challenge to FANET routing technology. Based on the Dynamic Source Routing (DSR) protocol, the continuous Hopfield neural network is used to optimize the route to be adapted to the highspeed movement of the FANET node. In a simulation using NS3, the result shows that the optimized DSR protocol has greatly improved key indicators such as end-to-end average delay, throughput, and packet delivery ratio.

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“…Yet, restricts speed convergence. DSR [5] [7] is less expensive, it doesn't need symmetric links, routes are detected only when they are required, and it quickly responds to the network alterations. Although there are some defects such as route request packet will reach all the nodes present in the network, it is not measured to huge networks, and it requires more number of processing resources.…”

Section: Reviewmentioning

confidence: 99%

Impact of Improved Chicken Swam Optimization Based A algorithm In MANETs Routing

Devi,

Rao

2019

IJEAT

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Wireless devices utilization had increased drastically, which has shown an impact on over-all demand and utilization Mobile Ad-Hoc Network (MANET). Routing protocol is the fundamental and vital performance factor in the Mobile Ad-hoc Network (MANET). The routing protocols in MANET are accomplished to handle a lot number of nodes with restricted resources. Multiple routing protocols exist in MANETs. Once of the main challenges in routing protocols is its generation of adverse influence on network performance. Accordingly, this paper plans to implement an obstacle-ware MANET routing model using improved meta-heuristic-based A* algorithm. The algorithm efficiently plots a path between multiple nodes avoiding obstacles, or points, on the graph that results in producing a shortest path without any obstacles. The improved meta-heuristic algorithm termed as Fitness and Position Ratiobased Chicken Swarm Optimization (FPR-CSO) is used to improvise the A* algorithm. The comparative analysis of different optimized A* over Ad hoc On-Demand Distance Vector (AODV) confirms the consistent performance of the proposed model

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A method of routing optimization using CHNN in MANET

Cited by 32 publications

References 30 publications

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

Prediction of Scenarios for Routing in MANETs Based on Expanding Ring Search and Random Early Detection Parameters Using Machine Learning Techniques

An optimization routing protocol for FANETs

Impact of Improved Chicken Swam Optimization Based A algorithm In MANETs Routing

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