FQ-AGO: Fuzzy Logic Q-Learning Based Asymmetric Link Aware and Geographic Opportunistic Routing Scheme for MANETs

Alshehri, Ali; Badawy, Abdel-Hameed A.; Huang, Hong

doi:10.3390/electronics9040576

Cited by 14 publications

(5 citation statements)

References 42 publications

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“…Both the network's routing and neighbour node cooperation are impacted by these factors. MANETs are emerging as significant technologies to support reliable and effective processes in mobile wireless networks by integrating routing functions into mobile knots [7][8]. For situations needing quickly deployable communications with resilient, effective dynamic networking, ad hoc network equipment can offer a very flexible technique of starting transportation for burn, security, and rescue operations [9].…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

R.,

Mohan,

Mahesh

2023

IJRITCC

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Portable impromptu organization is a self-putting together, major construction-less, independent remote versatile hub that exists without even a trace of a determined base station or government association. MANET requires no extraordinary foundation as the organization is unique. Multicasting is an urgent issue in correspondence organizations. Multicast is one of the effective methods in MANET. In multicasting, information parcels from one hub are communicated to a bunch of recipient hubs all at once, at a similar time. In this research work, Failure Node Detection and Efficient Node Localization in a MANET situation are proposed. Localization in MANET is a main area that attracts significant research interest. Localization is a method to determine the nodes’ location in the communication network. A novel routing algorithm, which is used for Predicting and Recovering Link Failure Localization using a Genetic Algorithm with Competitive Swarm Optimization (PRLFL-GACSO) Algorithm is proposed in this study to calculate and recover link failure in MANET. The process of link failure detection is accomplished using mathematical modelling of the genetic algorithm and the routing is attained using the Competitive Swarm optimization technique. The result proposed MANET method makes use of the CSO algorithm, which facilitates a well-organized packet transfer from the source node to the destination node and enhances DSR routing performance. Based on node movement, link value, and endwise delay, the optimal route is found. The main benefit of the PRLFL-GACSO Algorithm is it achieves multiple optimal solutions over global information. Further, premature convergence is avoided using Competitive Swarm Optimization (CSO). The suggested work is measured based on the Ns simulator. The presentation metrix are PDR, endwise delay, power consumption, hit ratio, etc. The presentation of the proposed method is almost 4% and 5% greater than the present TEA-MDRP, RSTA-AOMDV, and RMQS-ua methods. After, the suggested method attains greater performance for detecting and recovering link failure. In future work, the hybrid multiway routing protocols are presented to provide link failure and route breakages and liability tolerance at the time of node failure, and it also increases the worth of service aspects, respectively.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

R.,

Mohan,

Mahesh

2023

IJRITCC

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“…Several proposals have been studied in the literature [51][52][53][54][55] in order to improve geographical routing protocols by establishing stable and faster routing paths towards the destination in various network environments in MANETs and VANETs/FANETs. Most of these protocols were assigned to improve routing to select the best candidate nodes toward the destination.…”

Section: Gpsr Routing Mechanismmentioning

confidence: 99%

AFB-GPSR: Adaptive Beaconing Strategy Based on Fuzzy Logic Scheme for Geographical Routing in a Mobile Ad Hoc Network (MANET)

Al-Essa,

Al-Suhail

2023

Computation

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In mobile ad hoc networks (MANETs), geographical routing provides a robust and scalable solution for the randomly distributed and unrestricted movement of nodes. Each node broadcasts beacon packets periodically to exchange its position with neighboring nodes. However, reliable beacons can negatively affect routing performance in dynamic environments, particularly when there is a sudden and rapid change in the nodes’ mobility. Therefore, this paper suggests an improved Greedy Perimeter Stateless Routing Protocol, namely AFB-GPSR, to reduce routing overhead and increase network reliability by maintaining correct route selection. To this end, an adaptive beaconing strategy based on a fuzzy logic scheme (AFB) is utilized to choose more optimal routes for data forwarding. Instead of constant periodic beaconing, the AFB strategy can dynamically adjust beacon interval time with the variation of three network parameters: node speed, one-hop neighbors’ density, and link quality of nodes. The routing evaluation of the proposed protocol is carried out using OMNeT++ simulation experiments. The results show that the AFB strategy within the GPSR protocol can effectively reduce the routing overhead and improve the packet-delivery ratio, throughput, average end-to-end delay, and normalized routing load as compared to traditional routing protocols (AODV and GPSR with fixed beaconing). An enhancement of the packet-delivery ratio of up to 14% is achieved, and the routing cost is reduced by 35%. Moreover, the AFB-GPSR protocol exhibits good performance versus the state-of-the-art protocols in MANET.

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“…When the agent reaches the destination during the movement, the algorithm completes an iteration. The agent returns to the initial node to continue the iteration cycle until the iterative learning process is complete [36,37].…”

Section: Q-learning Algorithmmentioning

confidence: 99%

Indoor Emergency Path Planning Based on the Q-Learning Optimization Algorithm

Gu²,

et al. 2022

IJGI

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The internal structure of buildings is becoming increasingly complex. Providing a scientific and reasonable evacuation route for trapped persons in a complex indoor environment is important for reducing casualties and property losses. In emergency and disaster relief environments, indoor path planning has great uncertainty and higher safety requirements. Q-learning is a value-based reinforcement learning algorithm that can complete path planning tasks through autonomous learning without establishing mathematical models and environmental maps. Therefore, we propose an indoor emergency path planning method based on the Q-learning optimization algorithm. First, a grid environment model is established. The discount rate of the exploration factor is used to optimize the Q-learning algorithm, and the exploration factor in the ε-greedy strategy is dynamically adjusted before selecting random actions to accelerate the convergence of the Q-learning algorithm in a large-scale grid environment. An indoor emergency path planning experiment based on the Q-learning optimization algorithm was carried out using simulated data and real indoor environment data. The proposed Q-learning optimization algorithm basically converges after 500 iterative learning rounds, which is nearly 2000 rounds higher than the convergence rate of the Q-learning algorithm. The SASRA algorithm has no obvious convergence trend in 5000 iterations of learning. The results show that the proposed Q-learning optimization algorithm is superior to the SARSA algorithm and the classic Q-learning algorithm in terms of solving time and convergence speed when planning the shortest path in a grid environment. The convergence speed of the proposed Q- learning optimization algorithm is approximately five times faster than that of the classic Q- learning algorithm. The proposed Q-learning optimization algorithm in the grid environment can successfully plan the shortest path to avoid obstacle areas in a short time.

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FQ-AGO: Fuzzy Logic Q-Learning Based Asymmetric Link Aware and Geographic Opportunistic Routing Scheme for MANETs

Cited by 14 publications

References 42 publications

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

AFB-GPSR: Adaptive Beaconing Strategy Based on Fuzzy Logic Scheme for Geographical Routing in a Mobile Ad Hoc Network (MANET)

Indoor Emergency Path Planning Based on the Q-Learning Optimization Algorithm

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