ANFIS Aided AODV Routing Protocolfor Mobile Ad Hoc Networks

Sharma, Vivek; Alam, Bashir; Doja, M. N.

doi:10.3844/jcssp.2017.514.523

Cited by 3 publications

(2 citation statements)

References 34 publications

(40 reference statements)

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“…ANFIS is a soft computing strategy that combines fuzzy logic and ANN soft computing techniques. Kemal and Alhasa (2016) (Cheng et al, 2005) (Sharma et al, 2017). Fuzzy reasoning can change the qualitative aspects of human knowledge and offer fresh perspectives on the process of exact quantitative analysis.…”

Section: Adaptive Neuro-fuzzy Interference System (Anfis)mentioning

confidence: 99%

Feasibility of Hybrid Neuro-Fuzzy (ANFIS) Machine Learning Model with Classical Multi-Linear Regression (MLR) For the Simulation of Solar Radiation: A Case Study Abuja, Nigeria

Gafai¹,

Belgore²

2022

Energy Research Journal

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The extremely variable nature of solar radiation makes it difficult for solar power plants to keep up with predicted power output and demand curves. As a result, solar radiation simulation is crucial to the efficient design, administration, and operation of any solar power plant. With only partially satisfactory results, empirical models have been routinely employed in Nigeria to predict solar radiation from easily measurable environmental characteristics like temperature, humidity and cloud cover. Only a few machine learning models have been used to predict sun radiation in Nigeria, despite the global trend toward machine learning. With almost no published work utilizing Abuja as a case study, machine learning algorithms for simulating sun radiation in Nigeria have not been sufficiently studied. By contrasting the performance of the conventional Multi-Linear Regression (MLR) model with the cutting-edge machine learning model, ANFIS, this study seeks to close this gap and establish which model is more suited and accurate for forecasting solar radiation in Abuja, Nigeria. Data for daily measured climatic variables, such as maximum and minimum temperatures, relative humidity, precipitation, maximum and minimum wind speeds, sunshine hours, and solar radiation were retrieved for this study over ten years from the National Space Research and Development Agency, (NASDRA) Abuja. R, R 2 , RMSE, and MSE were used to simulate and assess the performance of various model combinations throughout both the training and testing stages. When compared to the best MLR model simulation, ANFIS model 8 was shown to generate accurate results.

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Section: Adaptive Neuro-fuzzy Interference System (Anfis)mentioning

confidence: 99%

Feasibility of Hybrid Neuro-Fuzzy (ANFIS) Machine Learning Model with Classical Multi-Linear Regression (MLR) For the Simulation of Solar Radiation: A Case Study Abuja, Nigeria

Gafai¹,

Belgore²

2022

Energy Research Journal

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“…To find the optimal route, a new scheme for AODV named as AAODV (Adaptive Neural Fuzzy Inference System-AODV) routing protocol was suggested by vivek sharma et al [8]. ANFIS takes three input parameters: hop count, energy, and delay, and produces one output parameter.…”

Section: Related Workmentioning

confidence: 99%

F-EEAODV: Fuzzy Based Energy Efficient Reactive Routing Protocol in Wireless Ad-hoc Networks

Sarao¹

2018

JCM

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Wireless ad-hoc networks are the self organizable, self-maintained and ad-hoc by nature. Due to high mobility of nodes, the energy of a node is most important parameter in respect of network lifetime.. Routing in such a network is a big challenge.In this paper, we have proposed a fuzzy based scheme for enhancing the efficiency of AODV in wireless ad-hoc networks. Next hop selection is performed on the basis of node energy, its neighbour node enrgy, node degree. Thus next hop selection is taken by considering a number of parameters including number of hops. As we are considering energy based parameters for next hop selection, the lifetime of nodes and network is improved. Each node is embedded with a fuzzy controller system through which output parameter chance is calculated. Based on this chance value next hop selection is carried out.We have compared the proposed routing protocol F-EEAODV with AODV, DSR, and DSDV routing protocols. Simulation work is carried out using NS-2. The simulation results taken by NS-2 shows that our proposed scheme outperforms as compared to AODV in respect of throughput, end-to-end delay, propagation delay.

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A Multi-metric Routing Protocol to Improve the Achievable Performance of Mobile Ad Hoc Networks

Quy

Ban

Hân

2018

Modern Approaches for Intelligent Information and Database Systems

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ANFIS Aided AODV Routing Protocolfor Mobile Ad Hoc Networks

Cited by 3 publications

References 34 publications

Feasibility of Hybrid Neuro-Fuzzy (ANFIS) Machine Learning Model with Classical Multi-Linear Regression (MLR) For the Simulation of Solar Radiation: A Case Study Abuja, Nigeria

Feasibility of Hybrid Neuro-Fuzzy (ANFIS) Machine Learning Model with Classical Multi-Linear Regression (MLR) For the Simulation of Solar Radiation: A Case Study Abuja, Nigeria

F-EEAODV: Fuzzy Based Energy Efficient Reactive Routing Protocol in Wireless Ad-hoc Networks

A Multi-metric Routing Protocol to Improve the Achievable Performance of Mobile Ad Hoc Networks

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