Mamdani Fuzzy Inference System Research Articles

Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation.

Traditional manual weighing systems for birds on poultry farms are time-consuming and may compromise animal welfare. Although automatic weighing systems have been introduced as an alternative, they face limitations in accurately estimating the weight of heavy birds. Therefore, exploring alternative methods that offer improved efficiency and precision is necessary. One promising solution lies in the application of AI, which has the potential to revolutionize various aspects of poultry production and management, making it an indispensable tool for the modern poultry industry. This study aimed to develop an AI approach based on the FL model as a viable solution for estimating poultry weight. By incorporating expert knowledge and considering key input variables such as indoor temperature, indoor humidity, and feed consumption, FL-based models were developed with different configurations using Mamdani inferences and evaluated across eight different rearing periods in Samsun, Türkiye. This study's results demonstrated the effectiveness of FL-based models in estimating poultry weight. The models achieved varying average absolute error values across different age groups of broilers, ranging from 0.02% to 5.81%. These findings suggest that FL-based methods hold promise for accurate and efficient poultry weight estimation. This study opens up avenues for further research in the field, encouraging the exploration of FL-based approaches for improved poultry weight estimation in poultry farming operations.

A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology

This study explores the generalization of heterogeneous medical data for monitoring anomalies and changes over time using fuzzy intervals. The most important feature of these intervals is saving the parameter value as a membership function from the interval [0, 1]. An example illustrating this method is the blood count parameters of an oncological patient recorded for three years with a monthly frequency. Over 20 typical measurements of these features are considered, and eight with the highest variance are selected. The registration of the overall picture of changes, a synthesis of eight fuzzy intervals, allowed for observing a systematic improvement in health. This approach allows the doctor to take a holistic view of the patient’s health (based on blood tests), avoiding the dilemma of which parameters are less and which are more important. The Mamdani fuzzy inference system was used to assess the patient’s health status. The study presents the actual results of medical measurements, and the GitHub repository contains measurement data.

Development of A Fuzzy Logic-Based Cost Modeling System for Sugar Industry

The objective of the study was to create a fuzzy logic-based cost modelling system for the sugar industry in order to comprehend the impacts of sugar cost determinants on total cost. The designed system takes into account variables such as fluctuating cane prices, cane weight, distribution distances, and crushing cessation. The data is collected from the cost statements of various Pakistani sugar refineries. The primary research objective was to determine the effects of the unpredictability of various cost variables on the ultimate price of sugar. The cost determinants were deemed input variables, while the cost of sugar production was considered an output variable. The cost variables were categorised as follows: cost of basic materials, cost of distribution, cost of labour, cost of operations, factory overheads, and crushing cessation (losses). The Mamdani Fuzzy Inference system was used to analyse the data. To develop the system, 729 fuzzy rules and three levels of fuzzy sets were created. The developed fuzzy logic-based system is able to evaluate the sugar cost structure and take uncertainty factors into account. Consequently, a significant contribution of the developed system is the provision of heuristic principles that facilitate the selection of cost-effective outcomes.

Intelligent identification and classification of diabetic retinopathy using fuzzy inference system

ABSTRACT Background and Objective Persistent diabetes results in diabetic retinopathy (DR), affecting the retinal blood vessels (BVs), causing lesions. Rapid identification and treatment are crucial for preventing vision loss. Low ophthalmologist to patient’s ratio results automating the DR detection a dire need. Therefore, a feature extraction method is proposed using a Mamdani fuzzy inference system (FIS) classifier for efficient identification. Methods Mathematical morphology, region growth, and 12-region search computation have been used to mask the BVs and macula. The masked green plane image was subjected to Nick's thresholding to locate the dark lesions, from which statistical features were extracted and employed in the Mamdani FIS to classify the DR. Results On evaluating a total of 909 images from the MESSIDOR database shows, average sensitivity, specificity, area under the curve receiver operating characteristics, and accuracy of 99.7%, 99.8%, 99.4%, and 99.6%, respectively. The algorithm performs well in real-time images from two local hospitals. Conclusion The proposed technique provides a powerful yet flexible tool for improving the diagnosis and treatment of this condition that threatens vision, as it combines the strengths of fuzzy logic, clinical knowledge, and adaptive learning to provide precise, timely, non-invasive, and economical solutions.

Hybrid Source Multi-Port Quasi-Z-Source Converter with Fuzzy-Logic-Based Energy Management

In this paper, a fuzzy-logic-based energy management system and a multi-port quasi-z-source converter that utilizes hybrid renewable energy sources are proposed. The system ensures that each energy source module can be used individually by employing fuzzy logic to define the power modes. This approach also helps to prevent switching losses resulting from the extra switching of the source modules. In addition, the proposed energy management does not have a mathematical model, so its applicability is simple, and it is suitable for different multiple-input topologies. The Mamdani fuzzy inference system can be designed to capture the nonlinear behavior of the system owing to linguistic rules. Moreover, the switching losses of the multiport modules were significantly reduced by adopting the quasi-z-source network to the end of the multiport converter. Furthermore, different errors, such as the root mean square error (RMSE), average squared error (ASE), average absolute error (AAE), average time-weighted absolute error (ATWAE), tracking error (TE), and unscaled mean bounded relative absolute error (UMBRAE), were applied to evaluate the fuzzy logic performance from different perspectives. The simulation results were obtained using MATLAB Simulink, and the experimental results were obtained by connecting the circuit to MATLAB Simulink using an Arduino Due.

Secure Internet of Thing based data communication in blockchain model using novel teaching‐learning optimized fuzzy approach

AbstractThe potential uses in the Internet of Things (IoT), blockchain is gaining prominence. It is particularly skillful at storing data in immutable blocks, which is connected to its protected peer‐to‐peer architecture in the face of a growing challenge of transaction authorization in industrial and service‐provider applications. IoT is prolonged to several additional applications as a result of its integration with various technologies, allowing direct engagement with our individual and professional lives. The major aim of this study is to provide the security based IoT data communication in blockchain using novel techniques. The device's security is a significant concern. However, on‐demand security solutions have issues since devices have limited processing and energy resources. IoT devices do not allow for the use of large, costly systems. In order to address these issues, we present a novel optimized fuzzy architecture for a blockchain‐based IoT device data exchange and communication in this research. The TLBO method is used to improve decision‐making capabilities, while the Mamdani fuzzy inference system makes the majority of the decisions. To construct the two input variables (drop ratio and integrated trust level) and reduce the dimensionality of the input information, the TLBO method was used. We examine and illustrate the efficacy of our method with another state‐of‐art method. Based on the experiment, we have obtained 1.8, 2.1, 2.8, 3.2 and 3.8 ms computational time based on the number of users from 2 to 10, respectively. The proposed method attains 96% accuracy and 95.4% F‐score results than other previous methods.

KEdge: Fuzzy-Based Multi-AI Model Coalescence Solution for Mobile Healthcare System

In this work, we propose a condition-aware analytical framework—KEdge—for health condition recommendation in Internet of Things (IoT) based mobile healthcare systems. Procuring data from multiple sensors and making a singular assessment from them is a challenging task. KEdge overcomes such an issue by determining the severity of the patient by determining a condition index (CI) using a two-step analytical framework and a multiple rule fuzzy inference system (FIS). In the first step, KEdge detects the heart severity condition using a convolutional neural network model and, in the second step, it detects the respiratory condition using a random forest classification model. KEdge also utilizes auscultation sounds from SkopEdge (a digital stethoscope) for assessing the heartbeats. Through extensive experiments, we observe that KEdge identifies the arrhythmia condition with an accuracy of 98.53% and respiratory condition by 98.68%. KEdge considers the analytical predictions and analysis from SkopEdge to evaluate the CI for recommending the overall health condition using Mamdani FIS. We observe that KEdge is suitable for resource-constrained IoT devices providing memory consumption of 6.6%. On offloading the same to the fog nodes, we observe improved CPU utilization with data upload rates in the order of 25 kb/s (KEdge) and 5 Mb/s (SkopEdge).

Smart Pet Feeder on Cat Food Portions Using Mamdani's Fuzzy Logic Inference System Method

This is a system that can help cat owners provide the right amount of food for their cats. This system uses the Mamdani fuzzy logic inference system method which can process input in the form of information about the cat's body weight and make a decision about the amount of food to be given to the cat. This system is integrated with a smart pet feeder called FoodyPet which can dispense food according to the decision made by the system. By using this system, cat owners can easily provide the right amount of food for their cats and maintain their cat's health. In addition, this system can also help cat owners automatically schedule their cat's meal times so they don't have to worry about forgetting to give food to their cats. This system consists of several main components, namely Arduino UNO as a microcontroller that runs all data processing processes, an loadcell as a tool to measure the cat's body weight, an ultrasonic sensor as a tool to measure the level of food in the tank, and an LCD display used to display information about the food portion to be given to the cat. This system is also equipped with a buzzer that will signal the cat owner when the food is ready to be given to the cat.

A method for predicting blast-induced ground vibration based on Mamdani Fuzzy Inference System

Blast-induced ground vibration is highly possible to result in serious losses such as destroyed buildings. The crucial parameter of the mentioned vibration is peak particle velocity (PPV). Many equations have been developed to predict PPV, however, worse performance has been reported by multiple literatures. This paper developed a method for predicting PPV based on Mamdani Fuzzy Inference System. Firstly, Minimum Redundancy Maximum Relevance was employed to identify the blasting design parameters which significantly contribute to the PPV induced by blasting. Secondly, K-means method was applied to determine the value ranges of the selected parameters. The selected parameters and corresponding value ranges were combined to input into Mamdani Fuzzy Inference System for obtaining predicted PPV. Totally, 280 samples were collected from a blasting site. 260 out of them were used to train the proposed method and 20 were assigned for test. The proposed method was tested in the comparison with empirical equation USBM, multiple linear regression analysis, pure Mamdani Fuzzy Inference System in terms of the difference between predicted PPV and measured PPV, coefficient of correlation, root-mean-square error, and mean absolute error. The results from that showed that the proposed method has the better performance in PPV prediction.

Study of the Preparation Phase of Turkey’s Powerful Earthquake (6 February 2023) by a Geophysical Multi-Parametric Fuzzy Inference System

On 6 February 2023, a powerful earthquake at the border between Turkey and Syria caused catastrophic consequences and was, unfortunately, one of the deadliest earthquakes of the recent decades. The moment magnitude of the earthquake was estimated to be 7.8, and it was localized in the Kahramanmaraş region of Turkey. This article aims to investigate the behavior of more than 50 different lithosphere–atmosphere–ionosphere (LAI) anomalies obtained from satellite data and different data services in a time period of about six months before the earthquake to discuss the possibility of predicting the mentioned earthquake by an early warning system based on various geophysical parameters. In this study, 52 time series covering six months of data were acquired with: (i) three identical satellites of the Swarm constellation (Alpha (A), Bravo (B) and Charlie (C); and the analyzed parameters: electron density (Ne) and temperature (Te), magnetic field scalar (F) and vector (X, Y and Z) components); (ii) the Google Earth Engine (GEE) platform service data (including ozone, water vapor and surface temperature), (iii) the Giovanni data service (including the aerosol optical depth (AOD), methane, carbon monoxide and ozone); and (iv) the USGS earthquake catalogue (including the daily seismic rate and maximum magnitude for each day), around the location of the seismic event from 1 September 2022 to 17 February 2023, and these were analyzed. The results show that the number of seismic anomalies increased since about 33 days before the earthquake and reached a peak, i.e., the highest number, one day before. The findings of implementing the proposed predictor based on the Mamdani fuzzy inference system (FIS) emphasize that the occurrence of a powerful earthquake could be predicted from about nine days to one day before the earthquake due to the clear increase in the number of seismo-LAI anomalies. However, this study has still conducted a posteriori, knowing the earthquake’s epicenter and magnitude. Therefore, based on the results of this article and similar research, we emphasize the urgency of the creation of early earthquake warning systems in seismic-prone areas by investigating the data of different services, such as GEE, Giovanni and various other global satellite platforms services, such as Swarm. Finally, the path toward earthquake prediction is still long, and the goal is far, but the present results support the idea that this challenging goal could be achieved in the future.

Application of interval type-2 fuzzy logic and type-1 fuzzy logic-based approaches to social networks for spam detection with combined feature capabilities.

Social networks are large platforms that allow their users to interact with each other on the Internet. Today, the widespread use of social networks has made them vulnerable to malicious use through different methods such as fake accounts and spam. As a result, many social network users are exposed to the harmful effects of spam accounts created by malicious people. Although Twitter, one of the most popular social networking platforms, uses spam filters to protect its users from the harmful effects of spam, these filters are insufficient to detect spam accounts that exhibit new methods and behaviours. That's why on social networking platforms like Twitter, it has become a necessity to use robust and more dynamic methods to detect spam accounts. Fuzzy logic (FL) based approaches, as they are the models such that generate results by interpreting the data obtained based on heuristics viewpoint according to past experiences, they can provide robust and dynamic solutions in spam detection, as in many application areas. For this purpose, a data set was created by collecting data on the twitter platform for spam detection. In the study, fuzzy logic-based classification approaches are suggested for spam detection. In the first stage of the proposed method, a data set with extracted attributes was obtained by applying normalization and crowdsourcing approaches to the raw data obtained from Twitter. In the next stage, as a process of the data preprocessing step, six attributes in the binary form in the data set were subjected to a rating-based transformation and combined with the other real-valued attribute to create a database to be used in spam detection. Classification process inputs were obtained by applying the fisher-score method, one of the commonly used filter-based methods, to the data set obtained in the second stage. In the last stage, the data were classified based on FL based approaches according to the obtained inputs. As FL approaches, four different Mamdani and Sugeno fuzzy inference systems based on interval type-1 and Interval Type-2 were used. Finally, in the classification phase, four different machine learning (ML) approaches including support vector machine (SVM), Bayesian point machine (BPM), logistic regression (LR) and average perceptron (Avr Prc) methods were used to test the effectiveness of these approaches in detecting spam. Experimental results were obtained by applying different FL and ML based approaches on the data set created in the study. As a result of the experiments, the Interval Type-2 Mamdani fuzzy inference system (IT2M-FIS) provided the highest performance with an accuracy of 0.955, a recall of 0.967, an F-score 0.962 and an area under the curve (AUC) of 0.971. However, it has been observed that FL-based spam models have a higher performance than ML-based spam models in terms of metrics including accuracy, recall, F-score and AUC values.

Analysis of fuzzy based intelligent health care application system for the diagnosis of mental health in women with ovarian cancer using computational models

Depending on the characteristics of the cancer and the specific treatment required, each type of cancer comes with a unique set of challenges in the psychological wellbeing of women. This research work mainly focuses on Ovarian cancer since the it is one of the 5th leading cancers among women. As per the statistics of 2021, by the American Cancer Society, 21,410 women would be diagnosed with ovarian cancer and 13,770 women might die from ovarian cancer. Both physically and psychologically, ovarian cancer presents several challenges. To control the growth of the tumour, multiple treatments are required. The psychological issues in women with ovarian cancer is mainly due to “loss of femininity” that affects them while they proceed through the phases of diagnosis, treatment and recurrence. Psychological factors associated with both, having ovarian cancer and being at risk are considered in this study. In the proposed work, PHQ 9 and GAD 7 are the tools used to measure depression and anxiety among women who are undergoing treatment for ovarian cancer. The data, collected with the help of these tools, is analysed using the popular Machine Learning algorithms such as k-Nearest Neighbour (kNN), Random Forest, Support Vector Machine (SVM), Artificial Neural Network etc. The results of Machine Learning algorithms are then compared with Mamdani and Sugeno fuzzy inference models. The Sugeno fuzzy inference system outperformed in comparison to all other models, with an accuracy of 96.2% for depression and 98.83% for anxiety, followed by Mamdani fuzzy inference system giving 94.3% accuracy for depression and 96.7% for anxiety. The performance is then compared with the linear SVM which obtained an accuracy of 91.52% for depression and 93.78% for anxiety. The analysed performance of the data using computational algorithms are compared with that of expert clinical psychologists. The severely affected women are advised to visit a psychiatrist.

NMSFRA: Heterogeneous routing protocol for balanced energy consumption in mobile wireless sensor network

Wireless sensor networks are widely used in various fields and consist of a large number of sensor nodes that are powered by batteries. As a result, energy efficiency is crucial for the effective operation of these networks. With the advancement of cross technology communication (CTC), the presence of heterogeneous wireless nodes, especially mobile nodes, in wireless sensor networks has increased, making traditional routing protocols for homogeneous wireless sensor networks inapplicable. To overcome the above issues, this paper proposes a heterogeneous wireless sensor network routing protocol for a balanced energy consumption, NMSFRA (NGO based mean shift fuzzy clustering routing algorithm). In order to mitigate the impact of uneven cluster distribution and unstable network links due to the mobility of nodes, the proposed protocol first divides the network into balanced clusters using the Mean Shift (MS) method. The Mamdani fuzzy inference system is then used to take node mobility into account. The protocol dynamically selects different parameters as inputs to the fuzzy logic, enabling it to choose cluster heads (CHs) based on the overall location of the clusters. Additionally, to extend the network lifetime, the protocol generates multi-hop forwarding paths using the Northern Goshawk Optimization (NGO) method. Finally, the paper presents experimental simulations carried out on three different network topologies and compares the results with existing protocols such as LEACH, EETPF, E-FUCA, and DE-SEP. The results show that the NMSFRA protocol extends the survival period of nodes by 235 rounds, 240 rounds, 364 rounds, and 190 rounds, on average, compared to the LEACH, EETPF, E-FUCA, and DE-SEP protocols, respectively. The network throughput also increased by 154.92%, 48.74%, 84.77%, and 106.77%, respectively. The NMSFRA protocol also showed better performance in terms of total residual energy, FND, HND, and average speed difference between nodes in the first 5 rounds. Therefore, the proposed NMSFRA protocol demonstrates superior performance in terms of network lifetime, energy utilization, throughput, and network stability.

A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

With the maturation of nonlinear systems, considerable endeavors have been made to provide valid and high-speed controllers to supervise superior and more complex systems. Artificial intelligence has been remembered as the head topic among designers in the last decade. One of the popular control techniques is fuzzy logic, which is known to provide a controller that simulates the behavior of an expert operator. On the other hand, due to the necessity of change in human energy sources and the popularity of solar energy, attention to the greatest utilization of this category of green resources has significantly increased. Maximum power point tracking (MPPT) in solar systems is a headed topic, with innovative methods being presented every day despite numerous articles. However, the less discussed topic is the choice of a fuzzy inference system. In this article, the two classes of Mamdani and Sugeno are discussed to introduce the best controller for extracting more power from a solar system by implementing both types and gaining an understanding of their differences. In addition, the influence of the number of input membership functions on the controller performance is investigated. Therefore, two different input membership functions are given to each fuzzy system model. It should be noted that fuzzy system setup has been done by genetic algorithm to respond to the mortal desire to automate various processes, which is a subset of artificial intelligence. Accordingly, four different fuzzy systems have been designed and implemented on a solar system. The results were tested and summarized in various radiations in MATLAB Simulink.

Fuzzy AHP point factored inference system for detection of cardiovascular disease

The World health organization (WHO) reported that cardiovascular disease is the leading cause of death worldwide, particularly in developing countries. But while diagnosing cardiovascular disease, medical practitioners might have differences of opinions and faced challenging when there is inadequate information and uncertainty of the problem. Therefore, to resolve ambiguity and vagueness in diagnosing disease, a perfect decision-making model is required to assist medical practitioners in detecting the disease at an early stage. Thus, this study designs a fuzzy analytic hierarchy process (FAHP) point-factored inference system to detect cardiovascular disease. The attributes are selected and classified into sub-attributes and point factor scale using the clinical data, medical practitioners, and literature review. Fuzzy AHP is used in calculating the attribute weights, the strings are generated using the Mamdani fuzzy inference system, and the strength of each set of fuzzy rules is calculated by multiplying the attribute weights with the point factor scale. The string weights determine the output ranges of cardiovascular disease. Moreover, the results are validated using sensitivity analysis, and comparative analysis is performed with AHP techniques. The results show that the proposed method outperforms other methods, which are elucidated by the case study.

A Hybrid Multi-Hazard Susceptibility Assessment Model for a Basin in Elazig Province, Türkiye

Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation, site selection, and planning in areas prone to multiple natural hazards. In this study, we proposed a novel multi-hazard susceptibility assessment approach that combines expert-based and supervised machine learning methods for landslide, flood, and earthquake hazard assessments for a basin in Elazig Province, Türkiye. To produce the landslide susceptibility map, an ensemble machine learning algorithm, random forest, was chosen because of its known performance in similar studies. The modified analytical hierarchical process method was used to produce the flood susceptibility map by using factor scores that were defined specifically for the area in the study. The seismic hazard was assessed using ground motion parameters based on Arias intensity values. The univariate maps were synthesized with a Mamdani fuzzy inference system using membership functions designated by expert. The results show that the random forest provided an overall accuracy of 92.3% for landslide susceptibility mapping. Of the study area, 41.24% were found prone to multi-hazards (probability value > 50%), but the southern parts of the study area are more susceptible. The proposed model is suitable for multi-hazard susceptibility assessment at a regional scale although expert intervention may be required for optimizing the algorithms.

Movie recommendation system via fuzzy decision making based dual deep neural networks

The internet and social networks produce an increasing amount of data. There is a serious necessity for a recommendation system because exploring through the huge collection is time-consuming and difficult. In this study, a multi-modal classifier is introduced which makes use of the output from dual deep neural networks: GRU for text analysis and Faster R-CNN for image analysis. These two networks reduce overall complexity with minimal computational time while retaining accuracy. More precisely, the GRU network is utilized to process movie reviews and the Faster RCNN is used to recognize each frames of the movie trailers. Gated Recurrent Unit (GRU) is a well-known variety of RNN that computes sequential data across recurrent structures. Faster RCNN is an enhanced version of Fast RCNN, it combines with the rectangular region proposals and with the features is extract by the ResNet-101. Initially, the trailer of the movie is manually splitted into frames and these frames are pre-processed using fuzzy elliptical filter for image analysis and the movie reviews are also tokenized for text analysis. The pre-processed text is taken as an input for GRU to classify offensive and non-offensive movies and the pre-processed images are taken as an input for Faster R-CNN to classify violence and non- violence movies based on the extracted features from the movie trailer. Afterwards, the four classified outputs are given as input for fuzzy decision-making unit for recommending best movies based on the Mamdani fuzzy inference system with gauss membership functions. The performance of the dual deep neural networks was evaluated using the specific parameters like specificity, precision, recall, accuracy and F1 score measures. The proposed GRU yields accuracy range of 97.73% for reviews and FRCNN yields the accuracy range of 98.42% for movie trailer.

Optimal relaying nodes selection for IEEE 802.15.6-based two-hop star topology WBAN

Wireless body area networks (WBANs) are a promising communication technology that supports various types of medical and non-medical applications with heterogeneous requirements. Generally, IEEE 802.15.6-based WBAN use one-hop star topology to establish direct communication between the hub and body sensors. The standard also supports two-hop star topology to establish communication where direct transmission between the hub and sensor nodes is not possible due to poor link quality caused by body shadowing. Data transmission reliability and network lifetime are key factors in WBANs. Therefore, we propose an optimal relaying nodes selection mechanism as an alternative to the two-hop star topology extension of IEEE 802.15.6 to enhance the packet delivery ratio, network lifetime, and throughput. In this work, while choosing a relaying node for a disconnected node, we consider the fitness value of all the relaying capable nodes and select the fittest node as relaying node. We calculate the fitness value of all the relaying capable nodes based on their link quality, residual energy, and current traffic load using the multi-criteria decision making method “Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)”, which was originally developed by Ching-Lai Hwang and Yoon in 1981. We use the Mamdani fuzzy inference system (FIS) to calculate the link quality index of relaying capable nodes based on various link quality parameters such as Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Packet Error Rate (PER). Moreover, we also designed a schedule slot allocation mechanism for all the sensor nodes based on their packet generation rate (PGR). The proposed scheme significantly improved packet delivery ratio, throughput, and network lifetime compared to the IEEE 802.15.6 standard’s two-hop extension of star topology and other relay selection mechanisms. The simulation result of the proposed mechanism shows that the packet delivery ratio and network lifetime increase by (5–30)% and 50% respectively, compared to the IEEE 802.15.6 two-hop star topology.

A Trust-Based Secure Neuro Fuzzy Clustering Technique for Mobile Ad Hoc Networks

A MANET consists of a group of mobile nodes. In a MANET, scalability and mobility have a greater influence on routing performance. The clustering technique plays a vital role in enhancing the routing mechanism and improving the network lifetime of a large-scale network like a MANET. The clustering process will degrade network performance if the malicious node is chosen as the Cluster Leader (CL). Thus, the secure clustering process in a MANET is a very challenging task. To overcome this problem, the following key factors like Trust Value (TV), Residual Energy Level (REL), and Mobility (M) of the node are used as decision-making parameters to elect a Cluster Leader (CL). In this work, we have proposed a soft computing-based neuro-fuzzy model, ANFIS-based Energy-Efficient Secure Clustering Model (ANFIS-EESC), with a primary objective of forming energy-aware stable trust-based clustering in a MANET. Moreover, we have proposed two working novel algorithms: Weight-Based Trust Estimation (WBTE) algorithm and the Fuzzy-Based Clustering (FBC) algorithm. The primary objective of the WBTE algorithm is to measure the trustworthiness of the nodes and to mitigate the malicious nodes. Fuzzy-Based Clustering (FBC) algorithm is a fuzzy logic-based cluster formation algorithm. In our proposed work, each non-CL in the system applies the cluster density of CL and mobility for each CL node using the Mamdani Fuzzy Inference system, and makes the decision to join as a member with a CL that holds maximum value. Simulation results show that the proposed work enhances the network performance by electing a more stable trust-aware and energy-aware node as Cluster leader (CL). We compare the performance parameters of the proposed work, such as packet delivery rate, energy consumption, detection rate, and reaffiliation, with the existing work, Weighted Clustering Algorithm (WCA). The network lifetime is 39% greater in the proposed ANFIS-EESC model than in the other existing work, WCA. Moreover, ANFIS-EESC shows an enhancement of 22% to 32% in packet delivery ratio and 32% and 39% in throughput. From the above analysis, it has been proved that the proposed work gives a better performance in terms of reliability and stability when compared to the existing work, WCA.

Optimized MAC Protocol Using Fuzzy-Based Framework for Cognitive Radio AdHoc Networks

Cognitive radio networks (CRNs) have been widely used in various applications for effective radio spectrum utilization in recent years. It is essential to fend off the growing demand for this finite natural resource for next-generation communications. In CRNs, detecting the activity of the primary user requires opportunistic spectrum sensing for efficient usage of the available radio spectrum, which is a limited and exquisite resource. Thus, CRNs are the key component in solving the spectrum scarcity issue in the presence of primary user bands through secondary users. Cognitive radio AdHoc networks (CRAHNs) are a unique kind of CRNs where infrastructures less cognitive radio (CR) nodes are furnished. In CRAHN, the CR-MAC protocol works slightly differently from the traditional wireless network MAC protocols. Particularly, the proposed method includes a high traffic scenario under contention-based IEEE 802.11 DCF MAC protocol. Accordingly, it can be observed that both, throughput and delay, increase as the CW size and packet length of the 802.11 (DCF) MAC protocol for CRAHN varies. Therefore, this paper proposed a fuzzy-based optimization framework for the 802.11 (DCF) MAC protocol in CRAHNs. Furthermore, it optimized throughput and delay by training a database of input parameters, contention window, and packet length for the Mamdani and Sugeno fuzzy inference system (FIS) models of the 802.11 (DCF) protocol simultaneously. The experimental result of the proposed framework for CRAHN with FIS shows that altering the contention window increases throughput by 25% and reduces the delay by 38% compared to the IEEE802.11 (DCF) protocol for CRAHNs without FIS. Moreover, it is also revealed that the throughput is increased by and 7% and the delay is reduced by 40% to 50% due to altering the packet length.