Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.

Multi-Tier Sentiment Analysis of Social Media Text Using Supervised Machine Learning

Sentiment Analysis(SA)is often referred to as opinion mining.It is defined as the extraction,identification,or characterization of the sentiment from text.Generally,the sentiment of a textual document is classified into binary classes i.e.,positive and negative.However,fine-grained classification provides a better insight into the sentiments.The downside is that fine-grained classification is more challenging as compared to binary.On the contrary,performance deteriorates significantly in the case of multi-class classification.In this study,pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored.To augment the performance,a multi-layer classification model has been proposed.Owing to similitude with social media text,the movie reviews dataset has been used for the implementation.Supervised machine learning models namely Decision Tree,Support Vector Machine,and Naive Bayes models have been implemented for the task of sentiment classification.We have compared the models of single-layer architecture with multi-tier model.The results of Multi-tier model have slight improvement over the single-layer architecture.Moreover,multi-tier models have better recall which allow our proposed model to learn more context.We have discussed certain shortcomings of the model that will help researchers to design multi-tier models with more contextual information.

Breast Calcifications and Histopathological Analysis on Tumour Detection by CNN

The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer lives.The Computer-Aided Detection(CADs)/Computer-Aided Diagnosis(CADx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer Detection and Diagnosis(BCDD)and minimise mortality by the use of medical histopathological image classification in much less time.This paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the mammogram.When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer Detection and Diagnosis,to obtain the Discriminative Localization,Convolutional Neural Network with Class Activation Map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological image.The test results indicate that this method performed almost 225.15%better at determining the exact location of disease(Discriminative Localization)through breast microcalci-fications images.ResNet50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide,and the earlier identification of skin lesions using dermoscopic images can raise the survival rate.Classification of the skin lesion from those dermoscopic images will be a tedious task.The accuracy of the classification of skin lesions is improved by the use of deep learning models.Recently,convolutional neural networks(CNN)have been established in this domain,and their techniques are extremely established for feature extraction,leading to enhanced classification.With this motivation,this study focuses on the design of artificial intelligence(AI)based solutions,particularly deep learning(DL)algorithms,to distinguish malignant skin lesions from benign lesions in dermoscopic images.This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoen-coder(OSSAE)based feature extractor with backpropagation neural network(BPNN),named the OSSAE-BPNN technique.The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region.In addition,the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis.Moreover,the parameter tuning of the SSAE model is carried out by the use of sea gull optimization(SGO)algo-rithm.To showcase the enhanced outcomes of the OSSAE-BPNN model,a comprehensive experimental analysis is performed on the benchmark dataset.The experimentalfindings demonstrated that the OSSAE-BPNN approach outper-formed other current strategies in terms of several assessment metrics.

Automatic Clustering of User Behaviour Profiles for Web Recommendation System

Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedfiltering results.The cluster user profile-based clustering process is delayed when it has a low precision rate.Markov Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Profile(UBP)model group’s similar user behavior on a dynamic update of UBP.The Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate clusters.Hamilton’s Filtering Framework(HFF)is designed tofilter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accuratefiltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining process.PURFR-GD decreases the delay time in the end-to-end workflow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized Information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model defines higher Classification Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.

Deep Learning Based Intelligent Industrial Fault Diagnosis Model

In the present industrial revolution era,the industrial mechanical system becomes incessantly highly intelligent and composite.So,it is necessary to develop data-driven and monitoring approaches for achieving quick,trustable,and high-quality analysis in an automated way.Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery.The advent of deep learning(DL)methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals.This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network(IIFD-SOIR)Model.The proposed model operates on three major processes namely signal representation,feature extraction,and classification.The proposed model uses a Continuous Wavelet Transform(CWT)is for preprocessed representation of the original vibration signal.In addition,Inception with ResNet v2 based feature extraction model is applied to generate high-level features.Besides,the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer.Finally,a multilayer perceptron(MLP)is applied as a classification technique to diagnose the faults proficiently.Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset.The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6%and 99.64%on the applied gearbox dataset and bearing dataset.The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.

Web Attack Detection Using the Input Validation Method:DPDA Theory

A major issue while building web applications is proper input validation and sanitization.Attackers can quickly exploit errors and vulnerabilities that lead to malicious behavior in web application validation operations.Attackers are rapidly improving their capabilities and technologies and now focus on exploiting vulnerabilities in web applications and compromising confidentiality.Cross-site scripting(XSS)and SQL injection attack(SQLIA)are attacks in which a hacker sends malicious inputs(cheat codes)to confuse a web application,to access or disable the application’s back-end without user awareness.In this paper,we explore the problem of detecting and removing bugs from both client-side and server-side code.A new idea that allows assault detection and prevention using the input validation mechanism is introduced.In addition,the project supports web security tests by providing easy-to-use and accurate models of vulnerability prediction and methods for validation.If these attributes imply a program statement that is vulnerable in an SQLIA,this can be evaluated and checked for a set of static code attributes.Additionally,we provide a script whitelisting interception layer built into the browser’s JavaScript engine,where the SQLIA is eventually detected and the XSS attack resolved using the method of input validation and script whitelisting under pushdown automatons.This framework was tested under a scenario of an SQL attack and XSS.It is demonstrated to offer an extensive improvement over the current framework.The framework’s main ability lies in the decrease of bogus positives.It has been demonstrated utilizing new methodologies,nevertheless giving unique access to sites dependent on the peculiarity score related to web demands.Our proposed input validation framework is shown to identify all anomalies and delivers better execution in contrast with the current program.

A Formal Testing Model for Operating Room Control System Using Internet of Things

Technological advances in recent years have significantly changed the way an operating room works.This work aims to create a platformto solve the problems of operating room occupancy and prepare the rooms with an environment that is favorable for all operations.Using this system,a doctor can control all operation rooms,especially before an operation,and monitor their temperature and humidity to prepare for the operation.Also,in the event of a problem,an alert is sent to the nurse responsible for the room and medical stuff so that the problem can be resolved.The platformis tested using a Raspberry PI card and sensors.The sensors are connected to a cloud layer that collects and analyzes the temperature and humidity values obtained from the environment during an operation.The result of experimentations is visualized through a web application and an Android application.The platform also considers the security aspects such as authorization to access application functionalities for the Web and the mobile applications.We can also test and evaluate the system’s existing problems and vulnerabilities using the IEEE and owasp IoT standards.Finally,the proposed framework is extended with a model based testing technique that may be adopted for validating the security aspects.

Effect of Poly-Alkylene-Glycol Quenchant on the Distortion, Hardness, and Microstructure of 65Mn Steel

Currently,the 65Mn steel is quenched mainly by oil media.Even though the lower cooling rate of oil compared to water reduces the hardness of steel post quenching,the deforming and cracking of parts are often minimized.On the other hand,the oil media also has the disadvantage of being ammable,creating smoke that adversely affects the media.The poly alkylene glycol(PAG)polymer quenchant is commonly used for quenching a variety of steels based on its advantages such as non-ammability and exible cooling rate subjected to varying concentration and stirring speed.This article examines the effect of PAG polymer quenching solution(with concentrations of 10%and 20%)on deformation,hardness,and microstructure of C-ring samples made of 65Mn steel.Furthermore,the performance of PAG polymer quenchant is also compared with those of two common quenching solutions:Water and oil.When cooling in water,the C-ring samples had the largest deformation and 2 times higher than the results obtained when a 10%PAG solution was used.In particular,similar levels of deformation on the C-ring samples were observed in both cases of 20%PAG solution and oil as the primary quenching media.Furthermore,the hardness level measured between the sampled parts quenched in the 20%PAG solution appeared to be more uniform than that obtained from the oil-quenched sample.The study of the microscopic structure of steel by optical microscopy combined with X-ray diffraction showed that the water hardened sample exhibited cracks and comprised of two phases,martensite and retained austenite.According to the results of Electron Back Scattering Diffraction(EBSD)analysis and backscattering electronic image(BSE),the content of austenite residue in the sample when the sample was cooled in PAG 10 and 20%solution was 3.21%and 4.73%,respectively and smaller than the measurements obtained from oil quenching solution.Thus,the 65Mn steel is cooled in 20%PAG solution for high hardness and more evenly distributed than when it is quenched in oil while still ensuring a small level of deformation.Therefore,the PAG 20%solution can completely replace oil as the main media used to quench the 65Mn steel.

QoS in FANET Business and Swarm Data

This article shows the quality of services in a wireless swarm of drones that form an ad hoc network between them Fly Ad Hoc Networks(FANET).Each drone has the ability to send and receive information(like a router);and can behave as a hierarchical node whit the intregration of three protocols:Multiprotocol Label Switch(MPLS),Fast Hierarchical AD Hoc Mobile(FHAM)and Internet Protocol version 6(IPv6),in conclusion MPLS+FHAM+IPv6.The metrics analyzed in the FANET are:delay,jitter,throughput,lost and sent packets/received.Testing process was carried out with swarms composed of 10,20,30 and 40 units;In this work,the stage with 40 droneswas analyzed showing registration processes,and sentmessages sequences between different drones that were part of the same swarm.A special analysis about the traffic between drones(end-to-end)was carried out,as well as the possible security flaws in each drone and the current status and future trends in real services.Regarding future trends,in a real environment,we took as a starting point,metrics results obtained in the simulation(positive according to the obtained results).These results gave us a clear vision of how the network will behave in a real environment with the aim to carry out the experiment on a physical level in the near future.This work also shows the experience quality from the service quality metrics obtained through a mathematical model.This quality of experience model will allow us to use it objectively in the agricultural sector,which is a great interest area and is where we are working with drones.Finally in this article we show our advances for a business model applied to the aforementioned agricultural sector,as well as the data analysis and services available to the end customer.These services available to the end customer have been classified into a basic,medium,advanced and plus level.

Clustered Single-Board Devices with Docker Container Big Stream Processing Architecture

The expanding amounts of information created by Internet of Things(IoT)devices places a strain on cloud computing,which is often used for data analysis and storage.This paper investigates a different approach based on edge cloud applications,which involves data filtering and processing before being delivered to a backup cloud environment.This Paper suggest designing and implementing a low cost,low power cluster of Single Board Computers(SBC)for this purpose,reducing the amount of data that must be transmitted elsewhere,using Big Data ideas and technology.An Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and Docker.To obtain system data and analyze the setup’s performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is employed.This Paper assesses the system’s complexity and demonstrates how containerization can improve fault tolerance and maintenance ease,allowing the suggested solution to be used in industry.An evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture,taking into consideration the suggested solution’s resource use in respect to device restrictions.

Payload Capacity Scheme for Quran Text Watermarking Based on Vowels with Kashida

The most sensitive Arabic text available online is the digital Holy Quran.This sacred Islamic religious book is recited by all Muslims worldwide including non-Arabs as part of their worship needs.Thus,it should be protected from any kind of tampering to keep its invaluable meaning intact.Different characteristics of Arabic letters like the vowels(),Kashida(extended letters),and other symbols in the Holy Quran must be secured from alterations.The cover text of the Quran and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio(PSNR)and Embedding Ratio(ER).A watermarking technique with enhanced attributes must,therefore,be designed for the Quran’s text using Arabic vowels with kashida.The gap addressed by this paper is to improve the security of Arabic text in the Holy Quran by using vowels with kashida.The purpose of this paper is to enhance the Quran text watermarking scheme based on a reversing technique.The methodology consists of four phases:The first phase is a pre-processing followed by the second phase-the embedding process phase—which will hide the data after the vowels.That is,if the secret bit is“1”,then the kashida is inserted;however,the kashida is not inserted if the bit is“0”.The third phase is the extraction process and the last phase is to evaluate the performance of the proposed scheme by using PSNR(for the imperceptibility)and ER(for the capacity).The experimental results show that the proposed method of imperceptibility insertion is also optimized with the help of a reversing algorithm.The proposed strategy obtains a 90.5%capacity.Furthermore,the proposed algorithm attained 66.1%which is referred to as imperceptibility.

Remote Sensing Monitoring Method Based on BDS-Based Maritime Joint Positioning Model

Complicated sea conditions have a serious impact on ship navigation safety and even maritime accidents.Accordingly,this paper proposes a remote sensing monitoring method based on the Beidou Navigation Satellite System(BDS)maritime joint positioning model.This method is mainly based on the BDS and multiple Global Navigation Satellite Systems(GNSS)to build a data fusion model,which can capture more steady positioning,navigation,and timing(PNT)data.Compared with the current Global Positioning System(GPS)and Global Navigation Satellite System(GLONASS)mandatory used by the International Maritime Organization(IMO),this model has the characteristics of more accurate positioning data and stronger stability.The static and dynamic measurement show that such a model works for maritime ships and maritime engineering.Combined with the Ship’s Automatic Identification System(AIS)and Geographic Information System(GIS),a BDS-based remote sensing monitoring method can cover the world,serve maritime ships and construct maritime engineering.

Cache Memory Design for Single Bit Architecture with Different Sense Amplifiers

Most modern microprocessors have one or two levels of on-chip caches to make things run faster,but this is not always the case.Most of the time,these caches are made of static random access memory cells.They take up a lot of space on the chip and use a lot of electricity.A lot of the time,low power is more important than several aspects.This is true for phones and tablets.Cache memory design for single bit architecture consists of six transistors static random access memory cell,a circuit of write driver,and sense amplifiers(such as voltage differential sense amplifier,current differential sense amplifier,charge transfer differential sense amplifier,voltage latch sense amplifier,and current latch sense amplifier,all of which are compared on different resistance values in terms of a number of transistors,delay in sensing and consumption of power.The conclusion arises that single bit six transistor static random access memory cell voltage differential sense amplifier architecture consumes 11.34μW of power which shows that power is reduced up to 83%,77.75%reduction in the case of the current differential sense amplifier,39.62%in case of charge transfer differential sense amplifier and 50%in case of voltage latch sense amplifier when compared to existing latch sense amplifier architecture.Furthermore,power reduction techniques are applied over different blocks of cache memory architecture to optimize energy.The single-bit six transistors static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique consumes 8.078μW of power,i.e.,reduce 28%more power that makes single bit six transistor static random access memory cell with forced tack technique and voltage differential sense amplifier with dual sleep technique more energy efficient.

Synchronization Phenomena Investigation of a New Nonlinear Dynamical System 4D by Gardano’s and Lyapunov’s Methods

Synchronization is one of the most important characteristics of dynamic systems.For this paper,the authors obtained results for the nonlinear systems controller for the custom Synchronization of two 4D systems.The findings have allowed authors to develop two analytical approaches using the second Lyapunov(Lyp)method and the Gardanomethod.Since the Gardano method does not involve the development of special positive Lyp functions,it is very efficient and convenient to achieve excessive systemSYCR phenomena.Error is overcome by using Gardano and overcoming some problems in Lyp.Thus we get a great investigation into the convergence of error dynamics,the authors in this paper are interested in giving numerical simulations of the proposed model to clarify the results and check them,an important aspect that will be studied is Synchronization Complete hybrid SYCR and anti-Synchronization,by making use of the Lyapunov expansion analysis,a proposed control method is developed to determine the actual.The basic idea in the proposed way is to receive the evolution of between two methods.Finally,the present model has been applied and showing in a new attractor,and the obtained results are compared with other approximate results,and the nearly good coincidence was obtained.

Modified Bat Algorithm for Optimal VM’s in Cloud Computing

All task scheduling applications need to ensure that resources are optimally used,performance is enhanced,and costs are minimized.The purpose of this paper is to discuss how to Fitness Calculate Values(FCVs)to provide application software with a reliable solution during the initial stages of load balancing.The cloud computing environment is the subject of this study.It consists of both physical and logical components(most notably cloud infrastructure and cloud storage)(in particular cloud services and cloud platforms).This intricate structure is interconnected to provide services to users and improve the overall system’s performance.This case study is one of the most important segments of cloud computing,i.e.,Load Balancing.This paper aims to introduce a new approach to balance the load among Virtual Machines(VM’s)of the cloud computing environment.The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm(BA).This proposed Modified Bat Algorithm(MBA)allows balancing the load among virtual machines.The proposed algorithm works in two variants:MBA with Overloaded Optimal Virtual Machine(MBAOOVM)and Modified Bat Algorithm with Balanced Virtual Machine(MBABVM).MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.

Fruit Image Classification Using Deep Learning

Fruit classification is found to be one of the rising fields in computer and machine vision.Many deep learning-based procedures worked out so far to classify images may have some ill-posed issues.The performance of the classification scheme depends on the range of captured images,the volume of features,types of characters,choice of features from extracted features,and type of classifiers used.This paper aims to propose a novel deep learning approach consisting of Convolution Neural Network(CNN),Recurrent Neural Network(RNN),and Long Short-TermMemory(LSTM)application to classify the fruit images.Classification accuracy depends on the extracted and selected optimal features.Deep learning applications CNN,RNN,and LSTM were collectively involved to classify the fruits.CNN is used to extract the image features.RNN is used to select the extracted optimal features and LSTM is used to classify the fruits based on extracted and selected images features by CNN and RNN.Empirical study shows the supremacy of proposed over existing Support Vector Machine(SVM),Feed-forwardNeural Network(FFNN),and Adaptive Neuro-Fuzzy Inference System(ANFIS)competitive techniques for fruit images classification.The accuracy rate of the proposed approach is quite better than the SVM,FFNN,and ANFIS schemes.It has been concluded that the proposed technique outperforms existing schemes.

Thermodynamic Simulation on the Change in Phase for Carburizing Process

The type of technology used to strengthen the surface structure of machine parts,typically by carbon-permeation,has made a great contribution to the mechanical engineering industry because of its outstanding advantages in corrosion resistance and enhanced mechanical and physical properties.Furthermore,carbon permeation is considered as an optimal method of heat treatment through the diffusion of carbon atoms into the surface of alloy steel.This study presented research results on the thermodynamic calculation and simulation of the carbon permeability process.Applying Fick’s law,the paper calculated the distribution of carbon concentration in the alloy steel after it is absorbed from the surface into the internal of the sample.Using the SYSWELD software,an analysis was performed on the carbon permeability process to determine the distribution of carbon concentrations in 20CrMo steel that was then followed by a detailed analysis of the microstructure of the sample post the carburizing process.According to the calculation results,the surface carbon content was 0.9%and steadily decreased into the core.After 3 hours,the depth of the absorbent layer was measured at 0.5 mm for both the cylindrical and cubic samples.By analyzing the phase,the distribution of martensite phases such as ferrite/pearlite and residual austenite was also determined after the carburizing process.

A Heuristics-Based Cost Model for Scientic Workow Scheduling in Clou

Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic processes.Particularly,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’requirements.However,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational time.Motivated by this,current work proposes a novel SWFS cost optimization model effective in solving this challenge.The proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization criteria.The model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research context.This will ultimately reduce the cost for service consumers.At the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service providers.To evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of datasets.The proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of SWFAs.The results reveal that the proposed cost optimization model attained an optimal Job completion time(makespan)and total computational cost for small and large sizes of the considered dataset.In contrast,hybrid and hyper-based approaches consistently achieved better results for the medium-sized dataset.

Packet Drop Battling Mechanism for Energy Aware Detection in Wireless Networks

Network security and energy consumption are deemed to be two important components of wireless and mobile ad hoc networks(WMANets).There are various routing attacks which harm Ad Hoc networks.This is because of the unsecure wireless communication,resource constrained capabilities and dynamic topology.In order to cope with these issues,Ad Hoc On-Demand Distance Vector(AODV)routing protocol can be used to remain the normal networks functionality and to adjust data transmission by defending the networks against black hole attacks.The proposed system,in this work,identifies the optimal route from sender to collector,prioritizing the number of jumps,the battery life,and security,which are fundamental prerequisites.Researches have proposed various plans for detecting the shortest route,as well as ensuring energy conversions and defense against threats and attacks.In this regard,the packet drop attack is one of the most destructive attack against WMANet communication and hence merits special attention.This type of attack may allow the attacker to take control of the attacked hubs,which may lost packets or transmitted information via a wrong route during the packets journey from a source hub to a target one.Hence,a new routing protocol method has been proposed in this study.It applies the concept of energy saving systems to conserve energy that is not required by the system.The proposed method for energy aware detection and prevention of packet drop attacks in mobile ad hoc networks is termed the Ad Hoc On-Demand and Distance Vector–Packet Drop Battling Mechanism(AODV–PDBM).