International Conference on Communication, Computing and Electronics Systems

In a software-defined wireless mesh network, a centralized manner of managing and monitoring of the network occurs. The software-defined network (SDN) is the future of the upcoming generation network paradigm by separating control plane and data plane such that network management and optimization can be conducted in a centralized manner using global network information. In this paper, we proposed a novel architecture of software-defined wireless mesh networks to identify the issues of traffic balancing introduced due to node mobility. In order to reduce the overall response time of the SDN controller in the dynamic network topology, the new model predicts the probability of the link failure in the topology. Once the link failure is predicted, alternate selection of various routes proposed through the effective stability of traffic in the network is achieved and thereby overhead of the control plane is minimized. Utilizing ns-3 to efficiently address the above problem, we can enhance the network throughput and packet delivery fraction and minimize the delay in the network. Finally, performance is evaluated via extensive simulations.

Skin is one of the main parts of the human body. At the same time, skin will be easily infected and damaged by various kinds of skin diseases. Skin disease is a major health hazard across the globe. Nowadays, many people are suffering from skin diseases. It is tedious and time consuming for doctors to manually diagnose them. Recently, machine learning techniques have been successful in the detection and recognition of different types of objects in the images which have been applied to recognize various types of diseases from the medical images. Various machine learning techniques have been used to recognize and classify skin diseases from the images. Here, three machine learning techniques support vector machine (SVM), VGGNet and Inception-ResNet-v2 have been implemented to classify seven types of skin diseases from skin lesion images. Performance of these models has been evaluated and compared by using precision and recall values. Inception-ResNet-v2 has been found to be superior based on the classification performance among these three models.

In this article, a method based on blind source separation (BSS) is applied for separating multiple scattered electromagnetic signals. For BSS, we used fast independent component analysis (FastICA) algorithm. It is shown that individual echoes from the targets can be separated from multiple electromagnetic echoes collected at different spatially separated antennas. For generating the test data, we used a numerical electromagnetic tool. It is concluded that FastICA has the potential for separating echoes from multiple targets in the area of radar systems.

Communication gap between non-hearing and hearing people results in many interaction difficulties across the globe. Indian sign language is the traditional communication alternative in our country. Recently, the integration of deep learning with convolution neural network plays a major role in solving various image classification problems. The proposed deep convolution neural network model is prepared with six convolution layers and three fully connected layers by altering different parameters. This model is evaluated based on 1000 number sign images. These datasets are created in college laboratory with 100 different students. Here, 1–10 number sign images are collected, and the proposed CNN model by six convolutional layers with 1000 epochs is applied. This study highlights the training and validation accuracy analysis as well as training and validation losses. Different performance metrics are calculated to find out the accuracy of the proposed model. The aim of developing the proposed model is to find out classification accuracy for each class. This is multiclass problems as each number signs contain 100 images. This model can predict 10 different signs classes. We have taken 200 signs from 1000 number signs for testing purpose. The results indicate that the accuracy of the proposed method is rapidly increased by increasing the epochs. We have achieved 73% average classification model accuracy.

In this paper, we have done sentiment analysis for English written Bengali words given in different online shops in Bangladesh. For this work, we have chosen four latest mobile phones popular in Bangladesh. Here, the user reviews were in Bengali words written by English characters. The data was taken from online shopping sites from Bangladesh. Here, we have assumed six different features of mobiles written in the Result section. The main objective of the study was to find out the sentiment of Bengali words written with English alphabets. As it is a trend to write such reviews in Bangladesh, the data was taken and preprocessed to fit in algorithm, and they were compared whether it is positive or negative. Python was used as simulation tool, and Pursehub was used to extract the data set, and the system successfully finds out the positivity and negativity of the reviews. This result was achieved by using confusion matrix and that is making the overall performance of those mobile handsets. Out of 1201 reviews, 599 were found to be negative and 826 were found to be positive. The F1 score was 85.25%, accuracy was achieved 85.31%, and recall rate was 84.95%.

In the age of big data, data is increasing in a tremendous way in many fields and the data shared by the users is in a great risk. To preserve privacy of an individual anonymization-based algorithm like k-anonymity-related algorithm and differential privacy is proposed to make sure that the resulting dataset is free from privacy disclosure. However, majority of these anonymization algorithms are applied in isolated environment, without considering the utility in knowledge task making the dataset less informative. Also the presence of redundant data also decreases the performance and reduces accuracy of anonymization. Hence a preprocessing-based anonymization is required to increase the utility and to achieve accuracy in anonymization. This paper aims to apply the feature selection fast correlation-based filter (FCBF) solution to select the relevant features and remove the redundant data. Then k-anonymity is applied to dataset to achieve data anonymization. Comparisons on real-world dataset were made with anonymized dataset with preprocessing and without preprocessing and result was produced.

Embedded systems or embedded devices are the basic hardware needed for Internet of things (IoT). The “Internet” and “Things” are merged together to make them work as a powerful technology called the Internet of Things (IoT). The IoT is the bombardment of the real-world objects into Internet-based things that can exchange massive amount of data with minimal human interventions. However, the security and privacy concepts of infrastructural engineering are highly critical. Smart IoT projects with smart devices are worldwide known. Smart living is a residence filled with technology that processes the information expected to respond to the needs of the occupants. It promotes the locator comfort, convenience, and security through the technology. Smart devices with the IoT services convert the raw data, read from the home sensors and the actuators, and respond with operational commands to control the home network or home appliances technology from anywhere with the help of Internet. Devices such as smartphones, tablets, and personal computers are used to communicate with these technologies. The home technology highly depends on the interoperability among the communication system architecture to achieve security. In this paper, a survey is done on how the interoperability is built with the efficient use of power energy in the existing smart living.

Opinion mining is very much attractive field inmachind learning system as it is very much needed for natural language processing. The opinion mining of Bengali written English word has been done successfully using four different classifiers—support vector machine, naive Bayes, logistic regression and random forest. For the work data set was extracted from local online shops using pursehub. The work was done with vital steps—data preparation, classifying reviews according to sentiment score and evaluate the system in all steps. The F1 score was obtained 85.25%, 88.12%, 88.12%, 82.43% for naïve Bayes, logistic regression, SVM, random forest, respectively. The accuracy score was obtained 85.31%, 88.05%, 88.11%, 81.82% for naïve Bayes, logistic regression, SVM, random forest, respectively. The precision score was obtained 85.56%, 88.54%, 87.59%, 79.14% for naïve Bayes, logistic regression, SVM, random forest, respectively. The recall score was obtained 84.95%, 88.72%, 88.80%, 85.30% for naïve Bayes, logistic regression, SVM, random forest, respectively.

The comprehensive real-time dataset is skewed. The dataset is often imbalanced and hard to classify with the existing balanced dataset. The dataset may be skewed in the range of 10:1 ratio. Due to the data imbalance and errors, the data classification accuracy rate can be reached only to 90%. The classification accuracy rate can be increased if the number of errors in dataset is minimized. Hence, in this paper, we propose to identify error in SCRUM dataset by linear neural network model. For analysis, two datasets with errors and without errors are taken and analyzed with neural network model. The model train to determine error in dataset. With the proposed method, the model determines error in dataset with 98% accuracy.

The coexistence of technologies, like big data application, cloud computing, and the numerous images in the Web has paved the need for new image processing algorithms that exploit the processed image for diverse applications. There arises a need for new image processing algorithms to utilize the processed image for diverse applications though many techniques with variations exist. Ultimately, the enduring issue is to enhance the effectiveness of huge image processing and to maintain the combination of the same with recent works. This paper presents a review of the newest progress in researches on parallel processing methods for the processing of big data. Initially, the reviews about the parallel processing methods were carried out by highlighting some promising parallel processing methods in recent studies, such as the representation of MapReduce (MR) framework, distributed, parallel methods, and Hadoop framework. Subsequently, focus on analysis and deliberations about the challenges and promising solutions of parallel computing methods on big data in various applications and on image processing applications were made and concluded with a summary of number of open problems and research areas.

Virtual reality (VR) is a creative methodological approach dedicated to preparing individuals in an intelligent VR condition. Recently, expanding consideration has been attracted to VR for the provision of fire evacuation knowledge and behavioral assessment, as they are profoundly captivating and advance driving psychological learning. The motivation behind this investigation is to characterize and organize the threats, weaknesses, strengths and the opportunities (SWOT) and their sub-factors of the VR as a predictive and effective research tool on human fire behavior. However, SWOT analysis includes no means of analytically estimating the weights defining the intensity of the factors. The proposed framework A’WOT (AHP-SWOT) integrates the analytic hierarchy process (AHP) and SWOT analysis. AHP’s connection to SWOT permits precisely and systematically determined priorities for the factors contained in SWOT analysis and makes them measurable.

For modeling the user behavior in recommender systems, the task of combining the contexts of interactions corresponds to the sequential item history has inevitable role in improving the quality of recommendations. The resort of existing recommendation models is the left-to-right autoregressive training approach. While training a certain model at a specific time step, both future (right) context/data along with the past (left) is always available in the given training set sequences. It is intuitive that the current behavior of the user has certain connections with their future actions too. Future behaviors of users can boost the quality of recommendations. In this paper, two-way sequence modeling technique is proposed for concatenating both left-to-right (past) and right-to-left (future) dependencies in a user interaction sequence. Inspired from the text modeling techniques, a Multiple interactive Bidirectional Gated Recurrent Unit (MiBiGRU) architecture is proposed to model the two-way dependencies in recommender systems. Modeling future contexts along with past contexts is an auspicious way for attaining better recommendation accuracy.

Perceptual quality of audio signals at the receiver and transmission data rate are the major concerns for the speech codec developers. But both these parameters are inversely proportional in general. In the era of 4G, 3GPP launched Enhanced Voice Services (EVS) codec which can operate in multiple data rates with a six-stage speech classifier using threshold-based GMM statistical model. In this work, we propose a seven-stage audio classifier for voiced, unvoiced, transition, multi-speaker, silence, background noise and music signals using neural network by employing Levenberg Marquardt (LM) algorithm. In comparison with conventional statistical approach that requires determination of manual thresholds, the neural network method can simplify the categorization process especially while using a large number of parameters. The categorization is done by using extracted seven features that constitute to a 32-dimensional vector. TIMIT and NOIZEUS databases are used to generate the dataset and a classification accuracy of 94% is obtained. As the network model can perform efficiently using lesser number of neurons, the complexity is also less.

Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the forecasting error, which makes this topic as an integral part of planning in many companies of various kinds and sizes, ranging from generation, transmission, and distribution to consumption, by requiring reliable forecasting systems.

The twenty-first century is consumed with the automation of the world around us. Almost everything from things as easy as walking to driving a car has been automated. But according to our study, one of the least researched areas for automation has been agriculture. There are endless possibilities to the possibilities of automating the agricultural field yet many chose not to walk down this path. Machine learning is a sub-branch of artificial intelligence, it has been applied to image processing and this intelligence is demonstrated by machines in contrast to the natural intelligence displayed by humans. The artificial intelligence can be used in many sectors like transportation, finance, health care and banking and also it can be used in image processing and it helps us to implement object detection to detect and recognize the objects from our given input images and video. This is why we have dedicated this paper to help make the life of many farmers far easier. We decided on constructing low-cost agricultural robot architecture. This robotic architecture reduces the work upon farmers and helps them survey their farm area in a matter of minutes. The robotic architecture can survey the fields and assess every tomato to identify the perfect and ripe tomatoes that can be harvested. Hence, the farmers won’t have to waste their time searching and can go only to the designated areas of their farm for the harvest. We were able to accomplish this with the help of Haar Cascade. We developed a model that can be run upon various servers such as Windows, Unix or even a Linux Server. We were able to train our model by identifying the images with a ‘positive’ and ‘negative’ label. All those images which contained a background without a tomato were labelled as ‘negative’ images, while those that contained a tomato were labelled as ‘positive’ images. The goal of our model implementing Haar Cascade was to create a negative image far larger than the positive images. We were able to make this possible by implementing large data sets that consisted of nearly 200 images of tomatoes. These large data sets are then further clearly segregated to identify the state of every tomato. Haar Cascade classifier provides high accuracy even the images are highly affected by the illumination. The Haar Cascade classifier has shown superior performance with simple background images.

Multiple passive safety systems such as helmet and seatbelt protection on two- and four-wheeled vehicles are one of the primary concern in today’s life. In this work, physical sensors have be embedded in the helmet and seat belt which will detect the human presence and control the ignition of the engine through a microcontroller. This system is connected to a microcontroller using a short-distance wireless module to transmit a signal to the receiver connected to the ignition system of the vehicle. Ignition in the engine takes place only if the sensor detects a human being wearing the helmet in two-wheelers or seatbelt in four-wheelers. It has an in-built lock system, placed near the engine which would inform the rider to wear the helmet for riding the two-wheeler or to the driver to wear the seatbelt for a four-wheeler. In case of accidents, these systems would prevent the driver from succumbing to injuries due to the absence of a helmet or the seatbelt. The helmet is wireless hardware, which is powered by a rechargeable battery. By using these systems in vehicles, the safety of the rider/driver can be improved by a great margin.

Given the importance of micro, small and medium-sized enterprises (MSMEs) in Colombia, both in terms of the number of enterprises and the generation of employment, it is important to know their nature, as well as the main determinants of their financial performance. In this sense, this paper aims to provide relevant information for the formulation of strategies, programs and public policies that promote practices within companies and thus improve the performance of this segment of organizations.

Chatbots are existing since few years and recently it has started acquiring popularity. Earlier to chatbots, people use help desk as the enquiring medium and hence people working at help desks have to work all the days and answer all the questions. Most of the queries are repetitive in nature and answers are given from a structured database. In order to reduce the effort of humans, we can have a chatbot deployed for the same activity. This work focuses on a chatbot which has been developed to provide a faster human-like interaction for admission enquiry system. The chatbot is capable of handling negative or irrelevant scenarios and responds to the queries in faster manner. Decision making by the chatbot on choosing the right set of sentences is done using LSA algorithm and cosine similarity. In addition to answering, the chatbot also maintains data of questions which is not being answered. This data can be used for future analysis for retrieval-based system. The chatbot also takes the feedback from the customers and this data can be analyzed using the feedback category report generated by the chatbot using LDA algorithm

True random number generator (TRNG) is used to generate a purely random sequence in key generation. Real-world applications use key bits or strings as a passcode to secure systems. The security of a system depends on a robust design and the ambiguity of the keys that are used so that they are unpredictable. In this proposal, TRNGs are designed using reversible gates and fault-tolerant circuits. So the chance of the TRNG hardware produces faulty output is avoided. The inputs for this system are obtained from CPU usage. The generated true random number sequence is used in generating QR codes due to the uniqueness of the generated sequence. The proposed TRNG design highlights the effectiveness of using reversible fault-tolerant gates for TRNG application over the conventional logic implementation and reversible gate design in terms of power, area, and randomness. The proposed design in 90 nm technology consumes only 25.26 µW of power.

The exponential increase of the information available in digital format during the last years and the expectations of future growth make it necessary for the organization of information in order to improve the search and access to relevant data. For this reason, it is important to research and implement an automatic text classification system that allows the organization of documents according to their corresponding category by using neural networks with supervised learning. In such a way, a faster process can be carried out in a timely and cost-efficient way. The criteria for classifying documents are based on the defined categories.

In field of modern agriculture, artificial intelligence plays a major role in crop protection. Plant diseases have always been a cause of great concern to plant growth and crop cultivation around the globe. Plant diseases can affect plants from day-to-day activities. These diseases not only have serious consequences on plants health but also on human health affecting in various ways such as spreading viruses, bacteria, and fungi causing infections. The improvement in computer vision and increasing smartphone penetration have paved the way for deep learning possible through smartphone-assisted diagnosis. Deep learning is used on a large amount of data and it is a self-learning technique. We propose an additional method to classify the diseased leaves using the transfer learning on top of convolutional neural network model to improve the efficacy of image processing while applying deep learning.

This paper presents a new character segmentation algorithm by using shape-based-oriented feature vectors for offline printed Mizo script document. The approach in this technique involves area estimation for each isolated blobs which forms a different type of shape, finding the required amount of morphological dilation based on each estimated area, training the artificial neural network (ANN) to map between the area of the shape with the required amount of morphological dilation. This experiment is performed on a font size ranging from 8 to 72, on a dataset containing 70 printed documents each having approximate 200 characters, forming a total of 14,000 characters for single font size. In total, an approximate of 14,000 × 16 = 224,000 characters is considered. The experimental results show that using feed forward back propagation (FFBP) algorithm for both segmentation and classification achieved the best result with a 97% accuracy rate of recognition.

This paper includes an energy, technical, economic, and environmental analysis on the installation of a mini-hydraulic power plant in the Dreznica village in the province of Herzegovina in Bosnia and Herzegovina. The community of Dreznica is relatively isolated since it is completely surrounded by mountains and is composed of about 1500 inhabitants. This region is already provided with an energy system and this study is proposed as an investment in a clean and renewable energy project to contribute in the reduction of greenhouse gas emission.

Recognition of lumps in the breast region is well exploited through mammography. For radiologist, the identification of cancerous tissues is tedious and time-consuming, and many automated computer-aided techniques have been proposed to enhance the clinical diagnosis. This specific research work suggests the application of neighborhood component analysis (NCA) as a feature selection technique for breast mammograms classification. Tamura features (coarseness, contrast and directionality) which provide the characteristics of image surface and objects appearance in images and statistical features (mean, variance, skewness, kurtosis, energy and entropy) were extracted from the breast mammograms, and NCA was applied to identify the best features. Finally, support vector machine classification was performed to classify abnormal condition from normal. Simulation study using the local hospital datasets revealed an overall classification accuracy of 93% by making use of quadratic kernel with SVM classifier.

In modern electronics, the continuous growth of wearable and portable devices like mobile phones, laptops and smart watches demands low-power consumption. Since flip-flop is a basic storage element of any device, flip-flop designing with low-power consumption is a very critical issue. In this paper, the design of double-edge-triggered (DET) flip-flop belonging to C-element using LECTOR technique is presented. As technology is scaling down continuously so leakage power is an important parameter on which circuit performance mainly depends, in this paper an improvement has been done by introducing effective arrangement of extra transistors in the conventional design. The conventional design and modified circuit are implemented at 45 nm CMOS technology using cadence virtuoso tool at different supply voltage varying from 0.7 to 1.1 V, and reduction in power consumption and improvement in the power delay product (PDP) is achieved as compared with the conventional design.

Many municipalities and local organizations are currently planning to improve their services by applying information and communication technologies (ICT), especially Web-based approaches, to serve citizens, business, and the government agencies as well. Besides the quality of service, cost-effectiveness is a major criterion to be met. In this paper, we present a Web-based solution to urban and suburban waste management (Internet of wasted things), which employs smart bins, a wireless sensor network, and a back-end real-time data collection and communication system for managing waste collection in a cost-effective way. Results are gained by setting up a prototype smart waste management system (SWMS) in a local community in the lower north of Thailand. The system may be employed in similar projects, where cost-effectiveness is a major criterion regarding feasibility.

In recent years, artificial intelligence (AI) has revolutionized all the management functions across the globe. AI has touched all aspects of management through automation and robotization by substituting human beings at the workplaces with greater efficiency and lower costs. Most of the repetitive and standardized jobs and some of the highly specialized jobs are now being performed by machines powered by AI. Recruitment and selection function, which is quite critical for the success of organizations, has also been significantly disrupted by AI. Now, a good number of small, medium, and large companies are using AI for staffing. Some of the finest AI-driven tools for managing the entire hiring process are available and being adopted rapidly. This research paper is a humble attempt at examining how AI-powered hiring is changing the traditional recruitment and selection processes. Besides, concerns of various stakeholders have also been discussed in the paper.

Collaborative cloud environment required trustworthiness from and to consumer and provides services associated with it. Operations related to consumer’s side are able to send task and crucial data to the cloud data center, that operation raises high trust between both the parties. Large flow of user request becomes complicated and heavy to mange in collaborative computation offered by the cloud. So, inter-service bed must provide and tackle large number of consumer request efficiently and provide or allocated trustworthy resources to associate the request. In this context, precision-based manageable service provider platform with respect to trust is emerging as a vital problem. In this paper, we have proposed a precise and collaborative trust computing mechanism by using meta-heuristic approach for trustworthy cloud computing. In initial module, inter-service platform relay response and request for managing and logging of allocated virtual machine. In the second module, resource selection by using nonlinear objective functions with the help of meta-heuristic approach to improve trust evaluation. All those operation depend on quality parameter. Therefore, in the third module, we proposed QoS prediction by appling learning approach, i.e., neural network to understand historical information in collaborative cloud computing environments. Performance analysis and experimental results verified the feasibility and effectiveness of the proposed scheme.

Private universities in Malaysia are facing difficulties in retaining Generation Y academics due to divergence in attributions with leadership styles (head of department). In this study, 150 academics were participated to determine patterns of attributions most suited transformational leadership style by using association rules technique. Results showed all the attributions have met the minimum support level which is 30% except for four attributions (seek new ways to solve problems, creative and rethinker); however according to association rules technique, these attributes have shown a unique finding whereby it found strongly significant relationship with intellectual stimulation leadership style. As a result, apart from 150, 76 Generation Y academics have chosen intellectual stimulation leadership style as their choice.

Brushless DC motor employed wide role actuator plays a significant role in many real-time applications. This paper investigates modelling and simulation of BLDC motor with an optimization algorithm for self-tuning parameters in an unknown alleyway. Grey wolf algorithm (GWA), an intelligent control algorithm is developed with the behaviour of a wolf while hunting the pathways. Further the algorithm also reduces noise cancellation that accurately reduces the impact on load during unknown alleyways. GWA is employed to acquire the gain values and self-tune parameters for the inverter to drive BLDC motor, and constant term is introduced to reduce overreach of motor speed and position of shaft. A comparative analysis is conducted among the feedback controller for BLDC motor optimization such as neuro-ANN and fuzzy-PID through simulation. Results suggest the proposed GWA algorithm holds better performance and reduce error in comparison with the other two optimization methods.

Pulse oximetry is a method of determining the oxygen saturation in the arterial blood by placing the sensor on the finger, toe, wrist or ear lobe of the subject and acts as an indicator of overall health. This research study attempts to propose a wearable wristwatch-based pulse oximeter for monitoring cardiac activities. Typical physiological parameters such as heart rate and oxygen saturation (SpO2) were estimated through laboratory-based first-level experimental settings. The pulse oximeter sensor with the real-time processor, ATmega328p was embedded into a wristwatch, and through Bluetooth mechanism, one can see the result of activities on a mobile phone or on a laptop. The use of MAXREFDES117# sensor provides an added advantage compared to the available sensors in the market with an integrated, level translator and power converter. The proposed study confirms the suitability for real-time monitoring of cardiac activities and resulted in a low-cost, user-friendly and low complex device design.

Data Science (DS) is an interdisciplinary field responsible for extracting knowledge from the data. This discipline is particularly complex in the face of Big Data: large volumes of data make it difficult to store, process and analyze with standard computer science technologies. The new revolution in Data Science is already changing the way we do business, healthcare, politics, education and innovation. This article describes three different teaching and learning models for Data Science, inspired by the experiential learning paradigm.

Cloud storage is tremendously increasing its services, huge range of storage and communication of massive data over network. This practically has an adverse effect on the way of upholding this data, when it especially comes to the privacy of the user-secured and highly confidential data. We first view you with a system that is vulnerable to this authentication protocol with its misuse of biometrics and incorrect password generates no user to lost the mobile device. We even went along with this scheme and gave out a major issue to overcome with impersonation attack. However, this scheme makes the way easy to attack for offline password guessing attack. We included a three-factor authentication which carries the smart card into card reader, gets the password and identity and conveys the user details’ requesting time. We then came up with a scheme to overcome these security flaws of this prescribed authentication scheme combining passwords, mobile devices and biometrics. The proposed system is robust three-factor authentication with the help of password, biometrics and mobile device which provides reliable security strength to the user’s data and makes counterattack to existing attack, giving with more benefits compared to the previous scheme. This scheme will not only encounters with the security issues, but also provides with most enhanced security functionalities.

The objective of this paper is to enable computers to learn on their own, identify malicious activities, increase scanner efficiency and sensitivity. The machine learning algorithm enables the identification of patterns in observed data, the development of models that explains the world and the prediction of things without explicitly preprogrammed rules and models. There have been huge research interests in the cybersecurity industry as well as in universities in the subjects of how to effectively block malicious documentation without a sign of slowing down. The main aim of the paper is to investigate the efficiency of large files and increase sensitivity in malware detection.

Predicting stock market movements has been a complex task for years by gaining the increasing interest of researchers and investors present all around the world. These have tried to get ahead of the way in order to know the levels of return and thus reduce the risk they face in investments [1]. Capital markets are areas of fundamental importance for the development of economies and their good management that favors the transition from savings to investment through the purchase and sale of shares [2]. These actions are so important that they are influenced by economic, social, political, and cultural variables. Therefore, it is reasonable to consider the value of an action in an instant not as a deterministic variable but as a random variable, considering its temporal trajectory as a stochastic process.

This paper proposes the connotation of the government Web sites’ Web page cloud archive and clarifies the archiving process of the government Web site webpage from the aspects of data collection, management, storage, utilization, and protection. On this basis, it builds the government Web site Web cloud archiving technology framework which contains the data acquisition layer, data management layer, data storage layer, data utilization layer, and application presentation layer.

The study presents the development of a platform for structuring adaptive courses based on active, reflexive, theoretical and pragmatic learning styles using artificial intelligence techniques. To this end, the following phases were followed: search, analysis and classification of information about the process of generating content for courses; analysis and coding of the software component for generating content according to learning styles; and application of tests for validation and acceptance. The main contribution of the paper is the development of a model using neural networks and its integration in an application server to determine the contents that correspond to the active, reflexive, theoretical and pragmatic learning styles.

Evaluation computing of the cultural tourism resources potential based on SVM intelligent data analysis and IoT is analyzed in this paper. The research highlights are as follows: (1) In order to effectively improve classification performance of symbol data, a symbolic space representation method is defined by deepening the spatial structure relationship between different attribute values and labels of symbol data. (2) Some parallel operators and parameters are adjusted to optimize the performance of the new algorithm. (3) In the security analysis of the actual protocols, the proof method of reduction is often adopted to reduce the security proof of the protocols to some recognized difficult problems. The simulation results prove the effectiveness of the proposed method.

The emergence of a networked social structure in the last decade of twentieth century is accelerated by the evolution of information technologies and, in particular, the Internet has given rise to the full emergence of what has been called the Information Age [1] or the Information Society [2]. Social media is yet another example of people’s extraordinary ability to generate, disseminate and exchange meanings in collective interpersonal communication with a massive, real-time networked system where everything tends to be connected. The analysis of the climate of opinion on Twitter is presented around the Common Core State Standards (CCSS), one of the most ambitious educational reforms of the last 50 years in USA.

CIGS (C = Copper, I = Indium, G = Gallium, S = Selenium) or CIS is a thin-film photovoltaic which is used for converting light into electricity. It is produced by deposition on glass or on plastic body, and there is two electrodes act as back and front contact, whose function is to collect charges. It has a high absorption co-efficiency, and for that, it can easily absorb sunlight [1]. A I–III–VI alloying semiconductor compound Cu(In,Ga)Se better known by CIGS is a composition made by copper, indium, gallium, and selenium. It is one of the most promising ingredients for CIGS solar cells in the photovoltaic industry which is better known as thin-film technology. A zinc oxide: aluminum (ZnO:Al)/zinc oxide (ZnO)/cadmium sulfide (CdS)/Cu(In,Ga)Se (CIGS)/molybdenum (Mo)/substrate-based photovoltaic has been designed in this paper, and besides an analytical numerical simulation is also performed by WxAMPS. 18.7% power conversion efficiency has been found under 1.5 spectrum at the formation of ZnO:Al/zinc oxide/CdS/CIGS/molybdenum/substrate where the applied temperature is 300 K. In the apparatus, a quasi-electrical field conducted on the part of the back contact is persuaded through the absorber, Ec, Ev exacerbated proximate to the back contact, in this manner the energy gap remains constant throughout the depth. For this reason, the conversion power efficiency is higher in CIGS thin-film solar cell. Moreover, thermal stability has been inspected in thin-film CIGS solar cell. These results will influence the future experimental work in technological optimization of CIGS solar cell.

This paper presents design of a two-stage folded cascode amplifier with CMOS Technology. Maximum DC gain is the important required factor for analog and mixed signal circuits.The proposed circuit is designed to achieve more than 100 db and the obtained DC gain is 107.615 db. Phase margin is measured as $$62.65^{\circ }$$. The power is measured as 1.97 mW. The proposed circuit is developed and performed with the technology of SCL 180 nm cadence tool. The designed values are compared with the system performance.

Remote sensing envisages the beaming of signals and the statistical processing of the inference. Traditionally, the probe signal is a radio wave, and a detector is used to anticipate the delay and direction of the reflected signal. The combination of optics (lasers, modulators, and switches) and electronics plays a key role in electro-optic devices. Analogous to electrons, the steady stream of photons in vacuity is called photonics. It is possible to construct passive devices that decompose and blend light in the optical domain. In this paper, we model the electromagnetic simulation of a set of optical devices through OptiFDTD.

Miniaturization of mechanical or electromechanical systems has paved the way to develop Micro Electro Mechanical Systems (MEMS), and they have the potentials for application in communication systems. Radio Frequency MEMS (RF-MEMS) switches can be used as an alternative to mechanical and semiconductor devices-based switches such as PIN diodes or varactor diodes for their better isolation, reduced insertion loss, low-power consumption and higher-power handling capabilities. There are various constraints involved in designing RF-MEMS switch like finite or limited time to toggle, prone to failure, power handling capacity, RF performance, material selection, etc. Hence, it is necessary to properly select key parameters and optimize the switch to achieve desired outcome for specific applications. This paper discusses design constraints and various parameters involved in designing RF-MEMS switch. From the review, it is found that shunt-type configuration of RF-MEMS switch with electrostatic actuation, capacitive contact type and bridge structure are suitable for millimetre wave applications which are explored for future bandwidth hungry communication systems.

In this paper, a multirate sampling filter is designed by using multiple constant multiplication (MCM). This is achieved by replacing the multipliers in generalized rational sampling rate converter (GRSRC) with codes derived from MCM algorithm. Using the existing generalized rational sampling rate converter structure, we achieved reduced computational complexity since the delay requirements are significantly reduced. However, in order to lower the cost of the hardware, we used a multiplierless approach. In addition, the area utilization of this modified structure has been seen to be significantly reduced in comparison to that of the unintegrated structure with a comparable trade-off in terms of the speed and power requirement. Simulink model is developed, and FPGA implementation is completed. It is seen that the computational performance and many other parameters are improved.

In this paper, multilevel inverter configuration called dual-inverter (DI) topology is presented in favor of asynchronous motor drive. The topology is easy in constriction as well as easy to operate when compared with other multilevel inverter configurations. Two special kinds of PWM methods are presented in favor of DI topology to recover the excellence of production voltage as well as decrease the common-mode voltage value. To test the concert of the PWM methods in favor of DI topology, first theoretical studies are carried and next model analysis is carried; here, MATLAB simulation model as well as outcome is presented.

This manuscript comprises a brief review of distinct tools that are used for analysing the renewable integration. Though numerous tools are used, a few are considered for explanatory purposes using the web sources of various tool developers. The details in this manuscript give the reader the necessary information to select and identify a suitable tool for renewable energy integration and its analysis for diverse objectives. This manuscript reveals that there is no tool exclusively which addresses all the problems that are related to renewable energy integration. Every objective has its own tool fulfilling its criterion. All the tools mentioned in this manuscript are related to typical applications for analysing the energy system from the state level to the national level. The details of the tools mentioned for analysis are looked at various factors like their energy sector, accounted technology, parameters, availability of tools, etc. Lastly, this manuscript provides information related to direct the decision-maker.

In most of the process industries, modelling and control is the common problem for a multi-input–multi-output nonlinear process. In this chapter, for benchmark system the three-tank system is considered. Three-tank system is majorly used for many industrial applications in various domains. The main objective is to model the system and design a controller with good transient and steady-state performance for a nonlinear MIMO process, which is a challenging task. Decentralized PI controller is applied to three-interacting cylindrical tank process. The process under study is a MIMO process with two manipulated variable and three process outputs, height of the tanks. Decentralized controller needs controller design between the most interacting pair. Mathematical modelling is obtained from the first principle theorem, state-space and transfer function methods. The interaction among all the inputs and outputs are computed by relative gain array (RGA). Condition number is computed to check whether the process is ill-conditioned or not. The PI controller applied between the most interacting loops is tuned using grey wolf optimizer and the performance studies done for servo and regulatory operation. Simulation results show the proposed controller can be implemented for a non-square matrix.

In assembling units, the PLC plays a significant role in measuring and controlling various applications like motor control, blending of fluid inside tanks, fluid level control of tanks and others. The existing PLCs are offering sluggish response and poor scanning time based on sequential processing execution. This paper has proposed various delay timer modules of PLC on reconfigurable FPGA-LabVIEW platform with IEC 61131-3 standard compliant. The GUI and LD-based input user program are created with the assistance of LabVIEW, and it is assembled with a massive parallel processing-based hardware structure inside FPGA. The TON, TOFF, RTON and RTOFF timer modules are developed with various time-based values like 1–1000 ms. Others PLC functions like AND, OR, NOT, NAND, NOR, X-OR and X-NOR, PT and OT are realized and simulated with timer module. The PLCs-based delay timer modules are fully implemented inside FPGA hardware (NI myRIO-1900). Proposed algorithm is validated on DC motor, electrical oven and single tank systems along with the comparison of SIMATIC S7-200 PLC. The proposed design offers remarkable benefits like user friendliness, cost-effectiveness, miniaturization, simplicity, faster speed and scanning time.

This research work emphasizes the effect of temperature variation on circuit metrics of distinct 1-bit full adder circuits operating at low voltage. This work is studied using 90 nm MOSFET technology. The design styles employed for constructing the adders are conventional complementary metal-oxide semiconductor (CMOS), complementary pass-transistor logic (CPTL) and transmission gate (TG). Cadence Virtuoso is used for designing and simulation of the circuits. The circuit metrics such as average power, delay and power delay product (PDP) are calculated from the simulation results. On comparison of the results, it is evident that average power and PDP of CPTL adder remain least affected by temperature, while the delay of CMOS adder remains least affected by temperature.

Security showcases a major breakthrough in the history of the embedded systems, as the connections move beyond computing devices, from intelligent traffic management systems to missile control system. The drift of Internet protocol from version 4 to version 6 has greatly expanded the number of devices that could be connected over the Internet and it is estimated that it could accommodate three times the number of devices currently existing in the world. With this growing pace in embedded systems, security issues are an area of great concern. Objective of this approach examines the vulnerability of the commonly used ARM processor to a simple distributive embedded security—power analysis attack for difference and also making the role of service provider active by modifying the conventional security model.

Fault tolerance property is incorporated in a circuit to increase its reliability. Error at the output side can be prevented by Fault-tolerant circuits. Its design enables the circuit to continue operation, at an error-free state, rather than failing completely, when some part of the circuit fails. Here, reversible adder fault-tolerant architectures of ripple carry, carry look-ahead adder (CLA) and carry skip adder (CSA) are implemented and their corresponding powers are estimated. Power efficiency and reliability of the implemented adders make them effective.

In this research work, a low-power grounded active inductor based on gyrator-C topology is proposed. The simulation results of the proposed active inductor show maximum quality factor of 341 at 2.51 GHz. The inductive bandwidth of the circuit is obtained as 0.79–2.69 GHz. The designed active inductor provides the inductance value of as high as 180 nH which makes it suitable for a wide range of applications. The circuit shows good performance in all aspects while consuming only 0.99 mW of DC power. The circuit also shows very good noise performance compared to reported works in the literature.

Modern-day DSP systems require the generation of sinusoidal or other periodic waveforms. One method of generating these signals involves “numerically controlled oscillators” (NCOs), in which a digital accumulator is used to generate sinusoidal signals using a sine/cosine lookup table for generation. This paper proposes a design based on NCO to generate a digital square wave with the provision of change of frequency and phase to any desired value with the help of a 14-bit word. The design was synthesized using the Xilinx ZedBoard FPGA development platform. The results of hardware implementation matched with the calculated (theoretical) and simulation results. This paper also presents the FPGA implementation of the proposed NCO-based quadrature wave generator that has greatly improved accuracy and precision which is also an inexpensive method.

To meet the resource demands of future wireless communications due to the increased usage of smart phones, smart devices and video-streaming platforms have led the future wireless communications to deploy dense heterogeneous Cloud Radio Access Network Systems (C-RANs). The heterogeneous communication environment offers fine-grained uniform experience to its subscribers and low-cost deployment irrespective of user location in the communication environment. The C-RANs have emerged as one of the promising solution to meet the operational cost, Quality of Service (QoS), and compression of baseband data requisite. This work, considers implementation of C-RAN model where baseband unit (BBU) and Remote Radio Heads (RRH) are connected through Common Public Radio Interface (CPRI) Fronthaul links. For such networks, reducing the data rate compression is very essential as the Fronthaul links capacity is limited and costly as they transport complex baseband samples. Fronthaul compression exploits the spatial and temporal behavior of time domain LTE signals for reducing the data rates has been considered by the existing models nonetheless it remains a challenge. To overcome the research challenge in building better transmission model, this work considers jointly exploiting both spatial and temporal correlations of the transmitted baseband signals to obtain efficient Fronthaul compression performance for LTE cellular networks using Refined Huffman. This work, assumes a case similar to massive Multiple-Input Multiple-Output (MIMO) communication mobile environment, where number of receiving antennas will outnumber the active user terminals. Our model applies Low-Rank (LR) approximation of complex baseband samples to obtain spatial and temporal correlations construction matrices of signals. The correlated baseband signals are then encoded using proposed refined Huffman encoder technique (RHCT) to achieve better compression. Experiments are carried out for evaluating the performance attained by the proposed method with Standard Huffman. The results obtained displays, that the proposed model attains superior performance enhancement than the existing state-of-the-art Huffman encoder model in terms of Symbol Error Rate (SER), Bit Error Rate (BER), Compression, and Throughput (Sum rate).

Resource management becomes an essential task in our day-to-day lives. Particularly, a few types of assets like fuel which is non-inexhaustible in nature should be overseen in a legitimate path so as to maintain a strategic distance from efficient misfortune. In today’s digital world, there should be automatization in Indian forces for fleet management which will greatly help them in maintaining equipment sustenance. The idea is specifically employed for Indian forces by developing three main modules: GPS tracker, weight measurement, and fuel level indicator. We have built up this system to continuously monitor the status of a vehicle by using GPS tracker. Weight sensor is used to measure the weight of the vehicle through which we can get direct information in case of smuggling of weapons. While transmitting fuel to another medium, in order to prevent losses of fuel, we have used fuel level indicator to sense the basic level of medium. The motivation behind developing such a system is to prevent smuggling and losses of fuels and weapons in Indian forces. This solution enhances security as it is accessed remotely over the fleet by an authentic person by using the Internet of Things (IoT) model.

In the recent times, IoT is playing a major role in the development of agriculture. Due to the enormous growth of IoT, smart farming is becoming an emerging concept as IoT is capable enough to provide information about the agriculture fields. This work focuses on greenhouses which can be used in growing plants under certain circumstances. Such plant life can be monitored on a regular basis so as to increase the yield with high quality and quantity. This paper aims to provide farmers with an IoT-based Web application for monitoring the agriculture fields and its conditions. With the arrival of open supply Arduino Uno boards beside low-cost wet sensors, it is feasible to make devices that monitor various sensors like temperature/humidity sensor, soil moisture sensor, ultrasonic sensor, PIR sensor, pressure sensor, and light level sensor for consequently irrigating the fields when required.

The advent of the concepts of Internet of Things has revolutionized and impacted not only the information technology sector but also finance, manufacturing, and the home. The penetration of wireless communication technologies based on mobile networks, bluetooth, and radio-based communication such as Wi-fi, RFID, and NFC has significantly contributed to widespread adoption and acceptance. This research tests the validity of an IoT enabled “marionette” which is armed with an array of sensors, and actuators to achieve easy communications between the user and a home automation system. The system is an integration of a number of IoT modules that provides an automation for several tasks including home utilities, personal/banking information dissemination mechanism, and security. The analysis of the system based on the comparison with traditional one and the power consumption evaluation depicted the need to implement this system for smarter, safer, and easy living in homes.

Internet of things (IoT) is the finest metric following technology that turns the attention of the people throughout the world. IoT is a global connecting network that allows people to connect with each other largely. It is a challenging technology due to its complex environment and resource-dependent features. Different surveys depict that there may be several billions of IoT users by 2020. There are numerous companies that offer IoT services. Nowadays, the security of an IoT feature is an issue which is non-measurable in nature. Different research works are being performed to find the optimal solution to provide security of IoT. IoT requires less human commanding and controls which results in vulnerability due to hacking. In this paper, the literature is reviewed based on the types of routing attacks in wireless sensor network interface layer communication and classified the attacks that disturb the communication. These attacks are classified as attacks on topology, resources, and traffic. Based on the classification of attacks, countermeasures are suggested to protect the routing standard for the IoT environment.

Secrecy communication is promising particularly for wireless systems due to the transmission environment of the radio path, which is simply interrupted. Wireless security methods have classically improved for conventional wireline applications, and these techniques are not assumed substantial properties of the wireless channels. To overcome these problems, in this research work a foundation was developed to introduce and examine the wireless networks inhibiting confidentiality presented via node spatial distribution, wireless propagation medium and combined network interference. This work proposed an approach, called as blowfish algorithm and secure hash algorithm (SHA), for the security which are inner qualities. Blowfish, a 64-bit symmetric block cipher which utilizes a key having the variable length from 32 bits to 448 bits, includes 16 rounds and produces the key-dependent S-boxes. It is faster speed for the procedure of encryption/decryption of group communication information from the given network. A hybrid method blowfish with SHA is proposed in this work to improve the security for ensuring secrecy from the eavesdroppers. It is used to ensure the higher security in terms of reliability and security in the given network by using efficient cryptography algorithm. The result shows that the formulated system produces higher efficiency in terms of better security rather than the existing system. The proposed hybrid BF + SHA algorithm provides higher ratio of packet delivery, average delay and throughput than the existing system.

Healthcare system providing cloud-based storage has the potential to store the patient’s therapeutic records to the remote server than maintaining the files and radiological images on a hard drive or local storage device that enables the patient to access their medical records at any place by using the Internet through a Web-based application. The structure of cloud application guarantees the privacy and security of health-related data to preserve the sensitive health information. The architectural design of cloud computing aims to alleviate the privacy concern and to fulfill the confidence and trust of the cloud-based healthcare organization. The requirements, architecture design, software components, and validation methods of cloud-based healthcare system are discussed.

Cognitive radio sensor networks (CRSNs) is a branch of wireless sensor networks (WSNs) where cognitive intelligence is used in order to utilize the underutilized spectrum. Imbibing cognitive intelligence in WSNs can overcome various drawbacks faced by traditional WSNs as cognitive technology has an ability to adapt to surrounding environment, thereby effectively utilizing the electromagnetic spectrum. In order to have an effective communication between nodes of CRSN, an energy-efficient and adaptive medium access control (MAC) layer protocol is necessary. In this work, a novel clustering algorithm called interference-aware cluster formation in CRSNs (IACFC) is proposed. This mechanism selects vacant channels for data transmission based on optimal number of clusters formed. We identify the vacant channels based on channel availability and channel overlapping probability. Binomial distribution is used to allocate the idle channels to cognitive users. Simulation results show that IACFC has a better performance in comparison with existing clustering algorithms in CRSNs.

In cloud computing paradigm, the management and utilization of infrastructural resources are a challenging process to service providers as well as the cloud users. A cloud user accesses services based on the service provider’s pay and use strategy. In this case, the users fail to utilize the computational resources effectively; it does not only lead to paying more, but also degrade the performance of the cloud data center. To overcome these inopportune situations, we propose a conceptual framework with a heterogeneous environment in a cloud data center. This model is proposed to enhance the management of infrastructural resources by provisioning virtual machines effectively and to improve the efficiency of resource utilization of applications running inside virtual machines (VMs). This paper presents the initial framework for data center setup with the results.

Energy management in educational institutions is a much needed requirement due to the random energy needs and changing occupational behavior. A smart energy management system will be able to counter the different energy needs of the university building so that the energy needs are reduced to a minimum. Hence, a smart energy framework is proposed by comprising several layers of information transfer, and algorithms for smart light and consumption control based on the data are discussed.

Wireless local area network is a data transmission technique developed mainly to allocate location-independent network system linked between devices by utilizing radio frequency waves rather than cable infrastructure. The standard protocol used for wireless local area network is IEEE 802.11. The wireless technology offers the capability to allow communication between two or more bodies over distances without the use of wires or cables. LNA is the active block in radio transceiver systems. The design specifications for LNA are generally dependent upon the value of S parameters, power consumption, noise figure, linearity, and gain of transistor. This paper will review the design parameters as well as provide comprehensive review of the existing topologies used for LNA in WLAN applications.

Mobile robots that addresses the automation needs are extensively used in service-based environments like warehouses, hotels, hospitals, restaurants etc. Path planning to reach destination from a source is a crucial task for any mobile robot. An idle path planning algorithm given a source and destination, should plan a shortest path with less computation time autonomously. In this work Quick Response (QR) code is used as via point/landmark in the environment. QR code is a 2D barcode which stores more data than a traditional barcode system. Path matrix which contains navigation information to reach nearby via points or destinations is stored in QR code. These QR codes are placed on the floor strategically, such that given a destination, a mobile robot can navigate in a shortest route possible without any overall environment information. The proposed system is implemented and tested using an in-house built omnidirectional mobile robot powered by BeagleBone Black. The obtained results proves that the mobile robot is able to reach its destination point accurately.

Internet of Things (IoT), the modern approach of science and technology, is currently experiencing a rapid evolution in the area of research and industry, but it still suffers from various security issues [1]. In an IoT environment, privacy is the prime concern, and the information is being handled mainly through the decentralized topology [2]. The commonly utilized methods for communicating and fetching data are carried out through the Internet by utilizing different types of smart devices that have the ability to go around the Internet into an unsafe platform. It is due to the receptiveness and heterogeneousness humor of IoT terminals, where they are uncovered to attackers by making its user’s identity easily traceable and for the most part, is not secured-by-design [3]. It is essential for an upgradation, to fix their vulnerabilities and to anticipate attackers from enlisting them into botnets. This paper is based on the relevant security issues and confidential challenges upon various vulnerabilities of Internet services for IoT devices by presenting some typical attack cases with an outlook on possible solutions that are carried out in a systematic literature review. We have focused on the privacy issue that prevails in the IoT environment and thereby proposed an appropriate security model through Blockchain strategy for establishing a secure and reliable communication among peers.

The inter-integrated circuit is a serial, half-duplex protocol. It is a synchronous device that can implement multi-masters and multi-slaves. The masters are the controlling blocks that initiate all activities of the slaves. It is important to ensure that this communication is secured. This paper aims at a simplistic approach to lock the clock line of the I2C using logic gates so as to protect the information passing through the data lines. It makes use of a four-digit hexadecimal password. This allows up to 65,536 combinations enhancing the security and to gain additional control over I2C. This proposal uses a concept of logic locking to gain security.

A new technique to design single-fed circularly polarized (CP) microstrip patch antenna is presented in this paper. The estimation of bandwidth enhancement is based on dimension adjustment of the fractal defected ground structure (FDGS). Then, by inserting circular slot on the patch, circular polarization (CP) is obtained. The parametric optimization is done in ANSYS HFSS simulator in order to fix the dimensions of the microstrip patch antenna. The microstrip patch antenna with third iterative FDGS is fabricated and measured. The bandwidth of the measured microstrip antenna with CP radiation is about 120 MHz (2.13–2.25 GHz) and axial ratio of 2.3 dB to ensure circular polarization.

Automobile theft is a worldwide immense problem. A vehicle of top-notch security features is usually higher in the cost, and it cannot be afforded by middle-class people. By considering all these parameters, we aimed to design a low-cost, real-time, robust security system for vehicles. The main purpose of this project is to notify the vehicle owner, when the vehicle is moved/theft from the parking area and to monitor the movement of the vehicle in real time. Raspberry Pi 3 board connected with SIM908 GPS module will be placed in the vehicle. GPS module receives the signal and provides the data in NMEA format. Out of several messages, GPRMC message will be sent from the board to the cloud. GPRMC message type has much information like latitude, longitude, speed, time, etc. Raspberry Pi 3 which is connected to mobile hotspot will communicate with the cloud using MQTT protocol. MQTT protocol is a lightweight protocol which comes on top of TCP/IP. The web application will be connected to the cloud by providing configuration details. Once the web application is subscribed to the GPS topic, it starts getting the GPS data which will be displayed on the web page.

The Internet of Things (IoT) is an exploding market as well as an important focus area for research. Security is a major issue for IoT products and solutions, with several massive problems that are still commonplace in the field. In this paper, we have successfully minimized the risk of data eavesdropping and tampering over the network by securing these communications using the concept of tunneling. We have implemented this by connecting a router to the Internet via a virtual private network while using PPTP and L2TP as the underlying protocols for the VPN and exploring their cost benefits, compatibility and most importantly, their feasibility. The main purpose of our paper is to try to secure IoT networks without adversely affecting the selling point of IoT.

In past few years, size of the data is growing exponentially by extreme fast rates, for instance, the size of data growth was ten times faster in the growth due to various means such as the data from mobile devices, remote sensing, sensing aerial devices, recording frequency of radio waves. Until most recently, most of the data was never analysed and most of the time it was discarded. The data stored requires lots of storage space whereas later due to lack of storage space the data is either ignored or deleted due to lack of storage space to process the data. Sometimes, we are even capable of storing the data but until that data is not processed, it is raw useless data to us because that will not be able to fetch with new insights. In the analysis, we face two types of challenges, first is the lack of storage space and second a suitable software to process this data. In this paper, we have discussed about the evolution of 4 V’s of big data, levels of big data tools, various data tools along with a comparative analysis of those tools on the basis of distinguished features like mode of software, data processing, language support, data flow security, latency and fault tolerance is also generalized for better understanding.