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With the recent explosion in the number of wireless communication technologies, the frequency spectrum has become a scarce resource. The need of the hour is an efficient method to utilize the existing spectrum and Cognitive Radio is one such technology that can mitigate the spectrum scarcity. In a cognitive radio system, the unlicensed secondary user accesses the spectrum allotted to licensed primary users when it lies vacant. To implement dynamic or opportunistic access of spectrum, secondary users perform spectrum sensing, which is a quintessential part of a Cognitive radio. From the Cognitive user’s point of view, lesser error probability means an increased likelihood of channel reuse when it is vacant, and a higher detection probability signifies better protection to the licensed users. In both cases the decision threshold plays a pivotal role in determining the fate of the unused spectrum. In this paper, we study the difficulty of selecting an appropriate threshold to minimize the error probability in an uncertain low SNR regime. The sensing failure issue is analyzed, and an optimal threshold is computed that yields minimum error rate. An adaptive double threshold concept has been proposed to make the detection robust and a closed-form equation for optimal threshold has been derived to minimize the error. The novel findings through simulation results exhibit improvement in Probability of detection and reduction in probability of error at low SNR in the presence of noise uncertainty factor.

Artificial intelligence has paved the way for different areas of computing such as speech recognition and translation, object detection, machine translation, and others. One of the goals of artificial general intelligence is to simulate human thinking and rationality within machines such that they are able to perceive their environment and then perform reasonable actions based on their perception. Creating a single model that performs every single task from visual perception to actuation is currently impossible. The system must be divided into several models each of which functions independently as well also contribute to the operation of the whole intelligent machine. In this paper, an intelligent sensing and caption generation (ISCG) system is proposed which is capable of detecting living/non-living objects and states of motion in images. The system consists of two separate modules of caption generator and intelligence engine with a Convolutional Neural Network (CNN) for determining the different objects in the images. Our model yields state-of-the-art performance on benchmarked dataset.

Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and pose an eminent challenge to the movement of emergency vehicles. To achieve better driving we proceed towards a trending research field called Social Internet of Vehicles (SIoV). A social network paradigm that permits the establishment of social relationships among every vehicle in the network or with any road infrastructure can be radically helpful. This holds as the aim of SIoV, to be beneficial for the drivers, in improving the road safety, avoiding mishaps, and have a friendly-driving environment. In this paper, we propose a Dynamic congestion control with Throughput Maximization scheme based on Social Aspect (D-TMSA) utilizing the social, behavioral and preference-based relationships. Our proposed scheme along with the various social relationship types allocates green signal to maximize the traffic flow passing through an intersection. Simulation results show that the D-TMSA outperforms the existing work by achieving high throughput, lowering the total traveling time and reducing the average waiting time to better the flow of traffic based on their social attributes with each other.

Over the last few years, deploying data to cloud service for repository is an appealing passion that avoids efforts on significant information sustenance and administration. In distributed repository utilities, deduplication technique is often exploited to minimize the capacity and bandwidth necesseties of amenities by erasing repetitive data and caching only a solitary duplicate of them. Proof-of-Ownership mechanisms authorize any possessor of the identical information to approve to the distributed repository server that he possess the information in a dynamic way. In repository utilities with enormous information, the repository servers may intend to minimize the capacity of cached information, and the customers may want to examine the integrity of their information with a reasonable cost. We propose Secure Deduplication and Virtual Auditing of Data in Cloud (SDVADC) mechanism that realizes integrity auditing and deduplication of information in cloud. The mechanism supports secure deduplication of information and effective virtual auditing of the documents during the download process. In addition, the proposed mechanism lowers the burden of dataowner to audit documents by himself and there is no need to delegate auditing to the Third Party Auditor (TPA). Experimental results demonstrate that the virtual auditing has low auditing time cost relative to the existing public auditing schemes.

The inductive link in a wireless power transfer (WPT) system, fed by various high-frequency power converters with appropriate control strategies is widely accepted for medium power ( 95%) in static wireless charging scenarios. However, considering the dynamic wireless charging, the inductive link implementation for millions of kilometers is quite expensive due to the need for sophisticated resources. Therefore, relatively low-cost capacitive link, which initially focused on low power ( 90%) and significant misalignment tolerance. This paper aims to find the significant roles played by inductive and capacitive ac link designs in static and dynamic expansion of wireless electric vehicle (EV) charging. Through a relative analysis, this paper emphasizes the need for mutual promotion of these ac links as a hybrid system in order to boost wireless EV charging technology.

Internet of Things (IoT) paradigm connects physical world and cyberspace via physical objects and facilitate the development of smart applications and infrastructures. A physical object is the basic constituent of IoT, often called as smart object, that interact with other objects and possess the information processing abilities. The smart objects when deployed in the real world, collect information from the surrounding environment and this is abstracted as a service. IoT has established a universe where humans are provided smart data services by the fusion of physical objects and information networks. This approach has been extended to include social networking aspects in the IoT that autonomously build social relationships to discover objects and their services viz; Social Internet of Things (SIoT). SIoT enhances information sharing, supports new applications and provide a reliable and trustworthy networking solutions utilizing the social network of friend objects. In this paper, we present the fundamentals of SIoT, identify thrust areas of it (as service discovery and composition, network navigability, relationship management, and trustworthiness management) and present several prerequisites, challenges and use case scenarios based on them. State-of-the-art research publications are reviewed on service discovery, relationship management, service composition and trust management constituents of the SIoT environment. Finally, we identify and discuss the future research directions that serves as a reference for the next generation discovery techniques to improve service provisioning, find the optimal solution for the link selection in the SIoT structure, develop large scale platforms and provide a smart mechanism for trust evaluation.

TCP is the dominant protocol in the Internet and delay friendliness of TCP is a well studied, complex and a practical issue. While all other components of end-to-end delay have been adequately characterized, the issue regarding queuing delay and the number of re-transmissions which are random in nature, add to uncertainty in the Internet. The datagrams at the router are from number of multiplexed flows’ M(n) which constitute a stochastic process. This paper examines queuing behavior subject to multiplexing a stochastic process M (n) of flows’. The arrival and service processes at the bottleneck router have been mathematically modeled accounting for different proportions of TCP and UDP with their datagram sizes as parameters. This model is then used to evaluate the mean queuing delay for datagrams for different fractions of TCP in the background traffic. Further using discrete queue analysis of datagrams, estimation of average instantaneous delay for datagrams of a tagged flow has been derived to ascertain the delay impact of multiplexing of flows’ on datagrams of a tagged flow by fellow flows’. The analysis divulges an intriguing behavior that flows’ multiplexing ‘harms’ the queuing delay. It has been observed that mean queuing delay for datagrams and average instantaneous delay for datagrams of a typical flow get degraded with the increased number of fellow TCP flows’ in the background traffic.

Mining opinions from online reviews is an essential step in obtaining the overall sentiment of a product. Deep learning procedure is applied over various fields. User ratings are huge for recommender structures since they consolidate various kinds of energetic information that may influence the exactness of the suggestion. In this work, a deep learning model is utilized to process the user remarks and to create a potential user rating for user comments is proposed. To start with, the system uses sentiments to create a feature vector as the input nodes. Further, the framework tools reduce the noise in the dataset to recover the classification of information mining. To finish, Deep Belief Network (DBN) and sentiment analysis reaches data learning for the approvals.

In this day and age, Artificial Intelligence has crept into every domain of work. It is only fitting that it be made a part of the medical domain as well. With the vast amount of healthcare data available in various forms such as image, video, text, and due to emerging technologies and advancements in data science, tools are being created to ease the process of medical analysis and provide an efficient and accurate diagnosis of the patient. Such techniques save lots of human efforts and provide a rather accurate result. In particular, the chapter focuses on emphasizing the importance of multimodal system where instead of relying on a particular data source or a particular field of data analytics; one can combine multiple sources and apply multi-domain techniques to extract information to an even greater extent. The high-quality information retrieved from the analysis can be further used to determine and diagnose more symptoms and hence help in providing accurate solutions.

As Internet of Things (IoT) advances from interacting objects to smart objects. Further, adopting social networking concepts into IoT leads to social objects. Following this conception, the Social Internet of Things (SIoT) comprising the amalgamation of social aspects into IoT was incised. In the recent past, the use of IoT technologies in the industrial sectors and applications has given birth to a new research area termed as the Industrial Internet of Things (IIoT) which has attained notable attention extensively. In machinery-based industries, elements such as bearings, axles, brackets, hangers, etc., are the essential machine element that serves a variety of purposes in the production environment. Thus, any severity related to it causing failure is unfavorable. This paper introduces the SIoT concept into the manufacturing industry for predicting the failure of the machine element before it could occur. This work proposes the construction of the ontological model that assists in the implementation of the prognostics and health management to predict the remaining useful life (RUL) of machine element, which is essential in minimizing maintenance cost and machine downtime, and further limiting disasters.

Cloud repository is one of the most important services afforded by Cloud Computing where information is preserved, maintained, archived in distant servers and made available to the users over the Internet. Provided with the cloud repository facilities, customers can organize themselves as a cluster and distribute information with one another. In order to allow public integrity auditing on the information stored in semi-trusted cloud server, customers compute the signatures for every chunk of the shared information. When a malicious client is repudiated from the group, the chunks that were outsourced to the cloud server by this renounced customer need to be verified and re-signed by the customer present in the cluster (i.e., the straightforward approach) which results in huge transmission and reckoning cost for the customer. In order to minimize the burden of customers present in the cluster, in the existing scheme Panda, the semi-trusted Cloud Service Provider (CSP) is allowed to compute the Re−sign key. Further, the CSP audits and re-signs the revoked customer chunks by utilizing the Re−sign key. So, it is easy for the CSP by colluding with the revoked customer to find the secret keys of the existing customer. We introduce a novel Collusion Resistant User Revocable Public Auditing of Shared Data in Cloud (CRUPA) by making use of the concept of regression technique. In order to secure the secret keys of the existing customers from the CSP, we have allowed the information proprietor to compute the Re−sign key using the regression technique. Whenever the information proprietor revokes the customer from the cluster, the information proprietor computes the Re−sign key using the regression technique and sends to the CSP. Further, the CSP audits and re-signs the revoked customer chunks using the Re−sign key. The Re−sign key computed by the information proprietor using regression method is highly secure and the malicious CSP cannot find the private information of the customers in the cluster. Besides, our mechanism achieves significant improvement in the computation cost of the Re−sign key by information proprietor. Further, the proposed scheme is collusion resistant, supports effective and secure customer repudiation, multi-information proprietor batch auditing and is scalable.

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm.

Internet of Things (IoT) paradigm links physical objects in the real world to cyber world and enables the creation of smart environments and applications. A physical object is the fundamental building block of the IoT, known as a Smart Device , that can monitor the environment. These devices can communicate with each other and have data processing abilities. When deployed, smart devices collect real-time data and publish the gathered data on the Web. The functionality of smart devices can be abstracted as a service and an IoT application can be built by combining the smart devices with these services that help to address challenges of day-to-day activities. The IoT comprises billions of these intelligent communicating devices that generate enormous amount of data, and hence performing analysis on this data is a significant task. Using search techniques, the size and extent of data can be reduced and limited, so that an application can choose just the most important and valuable data items as per its necessities. It is, however, a tedious task to effectively seek and select a proper device and/or its data among a large number of available devices for a specific application. Search techniques are fundamental to IoT and poses various challenges like a large number of devices, dynamic availability, restrictions on resource utilization, real time data in various types and formats, past and historical monitoring. In the recent past, various methods and techniques have been developed by the research community to address these issues. In this paper, we present a review of the state-of-the-art search methods for the IoT, classifying them according to their design principle and search approaches as: IoT data and IoT object-based techniques. Under each classification, we describe the method adopted, their advantages and disadvantages. Finally, we identify and discuss key challenges and future research directions that will allow the next generation search techniques to recognize and respond to user queries and satisfy the information needs of users.

A substitute solution for various organizations of data owners to store their data in the cloud using storage as a service(SaaS). The outsourced sensitive data is encrypted before uploading into the cloud to achieve data privacy. The encrypted data is search based on keywords and retrieve interested files by data user using a lot of traditional Search scheme. Existing search schemes supports exact keyword match or fuzzy keyword search, but synonym based multi-keyword search are not supported. In the real world scenario, cloud users may not know the exact keyword for searching and they might give synonym of the keyword as the input for search instead of exact or fuzzy keyword due to lack of appropriate knowledge of data. In this paper, we describe an efficient search approach for encrypted data called as Split Keyword Fuzzy and Synonym Search (S K F S). Multi-keyword ranked search with accurate keyword and Fuzzy search supports synonym queries are a major contribution of SKFS. The wildcard Technique is used to store the keywords securely within the index tree. Index tree helps to search faster, accurate and low storage cost. Extensive experimental results on real-time data sets shows, the proposed solution is effective and efficient for multi-keyword ranked search and synonym queries Fuzzy based search over encrypted cloud data.

To conserve the deployed information in cloud repository contrary to adulterations, including fault toleration to cloud repository together with information integrity verification and failure restoration becomes important. Belatedly, reconstructing codes acquire recognition because of their reduced reparation bandwidth while ensuring fault toleration. Prevailing distant auditing schemes for reconstructing-coded information authorizes an intermediary to reconstruct authenticators and information blocks on the suspended servers in the course of the reparation process. To address this issue, we propose Secure Regeneration of Corrupted Blocks by TPA in Cloud (SRCBT). We eliminate the semi-trusted proxy and allow the TPA to reconstruct authenticators and information chunks on the suspended servers in the course of the reparation process. Thus, our mechanism can totally relinquish information proprietors from online burden. The proposed scheme preserves privacy, TPA efficiently reconstructs authenticators and information chunks on the suspended servers in the course of the reparation process and also effectively performs batch auditing. The performance analysis shows that our mechanism is extremely effective and can be conceivably blended into the reconstructing-code-based distributed repository.

Search engines are an indispensable part of a web user's life. A vast majority of these web users experience difficulties caused by the keyword-based search engines such as inaccurate results for queries and irrelevant URLs even though the given keyword is present in them. Also, relevant URLs may be lost as they may have the synonym of the keyword and not the original one. This condition is known as the polysemy problem. To alleviate these problems, we propose an algorithm called automatic discovery and ranking of synonyms for search keywords in the web (ADRS). The proposed method generates a list of candidate synonyms for individual keywords by employing the relevance factor of the URLs associated with the synonyms. Then, ranking of these candidate synonyms is done using co-occurrence frequencies and various page count-based measures. One of the major advantages of our algorithm is that it is highly scalable which makes it applicable to online data on the dynamic, domain-independent and unstructured World Wide Web. The experimental results show that the best results are obtained using the proposed algorithm with WebJaccard.

Owing to the steep increase in the Internet population, the content over the web is increasing exponentially so as Community Question Answer (CQA) have acquired very huge amount of questions and answers. In this article, a machine learning algorithms are utilized for Question Classification (QC) and Answer Classification (AC). We identify the category of the question posted and further map with the corresponding question. Similarly for the answers posted by the multiple user will be processed for category mapping. Here the result shows the effective classifier that can be chosen to perform the mapping task for both Question classifier as well as answer classifier. Here the results shows that, for Question Classification (QA), Linear Support Vector Classification (LSVC) is found to be best classifier and Multinomial Logistic Regression (MLR) is most suitable for Answer Classification (AC). Using the probability of overall possible outcomes of a particular answer will give a best answer. Experiments results shows that our method outperforms efficiently.

Database outsourcing is one of the important utilities in cloud computing in which the Information Proprietor (IP) transfers the database administration to the Cloud Service Provider (CSP) in order to minimize the administration cost and preservation expenses of the database. Inspite of its immense profit, it undergoes few security issues such as privacy of deployed database and provability of search results. In the recent past, few of the studies have been carried out on provability of search results of Outsourced Database (ODB) that affords correctness and completeness of search results. But in the existing schemes, since there is flow of data between the Information Proprietor and the clients frequently, huge communication cost prevails at the Information Proprietor side. To address this challenge, in this paper we propose Efficient Auditing for Outsourced Database with Token Enforced Cloud Storage (EAODBT). The proposed scheme reduces the large communication cost prevailing at the Information Proprietor side and achieves correctness and completeness of search results even if the mischievous CSP knowingly sends a null set. Experimental analysis show that the proposed scheme has totally reduced the huge communication cost prevailing between Information Proprietor and clients, and simultaneously achieves the correctness and completeness of search results.

With the deeper penetration of the Internet of Things (IoT) devices into the physical infrastructure and wider acceptance of IoT technologies by the community has created a tremendous opportunity for designers and developers to put forth applications that aim to improve the present state-of-the-art solutions. Location-based Service (LBS) provisioning is one such area where location data is utilized to offer user-centric services and thus improve personalized user experience. In this paper, we propose a search framework for the IoT ecosystem that offers location-aware services based on data messaging and aggregation techniques. We design a taxonomy for the classification of the IoT devices based on their mobility frequency and leverage it to design a priority scheme to address the co-located devices that offer similar services. Experimental results show that our proposed LBS Provisioning System is more effective in term of query resolution and storage requirements when compared to several existing works.

With the cloud repository service furnished by the cloud computing, users can comfortably arrange themselves as a cluster and distribute information effectively. In order to empower public verifier to audit the distributed information, clients in the cluster need to Figure out signatures on complete chunks of collaborative information. Every client in the cluster modifies and signs his respective chunks, and deploys in the cloud server. Hence specific chunks of shared information are normally signed by specific clients. If anyone of the customers’ is found malicious, he is immediately repudiated from the cluster. The prevailing clients in the cluster are permitted to re-sign the chunks that were earlier signed by this eliminated client. This approach is inefficient due to the massive amount of collaborative information in the cloud. By exploiting the approach of proxy re-signatures, the CSP is acknowledged to re-sign chunks in support of the prevailing clients during customer repudiation. When many clients deploy the same information to the cloud repository, repository space has identical copies, hence deduplication technology is usually utilized to lower the capacity and bandwidth prerequisites of the utilities by removing repetitious information and hoarding only an original replica of them. In order to assimilate both data honesty and deduplication in cloud, we present a novel Secure Two Level Deduplication and Auditing of Shared Data in Cloud (STLDAS) mechanism. Experimental results show that our mechanism achieves secure deduplication and appreciable improvement in tag generation.

This paper involves a cloud computing environment in which the dataowner outsource the similarity search service to a third party service provider. Privacy of the outsourced data is important because they may be confidential data. The data should be made available to the authorized client groups, but not to be revealed to the service provider in which the data is stored. Given this scenario, the paper presents a technique called RSSMSO which has build phase, query phase, data transformation and search phase. The build phase and the query phase are about uploading the data and querying the data respectively; the data transformation phase transforms the data before submitting it to the service provider for similarity queries on the transformed data; search phase involves searching similar object with respect to query object. The RSSMSO technique provides enhanced query accuracy with low communication cost. Experiments have been carried out on real data sets which exhibits that the proposed work is capable of providing privacy and achieving accuracy at a low cost in comparison with FDH

Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user’s requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user’s input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images.

Cognitive Radio based Wireless Sensor Network is a novel concept that integrates the dynamic spectrum access capability of cognitive radio into wireless sensor networks for the futuristic sensor networks and wireless communication technology. Spectrum sensing plays a quintessential role in a cognitive radio network but is a major constraint for a battery powered sensor with stringent energy limitations. The spectrum sensing algorithms are expected to yield acceptable detection probability at low SNR under noise uncertainty with minimum power consumption in a WSN. In this paper, a new spectrum sensing method has been proposed to overcome sensing failure under low SNR environment. The proposed technique is based on adaptive double threshold theory which improves the detection performance by 39.63 and 27.22% at SNR = −10dB as compared to the conventional energy detection and available double threshold-based method respectively. Furthermore, the proposed method of spectrum sensing is evaluated for its deployment into a CR-WSN using the evaluation metrics: Time and Sample Complexity. The comparative evaluation of the spectrum sensing method in a WSN through simulations shows that the proposed technique offers substantial reduction in sample and time complexity of the wireless sensor nodes.

Recent advances in information technology and its widespread growth in the areas of business, engineering, medical and scientific studies have resulted in information explosion. High performance computing (HPC) systems are essential for solving scientific problems involving massive data using high computation power and high throughput networks. Due to the widespread growth of data in various fields, knowledge discovery and decision making is a challenging task and has resulted in the emerging trend of Big Data analytics. Big data is related to complex, diverse and massive data sets comprising of structured, semi-structured and unstructured data. Such data cannot be processed and analysed with the traditional database technologies. HPC systems can be extended to Big data applications for large-scale processing and analysis thus shifting the paradigm from traditional scientific computing domain to data intensive domain or Big data. The aim of this paper is to present an overview of the evolution and principles starting from scientific computing to present Big Data analytics.

The demand of deploying information has enormously increased within the last decade. Numerous distributed computing service suppliers have emerged (for eg., Microsoft Azure, Dropbox) in order to satisfy the requirements for information repository and high performance computation. The customers using the cloud repository services can conveniently arrange as a cluster and distribute information among themselves. Information proprietor computes the signatures for every chunk and deploys in the distributed server in order to allow the public verifier to perform public integrity verification on the information stored on the cloud server. In Panda scheme [1], by using the proxy re-signatures, Cloud Service Provider (CSP) verifies and re-signs the revoked customer chunks in favor of the existing customers. The malicious CSP might use the Resign key deliberately to transform the signature of one customer to another. Apart from this, conspiracy amidst the mischievous cloud server and the repudiated customer reveals the private key information of the customers present in the cluster. We propose Secure Auditing and Re-signing of Revoked Customer Chunks by Cloud Using Regression Method. Re-key computed by the information proprietor using regression method is highly secure and the mischievious cloud cannot detect the private information of the customers in the cluster. Our mechanism is collusion resistant, reduces computation cost of re-sign key by information proprietor and in addition CSP securely performs auditing and re-signing of repudiated customer chunks.

The two-stage on-board battery chargers in electric vehicles consists of conventional boost power factor correction (PFC) stage and an isolated DC-DC converter stage. Conventionally, the DC link voltage at output of PFC converter in the on-board chargers is maintained at 400 V which limits the charger output voltage in a range of 36-48 V, 72-150 V and 200-450V based on selection of DC-DC converter. The proposed variable DC link voltage at output of PFC converter enables wide output voltage range of charger with same DC-DC converter. The conventional boost PFC converters has the limitation that the required output voltage to be always greater than the peak of the input voltage. This paper presents a non-inverting two-switch buck-boost power factor correction (PFC) converter for use in on-board battery chargers to provide a wide DC link voltage. The operating modes of the converter in output voltage less than the peak of the input voltage and greater than peak of input voltage are explained in detail. The topology also maintains high power quality in both the modes. A 1kW PFC converter with universal input voltage 85-265 V(rms) is simulated in PSIM (11.0) and the preliminary experimental results is also been presented in the paper.

A two-stage battery charger in battery operated electric vehicles (BEVs) and plug-in-hybrid electric vehicles (PHEVs) with wide output voltage range of 100–500 V is most suitable for all vehicle architectures. Existing battery chargers have a different limited range of output voltages of 36–48 V, 72–150 V and 200–450 V is achieved by varying the output voltage of DC/DC converters keeping a fixed voltage at DC link. A universal charger, which can address this wide range of battery pack voltages is suitable for all vehicle architectures. The most feasible way to meet this requirement of wide output voltage range is to vary the voltage at DC link with a fixed conversion voltage ratio at DC/DC converter. In this paper, we propose the use of cascaded converter in power factor correction (PFC) converters to achieve the wide DC link voltages for battery chargers. The primary focus of the paper is on the analysis and operation of boost-cascaded by buck (BoCBB) converter. The control implementation presented in the paper achieves a high input power quality, wide DC link voltages with universal input voltage ranges of 85–265 V. It also provides the degree of control freedom to operate even if the VNm (output voltage to the peak of Input) < 0.5. Simulations of the proposed converter with 1 kW power rating are carried out in PSIM 11.0 software and the results with wide DC link voltage of 150–400 V are presented in the paper.

Electric Energy Storage Systems are pivotal for cleaner and sustainable development, applications ranging from transport electrification to electric power generation from renewable sources require some form of energy storage. Supercapacitors (SC) or Electric Double Layer Capacitors (EDLCs) with their remarkably long life cycles and high power handling capabilities have a growing presence in the energy storage market. With their complementary attributes compared to batteries, EDLCs provide a potent solution for hybrid electrical energy storage and even replace batteries in certain power applications. The lower energy density and a linear voltage-charge relation, limits the use of supercapacitors. In this paper a simple and efficient method to overcome these issues and increase the energy utilization of SCs and thereby downsizing the number of SCs required for an application has been presented with simulation and experimental results.

Opportunity charging of electric vehicles (EVs) during brief stops is an important application of wireless power transfer (WPT). Irrespective of the specific WPT technology used, it is possible to quantify the effect of opportunity charging on EVs using energy calculations. This paper presents an analysis of the potential reduction in battery size and extension in EV range enabled by opportunity charging, using urban driving cycle data and various charging power levels. Traction power expended for acceleration, and to overcome air drag and rolling friction are considered. Depending on the extent of opportunity charging, battery size reduction from 6% to 85% is possible. Alternatively, retaining the battery size at its base value, a range extension between 7% and 600% is realizable. Although the results are shown for a particular velocity profile, the generalized analysis method presented in this paper can cater to various types of driving cycles.

Unmanned aerial vehicles (UAVs) are changing the face of many real world applications ranging from war zones to farming. This paper introduces a wireless charging method that employs a capacitive wireless charging scenario for an unmanned aerial vehicle (drone) via a master/slave arrangement. This will increase the flight autonomy and range of UAVs thus making it more productive. A $150\ \text{mm} \times 170\ \text{mm}$ plate pair for wirelessly charging a drone battery is studied with different dielectrics. Finally, a comparison of different configurations of capacitive plate interface is outlined based on the simulation results.

The Jansen mechanism is a one degree-of-freedom, planar, 12-link, leg mechanism that can be used in mobile robotic applications and in gait analysis. This paper presents the kinematics and dynamics of the Jansen leg mechanism. The forward kinematics, accomplished using circle intersection method, determines the trajectories of various points on the mechanism in the chassis (stationary link) reference frame. From the foot point trajectory, the step length is shown to vary linearly while step height varies non-linearly with change in crank radius. A dynamic model for the Jansen leg mechanism is proposed using bond graph approach with modulated multiport transformers. For given ground reaction force pattern and crank angular speed, this model helps determine the motor torque profile as well as the link and joint stresses. The model can therefore be used to rate the actuator torque and in design of the hardware and controller for such a system. The kinematics of the mechanism can also be obtained from this dynamic model. The proposed model is thus a useful tool for analysis and design of systems based on the Jansen leg mechanism. (C) 2015 Elsevier B.V. All rights reserved.

Cloud computing provides economical and effective solution for sharing data among cloud users with low maintenance cost. The security of data and identity confidentiality while sharing data in multi-owner way cannot be assured by the Cloud Service Providers (CSP's). The Cloud Service Providers are reliable but curious to know the recurrent membership changes in the cloud. In this paper, we propose a secure multi-owner data sharing for dynamic group in the cloud with RSA Chinese Remainder Theorem (RSA-CRT) encryption technique and substring index generation method. RSA-CRT efficiently manages revocation list, key management, with reduced storage and computational overhead. The substring Index generation algorithm reduces the storage space compared to wild card fuzzy alogorithm(1). (C) 2016 The Authors. Published by Elsevier B.V.

In this paper, sensing coverage by wireless camera-embedded sensor networks (WCSNs), a class of directional sensors is studied. The proposed work facilitates the autonomous tuning of orientation parameters and displacement of camera-sensor nodes in the bounded field of interest (FoI), where the network coverage in terms of every point in the FoI is important. The proposed work is first of its kind to study the problem of maximizing coverage of randomly deployed mobile WCSNs which exploits their mobility. We propose an algorithm uncovered region exploration algorithm (UREA-CS) that can be executed in centralized and distributed modes. Further, the work is extended for two special scenarios: 1) to suit autonomous combing operations after initial random WCSN deployments and 2) to improve the network coverage with occlusions in the FoI. The extensive simulation results show that the performance of UREA-CS is consistent, robust, and versatile to achieve maximum coverage, both in centralized and distributed modes. The centralized and distributed modes are further analyzed with respect to the computational and communicational overheads.

Phase-shifted pulse width modulation (PS-PWM) technique is preferred for modular multilevel converters (MMCs) as it has even power distribution and also ensures uniform switch utilization among the submodules (SMs). In contrast, phase disposition PWM (PD-PWM) technique has superior output voltage profile, but suffers from unequal switch utilization. A new PWM technique is proposed in this paper, having the best features of the PS-PWM and PD-PWM. The SM capacitor voltage balancing algorithm has to be incorporated with the modulation scheme to maintain SM voltages at defined voltage level. This paper also presents a modified SM capacitor voltage balancing approach which can be easily implemented with any type of carrier-based PWM technique. The proposed balancing algorithm, as well as PWM technique can be extended to any level of MMC and also applicable to any SM type. The effectiveness of proposed PWM technique is validated for five-level MMC with flying capacitor SMs (FCSM) under different operating conditions against PS-PWM and PD-PWM by simulation results in PLECS platform.

Existing work Geographic opportunistic routing (GOR) selects a forwarding sensor node to progress data packets on the basis of geographic distance. Similarly, the multipath routing uses multiple paths to achieve both reliability and delay. However, geographic opportunistic routing results in lower packet delivery rate and high latency. The multipath routing introduces channel contention, interference, and quick depletion of energy of the sensor node in an asymmetric link wireless environment. The existing work Efficient QoS-aware Geographic Opportunistic Routing (EQGOR) elects and prioritize the forwarding nodes to achieve different QoS parameters. However, in EQGOR, the count of forwarding nodes increases with the increase in the required reliability. To improve energy efficiency, delay, and successful ratio of packet delivery in WSNs, we propose a Two-Hop Geographic Opportunistic Routing (THGOR) protocol that selects a subset of 2-hop neighbors of node which has high packet reception ratio and residual energy at the next forwarder node, and the selected 1-hop neighbors of node has supreme coverage of 2-hop neighbors as relay nodes. THGOR is comprehensively evaluated through ns-2 simulator and compared with existing protocols EQGOR and GOR. Simulation results show that THGOR significant improvement in packet advancement, delay, reliable transmission, and energy efficient.

Multiple events may occur simultaneously in heterogeneous sensor networks. It is necessary to aggregate event specific data. Event-Driven Data Aggregation (EDDA) is designed to efficiently gather aggregated data and route event specific data from the location of an event to the base station. The sensor nodes that have detected an event organize into an aggregation tree from the location of event to the base station. The nodes that do not detect the event relay the data to the base station. Such nodes integrate the data from different events into a single packet by prefixing a tag indicating the event type. The performance analysis shows that the EDDA algorithm incurs less energy consumption than existing state-of-the-art algorithms.

The world wide web, the internet, social interacting entities, and neural networks are the highly interconnected systems and are considered as complex networks. Now a days, scientists are focusing on link prediction in social networks. In literature survey, many supervised and unsupervised algorithms have been proposed for link prediction in simple and complex social networks. This article summarized the recent work about link prediction in complex social networks. Three data sets have been taken and baseline predictor methods common neighbor, jaccard coefficient, adamic/adar, preferential attachment, LRW and SRW are implemented on these datasets. Analysis of the results is done with the experiment by calculating two standard metrics AUC and precision.

Existing reliable-oriented routing protocols computes link reliability based on the packet reception ratio and neglects the impact of various parameters such as noise, shadowing, battery-lifespan, uncertainty and geographic locations. In this paper, we propose a Link Reliable Reactive Routing(LR3) protocol for WSNs to accomplish reliability and resilience to out-of-order transmission and path diversity at each hop. The log-normal shadowing model is used to estimate link reliability and a back-off scheme is used to determine delay. A new cost function is estimated to find forwarding nodes on the mentor path that includes link reliability, delay, status of queue, and packet advancement at the forwarding node. LR3 is simulated using NS-2 and the results show that it outperforms other reactive routing protocols in terms of packet delivery ratio, latency, link reliability and data transmission cost[1][2].

The availability of digital documents over web has increased exponentially and hence there is a need for effective methods to retrieve and organise. Since data is dispersed globally and unorganised...

This paper presents, a Group Based Optimal Retransmission Medium Access (GORMA) Protocol is designed that combines protocol of Collision Avoidance (CA) and energy management for low-cost, short-range, low-data rate and low-energy sensor nodes applications in environment monitoring, agriculture, industrial plants etc. In this paper, the GORMA protocol focuses on efficient MAC protocol to provide autonomous Quality of Service (QoS) to the sensor nodes in one-hop QoS retransmission group and two QoS groups in WSNs where the source nodes do not have receiver circuits. Hence, they can only transmit data to a sink node, but cannot receive acknowledgement control signals from the sink node. The proposed protocol GORMA provides QoS to the nodes which work independently on predefined time by allowing them to transmit each packet an optimal number of times within a given period. Our simulation results shows that the performance of GORMA protocol, which maximize the delivery probability of one-hop QoS group and two QoS groups and minimize the energy consumption., Comment: 9 pages in IEEE format and 6 figures

Energy management is one of the critical parameters in Wireless Sensor Networks. In this paper we attempt for a solution to balance the energy usage for maximizing the network lifetime, increase the packet delivery ratio and throughput. Our proposed algorithm is based on Energy Density of the clusters in Wireless Sensor Networks. The cluster head is selected using two step method and on-demand routing approach to calculate the balanced energy shortest path from source to sink. This unique approach maintains the balanced energy utilization among all nodes by selecting the different cluster heads dynamically. Our simulation results have compared with one of the plain routing scheme (EBRP) and cluster based routing (TSCHS), which shows the significant improvements in minimizing the delay and energy utilization and maximizing the network lifetime and throughput with respect to these works., Comment: 18 pages,7 Figures

Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like the finger knuckle print (FKP) of a person is unique and secure. Finger knuckle print is a novel biometric trait and is not explored much for real-time implementation. In this paper, three different algorithms have been proposed based on this trait. The first approach uses Radon transform for feature extraction. Two levels of security are provided here and are based on eigenvalues and the peak points of the Radon graph. In the second approach, Gabor wavelet transform is used for extracting the features. Again, two levels of security are provided based on magnitude values of Gabor wavelet and the peak points of Gabor wavelet graph. The third approach is intended to authenticate a person even if there is a damage in finger knuckle position due to injury. The FKP image is divided into modules and module-wise feature matching is done for authentication. Performance of these algorithms was found to be much better than very few existing works. Moreover, the algorithms are designed so as to implement in real-time system with minimal changes.

With the advancement of the internet and computing solutions, the lifestyle of people has changed for day-to-day activities. Data has reached every part of the world and device. Thus, data science or analytics plays a vital role in the current era of computing. It involves data collection, analysis and building intelligent systems for making critical business decisions. Recommendation systems are widely used in online based environments such as retail system, internet advertisements, mobile device applications, social networks and other systems that involve personal communication systems. These systems involve different sources of data either in the structured or unstructured format. In these types of systems, intelligent analytical solutions that mine structured data and unstructured data for gaining insights and in-turn better business decisions are required. In this paper, we provide an overview, challenges and opportunities in business analytics with big data. A case study on the recommendation system with analytics is presented explaining the role of analytics for recommendation systems in arriving at conclusions that in turn benefit for better business decisions.

The future of transportation is inevitably all-electric, and its driving forces are electric energy storage devices. This is particularly true for urban mass transit modes, such as trains, trams, buses, and public utility trucks. Ultracapacitors (UCs) are gaining popularity in transportation storage applications to supply peak power and power transient demands, especially in mass transit electrification. The inherent low open circuit voltage is overcome by connecting UCs in series to obtain the desired DC-bus voltage. However, variations in internal parameters of these devices can lead to unbalanced voltages across each UC cell during charge-discharge cycles, which negatively affects lifetime, energy storage efficiency, and safety of the UC bank. To overcome this issue, a suitable cell balancing (power management) circuitry is employed, which enhances the lifetime and performance of the overall UC bank. This paper reviews the concepts of various UC cell balancing methods and the associated power electronic converter modules. Comparison between the various techniques are presented and different balancing methods are grouped based on their principle of operation.

This paper involves a cloud computing environment in which the data owner out sources the similarity search service to a third party service provider. The user provides an example query to the server to retrieve similar data. Privacy of the outsourced data is important because they may be sensitive, valuable or confidential data. The data should be made available to the authorized client/client groups, but not to be revealed to the service provider in which the data is stored. Given this scenario, the paper presents a technique called FRORSS which has build phase, data transformation and search phase. The build phase is about uploading the data; the data transformation phase transforms the data before submitting it to the service provider for similarity queries on the transformed data; search phase involves searching similar object with respect to query object. Experiments have been carried out on real data sets which exhibits that the proposed work is capable of providing privacy and achieving accuracy at a lower value of result measure in comparision with FDH [1].

Wireless Sensor Networks are susceptible to communication failures and security attacks due to broadcast nature of communication. Multipath based communication techniques were designed to address communication failure. This paper proposes Secure Approximate Data Aggregation (SADA) in which synopsis are generated using primitive polynomial and Message Authentication Codes (MACs) are transmitted along with the synopsis to ensure integrity. SADA provides data freshness and integrity at a communication cost of O(1). Simulation results show that the SADA protocol incurs lower energy consumption and communication and computation cost compared to the state-of-the art protocols [1] [2].

The performance of the Internet is dominated by that of the TCP which in turn depends that of its congestion control algorithm. The primary purpose of the TCP is to take care of the congestion collapse of Internet and also to act as resource optimization mechanism in best effort environment. However, this makes TCP to be unfriendly with real time applications for which UDP is preferred. There is no significant work existing in the literature to analyse the delay performance of TCP in the presence of other TCP flows to the best of our knowledge. This paper quantitatively analyses the effect of TCP flows on fellow TCP flows as well as on other UDP flows for end to end delay as the performance parameter. Extensive simulations using NS2 are conducted to validate the degradation in delay performance of TCP due to the increased fraction of TCP in the background traffic. The analysis also provides an analytical results to already known problem of TCP not able to provide stringent Quality-of-Service (QoS) as desired by delay sensitive applications. The issue now raises is whether the variants of TCP help desired Quality-of-Service for delay sensitive applications on Internet or a new-fangled approach is called for.

One of the most fundamental services of cloud computing is Cloud storage service. Huge amount of sensitive data is stored in the cloud for easy remote access and to reduce the cost of storage. The confidential data is encrypt before uploading to the cloud server in order to maintain privacy and security. All conventional searchable symmetric encryption(SSE) schemes enable the users to search on the entire index file. In this paper, we propose the Domain and Range Specific Index Generation(DRSIG) scheme that minimizes the Index Generation time. This scheme adopts collection sort technique to split the index file into D Domains and R Ranges. The Domain is based on the length of the keyword; the Range splits within the domain based on the first letter of the keyword. A mathematical model is used to encrypt the indexed keyword that eliminates the information leakage. The time complexity of the index generation is O(NT × 3) where NT - Number of rows in index document and 3 is Number of columns in index document. Experiments have been conducted on real world dataset to validate proposed DRSIG scheme. It is observed that DRSIG scheme is efficient and provide more secure data than Ranked Searchable Symmetric Encryption(RSSE) Scheme.

In this paper, we propose a high capacity, self-synchronized, lossless audio watermarking algorithm based on the alpha (‘ �’) statistic modulation. Here ‘ �’ is related to the correlation among any given sequence i.e audio samples and it is modulated according to the watermark bit stream. The embedding scheme is tested in both the time domain and DWT domain. Though the time domain embedding reduces the computational time in searching the synchronization codes, the time-frequency localization capability of DWT provides good trade off between the computational complexity and robustness of synchronization codes. In case of DWT, ‘�’ related to the 2 nd level DWT coarse wavelet components is used for embedding the watermark. The offset value used for embedding is made adaptive to the required SNR for the final watermarked audio signal. After extraction of the embedded watermark using a watermark key, original audio can be recovered with minimal distortion. The watermarking method presented here does not require the use of the original signal for watermark detection. Also high embedding capacity is achieved by using small sized audio frames. Experimental results reveal that the proposed watermarking scheme maintains high audio quality and is simultaneously highly robust to pirate attacks, including MP3 compression, cropping, filtering, re-sampling, and re-quantization.