Showing papers by "Indian Institute of Technology Kharagpur published in 2018"

Assessment of the Suitability of Fog Computing in the Context of Internet of Things

Abstract: This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing—one of the first attempts of its kind. With the rapid increase in the number of Internet-connected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide ( $\text{CO}_2$ ) gas. In this work, we assess the applicability of the newly proposed fog computing paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, $\text{CO}_2$ emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding real-time service. A case study is performed with traffic generated from the $100$ highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with $50$ percent applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by $50.09$ percent. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing.

Control Techniques in AC, DC, and Hybrid AC–DC Microgrid: A Review

Abstract: The microgrid concept is gaining popularity with the proliferation of distributed generation. Control techniques in the microgrid are an evolving research topic in the area of microgrids. A large volume of survey articles focuses on the control techniques of the microgrid; however, a systematic survey of the hierarchical control techniques based on different microgrid architectures is addressed very little. The hierarchy of control in microgrid comprises three layers, which are primary, secondary, and tertiary control layers. A review of the primary and secondary control strategies for the ac, dc, and hybrid ac–dc microgrid is addressed in this paper. Furthermore, it includes the highlights of the state-of-the-art control techniques and evolving trends in the microgrid research.

Provably Secure Authenticated Key Agreement Scheme for Smart Grid

Abstract: Due to the rapid development of wireless communication systems, authentication becomes a key security component in smart grid environments. Authentication then plays an important role in the smart grid domain by providing a variety of security services including credentials’ privacy, session-key (SK) security, and secure mutual authentication. In this paper, we analyze the security of a recent relevant work in smart grid, and it is unfortunately not able to deal with SK-security and smart meter secret credentials’ privacy under the widely accepted Canetti–Krawczyk adversary (CK-adversary) model. We then propose a new efficient provably secure authenticated key agreement scheme for smart grid. Through the rigorous formal security analysis, we show that the proposed scheme achieves the well-known security functionalities including smart meter credentials’ privacy and SK-security under the CK-adversary model. The proposed scheme reduces the computation overheads for both smart meters and service providers. Furthermore, the proposed scheme offers more security functionalities as compared to the existing related schemes.

Databases, features and classifiers for speech emotion recognition: a review

Abstract: Speech is an effective medium to express emotions and attitude through language. Finding the emotional content from a speech signal and identify the emotions from the speech utterances is an important task for the researchers. Speech emotion recognition has considered as an important research area over the last decade. Many researchers have been attracted due to the automated analysis of human affective behaviour. Therefore a number of systems, algorithms, and classifiers have been developed and outlined for the identification of emotional content of a speech from a person's speech. In this study, available literature on various databases, different features and classifiers have been taken in to consideration for speech emotion recognition from assorted languages.

Municipal solid waste: Generation, composition and GHG emissions in Bangalore, India

Abstract: Municipal solid waste in developing countries mainly consists of degradable materials (>70%), which plays a significant role in GHG (Greenhouse gas) emissions in urban localities. The increasing municipal solid waste generation along with the high fraction of organic waste and its unscientific disposal is leading to emission of GHG (methane, CO2, etc.) in the atmosphere. Proportion of municipal solid wastes collected by the agencies disposed at identified sites is about 60%, while the balance is disposed-off at unauthorized disposal sites leading to the environmental consequences including greenhouse gas emissions. Mitigation strategy necessitates understanding of composition of waste for its treatment and management in an environmentally sound way. The study revealed that the per capita waste generated is about 91.01± 45.5 g/day with the per capita organic waste generation of 74±35 g/person/day. The household per capita waste generation was positively related with income and education levels, while negatively related with family (household) size. The organic fractions constitute 82% with the strong recovery potential and conversion to energy or compost range. The total organic waste generated is about 231.01 Gg/year and due to mismanagement consequent emissions are about 604.80 Gg/year. Integrated solid waste management strategy is suggested to manage the organic fractions through technology and policy interventions, which helps in mitigating GHG emissions with potential economic benefits.

MoS2 Quantum Dots as Efficient Catalyst Materials for the Oxygen Evolution Reaction

Abstract: The development of an active, earth-abundant, and inexpensive catalyst for the oxygen evolution reaction (OER) is highly desirable but remains a great challenge. Here, by combining experiments and first-principles calculations, we demonstrate that MoS2 quantum dots (MSQDs) are efficient materials for the OER. We use a simple route for the synthesis of MSQDs from a single precursor in aqueous medium, avoiding the formation of unwanted carbon quantum dots (CQDs). The as-synthesized MSQDs exhibit higher OER activity with a lower Tafel slope in comparison to that for the state of the art catalyst IrO2/C. The potential cycling of the MSQDs activates the surface and improves the OER catalytic properties. Density functional theory calculations reveal that MSQD vertices are reactive and the vacancies at the edges also promote the reaction, which indicates that the small flakes with defects at the edges are efficient for the OER. The presence of CQDs affects the adsorption of reaction intermediates and dramaticall...

A comprehensive review on harvesting of microalgae for biodiesel – Key challenges and future directions

Abstract: Economically viable microalgal biodiesel production is unrealistic and unsustainable owing to expensive harvesting or dewatering techniques. Hence, immense and meticulous exploration of harvesting process is essential to identify knowledge leads by which suitable harvesting technique could be ascertained for lucrative biodiesel production. With this in view, this review aims to collate and highlight the spectrum of harvesting techniques applied to microalgae, i.e., conventional – modern, high cost- inexpensiveness, energy efficient- energy consuming process. At the outset, global energy outlook and demand had been critically addressed, and the scientific ways to tackle or satiate the fuel demand had also been highlighted in this reveiw. This review manuscript has thrown widespread light on the physical harvesting methods namely centrifugation, sedimentation, filtration, flotation and technical advantages thereof. Due to the energy-intensive and cost barrier of physical harvesting techniques, chemical methods entailing organic, inorganic, and electroflocculation have come to limelight and in this regard, microalgae used, floc recovery and the dose of flocculants have been compared and presented in detail. Further, state of the art harvesting techniques viz., bioflocculation by microalgae/bacteria, flocculation by pH adjustment, and magnetic nanocomposite based microalgal harvesting had been critically articulated. Besides discussing the several methods, this paper has summarized the key challenges in conventional and advanced harvesting techniques and also provided the scope thereof. Hence, the key suggestions and findings given in this manuscript would positively offer a well-defined roadmap in choosing foreseeable harvesting technology for cost-effective microalgal biofuel development.

State of Health Estimation of Lithium-Ion Batteries Using Capacity Fade and Internal Resistance Growth Models

Abstract: In this paper, a method for the estimation of remaining useful lifetime (RUL) of lithium-ion batteries has been presented based on a combination of its capacity degradation and internal resistance growth models. The capacity degradation model is developed recently based on battery capacity test data. An empirical model for internal resistance growth is also developed based on electrochemical-impedance spectroscopy (EIS) test data. The obtained models are used in a particle filtering (PF) framework for making end-of-lifetime (EOL) predictions at various phases of its lifecycle. Further, the above two models were fused together to obtain a new degradation model for RUL estimation. It has been observed that the fused degradation model has improved the standard deviation of prediction as compared to the individual degradation models by maintaining satisfactory prediction accuracy. The effect of parameter variations on the performance of the PF algorithm has also been studied. Finally, the predictions are validated with experimental data. From the results it can be observed that with the availability of longer volume of data, the prediction accuracy gradually improves. The prognostics framework proposed in this paper provides a structured way for monitoring the state of health (SoH) of a battery.

Bayesian network modelling for supply chain risk propagation

Abstract: Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk prop...

Information communication technology (ICT) infrastructure and economic growth: A causality evinced by cross-country panel data

Abstract: This study examines certain long-run relationships hypothesised to be present among per capita real GDP, information and communication technology (ICT) infrastructure, consumer price index, labour force participation rate, and gross fixed capital formation manifest in G-20 countries recorded for the 2001–2012 period. Using panel cointegration, the study finds that the variables are cointegrated and do not drift apart in the long run. Methodology using vector error correction models (VECM) further confirms that embellishment of ICT infrastructure – an apparent imperative in an economy's information technology (IT) policy formulation – for both fixed broadband and internet users causes a boost in the per capita GDP.

Factors Affecting Mobile Payment Adoption Intention: An Indian Perspective:

Abstract: This study aims to identify the factors affecting mobile payment (m-payment) adoption intention in India by proposing a conceptual framework based on technology acceptance model (TAM). In addition ...

Soft-WSN: Software-Defined WSN Management System for IoT Applications

Abstract: In this paper, we propose a software-defined wireless sensor network architecture ( Soft-WSN )—an effort to support application-aware service provisioning in Internet of Things (IoT). Detailed architecture of the proposed system is presented involving the application, control, and infrastructure layers to enable software-defined networking (SDN) in IoT. We design a software-defined controller, which includes two management policies—device management and network management. Device management facilitates users to control their devices in the network. To enable device control mechanisms, we investigate three scheduling issues in a sensor node—sensing task, sensing delay, and active sleep. On the other hand, the topology of the network is controlled by the network management policies, which can be modified in run time to deal with dynamic requirements of IoT. Furthermore, the proposed scheme is implemented in a real hardware platform without changing the underlying sensor networking concepts, so that existing sensor devices can be seamlessly integrated. Therefore, in contrast to the existing SDN solutions for WSNs, the proposed system, Soft-WSN , focuses on both device management and topology management to meet run-time application-specific requirements of IoT, while enhancing flexibility and simplicity of WSN management. Experimental results on a real hardware-based test bed indicate that the proposed scheme is beneficial to meet real-time application-specific requirements of IoT, while ensuring significant improvements on network performance over the traditional approaches.

Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation.

Abstract: We present an efficient deep learning framework for identifying, segmenting, and classifying cell membranes and nuclei from human epidermal growth factor receptor-2 (HER2)-stained breast cancer images with minimal user intervention. This is a long-standing issue for pathologists because the manual quantification of HER2 is error-prone, costly, and time-consuming. Hence, we propose a deep learning-based HER2 deep neural network (Her2Net) to solve this issue. The convolutional and deconvolutional parts of the proposed Her2Net framework consisted mainly of multiple convolution layers, max-pooling layers, spatial pyramid pooling layers, deconvolution layers, up-sampling layers, and trapezoidal long short-term memory (TLSTM). A fully connected layer and a softmax layer were also used for classification and error estimation. Finally, HER2 scores were calculated based on the classification results. The main contribution of our proposed Her2Net framework includes the implementation of TLSTM and a deep learning framework for cell membrane and nucleus detection, segmentation, and classification and HER2 scoring. Our proposed Her2Net achieved 96.64% precision, 96.79% recall, 96.71% F-score, 93.08% negative predictive value, 98.33% accuracy, and a 6.84% false-positive rate. Our results demonstrate the high accuracy and wide applicability of the proposed Her2Net in the context of HER2 scoring for breast cancer evaluation.

Thermal reduction of graphene oxide: How temperature influences purity

Abstract: Among various methods used for the reduction of graphene oxide (GO) into a purer form of graphene, the thermal reduction method provides a simpler, safer, and economic alternative, compared to other techniques. Thermal reduction of GO causes significant weight loss and volume expansion of the material. Current work investigates the onset temperature where reduction in terms of exfoliation takes place, which is determined to be 325 °C at standard atmospheric pressure. Reduction temperature plays the most crucial role as it controls the quality of reduced graphene oxide in terms of weight percentage of carbon and lattice defect. The study leads to achieving highest content with a minimum defect in the graphene lattice at the optimum temperature, which is found to be 350 °C at standard atmospheric pressure. The thermal reduction process has been analyzed with the help of Fourier transform infrared spectroscopy, thermogravimetric analysis, and thermal degradation kinetics. From thermal degradation kinetics of GO, the rate of reaction has been found to be independent of concentration and is a sole function of temperature.

Controlled Synthesis of CuS/TiO2 Heterostructured Nanocomposites for Enhanced Photocatalytic Hydrogen Generation through Water Splitting.

Abstract: Photocatalytic hydrogen (H2) generation through water splitting has attracted substantial attention as a clean and renewable energy generation process that has enormous potential in converting solar-to-chemical energy using suitable photocatalysts. The major bottleneck in the development of semiconductor-based photocatalysts lies in poor light absorption and fast recombination of photogenerated electron-hole pairs. Herein we report the synthesis of CuS/TiO2 heterostructured nanocomposites with varied TiO2 contents via simple hydrothermal and solution-based process. The morphology, crystal structure, composition, and optical properties of the as-synthesized CuS/TiO2 hybrids are evaluated in detail. Controlling the CuS/TiO2 ratio to an optimum value leads to the highest photocatalytic H2 production rate of 1262 μmol h-1 g-1, which is 9.7 and 9.3 times higher than that of pristine TiO2 nanospindles and CuS nanoflakes under irradiation, respectively. The enhancement in the H2 evolution rate is attributed to increased light absorption and efficient charge separation with an optimum CuS coverage on TiO2. The photoluminescence and photoelectrochemical measurements further confirm the efficient separation of charge carriers in the CuS/TiO2 hybrid. The mechanism and synergistic role of CuS and TiO2 semiconductors for enhanced photoactivity is further delineated.

Online Estimation of the Electrochemical Impedance Spectrum and Remaining Useful Life of Lithium-Ion Batteries

Abstract: An electrochemical impedance spectrum (EIS) is considered to be one of the key indicators to monitor the health status of lithium-ion batteries. Experimental procedures to measure the EIS of a battery are offline and require manual intervention. So, in order to monitor the state of health of a battery in real time, online methods for EIS estimation would be very useful. This paper presents an approach for estimation of the EIS of lithium-ion batteries based on a fractional-order equivalent circuit model (FOECM) which can be implemented online. First, the parameters of the fractional-order model are determined using recursive least-squares technique in conjunction with a fractional-order state variable filter based on current and voltage measurements. The parameters obtained are then used to generate the estimated EIS of the battery under different aging conditions. Thereafter, a regression model is obtained based on the estimated EIS spectrum which can represent the degradation trend of the battery in terms of its internal resistance growth. Finally, the obtained regression model is used in the particle filtering framework to predict the remaining useful life (RUL) of the battery quite satisfactorily as compared to the RUL obtained based on the measured EIS data. Moreover, in order to justify the proposed RUL estimation method based on FOECM, comparative analyses with respect to other FOECM-based regression models and an integer order model have also been carried out.

Spread of hate speech in online social media

Abstract: The present online social media platform is afflicted with several issues, with hate speech being on the predominant forefront. The prevalence of online hate speech has fueled horrific real-world hate-crime such as the mass-genocide of Rohingya Muslims, communal violence in Colombo and the recent massacre in the Pittsburgh synagogue. Consequently, It is imperative to understand the diffusion of such hateful content in an online setting. We conduct the first study that analyses the flow and dynamics of posts generated by hateful and non-hateful users on Gab (this http URL) over a massive dataset of 341K users and 21M posts. Our observations confirms that hateful content diffuse farther, wider and faster and have a greater outreach than those of non-hateful users. A deeper inspection into the profiles and network of hateful and non-hateful users reveals that the former are more influential, popular and cohesive. Thus, our research explores the interesting facets of diffusion dynamics of hateful users and broadens our understanding of hate speech in the online world.