Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Provably Secure Fine-Grained Data Access Control Over Multiple Cloud Servers in Mobile Cloud Computing Based Healthcare Applications

Abstract: Mobile cloud computing (MCC) allows mobile users to have on-demand access to cloud services. A mobile cloud model helps in analyzing the information regarding the patients’ records and also in extracting recommendations in healthcare applications. In MCC, a fine-grained level access control of multiserver cloud data is a prerequisite for successful execution of end-users applications. In this paper, we propose a new scheme that provides a combined approach of fine-grained access control over cloud-based multiserver data along with a provably secure mobile user authentication mechanism for the Healthcare Industry 4.0. To the best of our knowledge, the proposed scheme is the first to pursue fine-grained data access control over multiple cloud servers in a MCC environment. The proposed scheme has been validated extensively in different heterogeneous environment where its performance was found good in comparison to other existing schemes.

Cloud resource provisioning: survey, status and future research directions

Abstract: Cloud resource provisioning is a challenging job that may be compromised due to unavailability of the expected resources. Quality of Service (QoS) requirements of workloads derives the provisioning of appropriate resources to cloud workloads. Discovery of best workload---resource pair based on application requirements of cloud users is an optimization problem. Acceptable QoS cannot be provided to the cloud users until provisioning of resources is offered as a crucial ability. QoS parameters-based resource provisioning technique is therefore required for efficient provisioning of resources. This research depicts a broad methodical literature analysis of cloud resource provisioning in general and cloud resource identification in specific. The existing research is categorized generally into various groups in the area of cloud resource provisioning. In this paper, a methodical analysis of resource provisioning in cloud computing is presented, in which resource management, resource provisioning, resource provisioning evolution, different types of resource provisioning mechanisms and their comparisons, benefits and open issues are described. This research work also highlights the previous research, current status and future directions of resource provisioning and management in cloud computing.

Highly bacterial resistant silver nanoparticles: synthesis and antibacterial activities

Abstract: In this article, we describe a simple one-pot rapid synthesis route to produce uniform silver nanoparticles by thermal reduction of AgNO3 using oleylamine as reducing and capping agent. To enhance the dispersal ability of as-synthesized hydrophobic silver nanoparticles in water, while maintaining their unique properties, a facile phase transfer mechanism has been developed using biocompatible block co-polymer pluronic F-127. Formation of silver nanoparticles is confirmed by X-ray diffraction (XRD), transmission electron microscopy (TEM) and UV–vis spectroscopy. Hydrodynamic size and its distribution are obtained from dynamic light scattering (DLS). Hydrodynamic size and size distribution of as-synthesized and phase transferred silver nanoparticles are 8.2 ± 1.5 nm (σ = 18.3%) and 31.1 ± 4.5 nm (σ = 14.5%), respectively. Antimicrobial activities of hydrophilic silver nanoparticles is tested against two Gram positive (Bacillus megaterium and Staphylococcus aureus), and three Gram negative (Escherichiacoli, Proteusvulgaris and Shigellasonnei) bacteria. Minimum inhibitory concentration (MIC) values obtained in the present study for the tested microorganisms are found much better than those reported for commercially available antibacterial agents.

Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Abstract: The Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

A Deep Learning-based Cryptocurrency Price Prediction Scheme for Financial Institutions

Abstract: A cryptocurrency is a network-based digital exchange medium, where the records are secured using strong cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). It uses blockchain technology to make the transactions secure, transparent, traceable, and immutable. Due to these properties, the cryptocurrencies have gained popularity in almost all the sectors especially in financial sectors. Though, cryptocurrencies are getting recognition form the approval bodies, but still, the uncertainty and dynamism in their prices risk the investments substantially. Cryptocurrency price prediction has become a trending research topic globally. Many machine learning and deep learning algorithms such as Gated Recurrent Unit (GRU), Neural Networks (NN), and Long short-term memory (LSTM) have been used by the researchers to predict and analyze the factors affecting the cryptocurrency prices. In this paper, a LSTM and GRU-based hybrid cryptocurrency prediction scheme is proposed, which focuses on only two cryptocurrencies, namely Litecoin and Monero. The results depict that the proposed scheme accurately predicts the prices with high accuracy, revealing that the scheme can be applicable in various cryptocurrencies price predictions.