Thapar University

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

Some results on information measures for complex intuitionistic fuzzy sets

Abstract: Complex intuitionistic fuzzy sets (CIFSs), modeled by complex‐valued membership and nonmembership functions with codomain the unit disc in a complex plane, handle two‐dimensional information in a single set. Under this environment, the primary objective of the present study is to introduce some novel formulae of information measures (similarity measures, distance measures, entropies, and inclusion measures) and discuss the transformation relationships among them. To demonstrate the efficiency of the proposed similarity measures, we apply it to pattern recognition problem and a detailed comparative analysis is conducted with some of the existing measures. Further, algorithms based on proposed measures are developed for handing multicriteria decision‐making problems and their working is illustrated with the help of an example. Besides this, the practicality of the proposed similarity measure is demonstrated by developing a clustering algorithm under CIFS environment.

Secure Multifactor Authenticated Key Agreement Scheme for Industrial IoT

Abstract: The application of Internet of Things (IoT) has generally penetrated into people’s life and become popular in recent years. The IoT devices with different functions are integrated and applied to various domains, such as E-health, smart home, Industrial IoT (IIoT), and smart farming. IIoT obtains the general attention among these domains, which allows the authorized user remotely access and control the sensing devices. The user suffices to attain the real-time data collected by sensing devices during the process of production. However, these data is usually transmitted via an insecure channel, which brings the problem of the security and privacy arising from the hostile attacks in IIoT. To resist the hostile attacks by the adversary and protect the security of the transmitted data, we propose a secure multifactor authenticated key agreement scheme for IIoT to support the authorized user remotely accessing the sensing device. The scheme adopts password, biometrics, and smart card to identify the user in the IIoT environment. We employ the secret-sharing technology and Chinese remainder theorem to construct a group key among legitimate sensing devices, and then this group key is utilized to assist in negotiating a secure session key between the user and multiple sensing devices. The proposed scheme is suitable for the resource-constrained IIoT as it only uses hash function, bitwise XOR operation, and symmetric cryptography. The performance analysis indicates that our scheme has less communication and computational costs in contrast to other correlative schemes. Besides, the security analysis indicates that our scheme can withstand many known attacks.

The Number of Confirmed Cases of Covid-19 by using Machine Learning: Methods and Challenges.

Abstract: Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make predictions about the events. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19.

BloCkEd: Blockchain-Based Secure Data Processing Framework in Edge Envisioned V2X Environment

Abstract: There has been an increasing trend of moving computing activities closer to the edge of the network, particularly in smart city applications (e.g., vehicle-to-everything – V2X). Such a paradigm allows the end user’s requests to be handled/processed by nodes at the edge of the network; thus, reducing latency, and preserving privacy of user data/activities. However, there are a number of challenges in such an edge computing ecosystem. Examples include (1) potential inappropriate utilization of resources at the edge nodes, (2) operational challenges in cache management and data integrity due to data migration between edge nodes, particularly when dealing with vehicular mobility in a V2X application, and (3) high energy consumption due to continuous link breakage and subsequent reestablishment of link(s). Therefore in this paper, we design a blockchain-based secure data processing framework for an edge envisioned V2X environment (hereafter referred to as BloCkEd ). Specifically, a multi-layered edge-enabled V2X system model for BloCkEd is presented, which includes the formulation of a multi-objective optimization problem. In addition, BloCkEd comprises an optimal container-based data processing scheme, and a blockchain-based data integrity management scheme, designed to minimize link breakage and reducing latency. Using Chandigarh City, India, as the scenario, we implement and evaluate the proposed approach in terms of its latency, energy consumption, and service level agreement compliance.