Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Computational Intelligence

The rapid development forms a new transition of information technologies to offer an intelligent manufacturing. The manufacturer has revolutionized the stages of product lifecycle including process planning and maintenance for the early detection of potential system failures and proactive management. Technological advancements including big data, the cloud, and the Internet of Things have applied digital-twin for industrial practice. It has low-power wireless-enabled devices to play a vital role in various industrial automation systems such as industry logistics, portable equipment, and intelligent wireless monitoring. It is evident that industrial manufacturers are nowadays aiming to transform the machine into fully automated systems that not only control the operation of the equipment but also try to meet the demand of future markets effectively. One of the challenging issues in the automation of the machinery process is the deployment of reliable systems to analyze the machinery condition such as fault diagnosis. Thus, this article proposes a digital-twin-assisted fault diagnosis using deep transfer learning to analyze the operational conditions of machining tools. Moreover, this proposed system has developed an intelligent tool-holder that integrates a k-type thermocouple and cloud data acquisition system over the WiFi module. The analytical study proves that this intelligent tool-holder provides better accuracy to demonstrate the optimization of milling and drilling operations of cutting tools. © 2021 Wiley Periodicals LLC

School of Computer Science and Engineering

Vellore Campus

World University root node

International Journal of Intelligent Systems

Digital-twin assisted: Fault diagnosis using deep transfer learning for machining tool condition

Numerous application domains in practice related to target- tracking, monitoring, and transportation have utilized the wide usage of Wireless Sensor Networks (WSN's) technology. These domains use physical networking objects connecting over the Internet to collect and exchange the data. Using the advancement of cloud computing technologies and aggravation of big-data growth caused by the incorporation of the Internet of Things (IoT), secure user authentication for remote access is playing a crucial role. Since it has limited authorization and authentication privileges for mobility users, an approach of mobile-sink has been instigated for the improvisation of remote user authentication i.e. cloud-based IoT applications. As a result, this article proposes a lightweight smartcard based secure authentication (LS-BSA) approach using the mathematical assumption of bilinear-pairing/mapping, elliptic-curve cryptosystems, and fuzzy verifier. An extensive security investigation demonstrates that the proposed LS-BSA not only guarantees the AKA security properties but also prevents significant vulnerabilities. Furthermore, the proposed LS-BSA uses lightweight operations to establish a seamless data connectivity over a secure network. It maintains the compatibility standards including low-cost and low-power to mitigate the computation and communication cost of cloud-based intelligent data computing. Formal security verification of BAN-logic is introduced to show that LS-BSA offers proper mutual user authentication and secret secure-session key agreement between the real-time entities. In addition, a network scenario has been set up using the NS-3 simulator to prove that the proposed LS-BSA is more efficient than other existing schemes in terms of packet delivery ratio, end-to-end delay, and throughput rate. © 2020 Elsevier B.V.

Future Generation Computer Systems

Lightweight authentication for IoT/Cloud-based forensics in intelligent data computing

Session Initiation Protocol (SIP) is a communication protocol of multimedia systems providing IP signaling to initiate instant messaging. The rapid growth of wireless communication has gained momentum for the development of value-added services such as voice, data, and video ubiquitously. Continuous development has emerged in various application domains such as healthcare, surveillance, and automation that integrate the sources of multimedia to support real-time analysis. In mHealth, it has a dedicated multimedia information system (MIS) to support diagnosis, treatment, medicare, and report preparation. It may enable the user to collect the diagnostic information over smart devices. On the other hand, it may eliminate the geographical restriction between users and medical experts to manage medical emergencies. It uses a packet-switched network to cooperate with the wireless infrastructure to provide a standard interface that serves as a data transmission network. However, the communication interface such as real-time transport protocol is still challenging to authenticate the network services, key exchanges, and session blockage in the multimedia systems. Thus, this paper proposes a technique of session scheme interrogation (SSI) and robust lightweight privacy-preserving (RLPP) to resolve the major issues of LTE-A networks. In this paper, the session adaptive strategy is mathematically driven to adopt the session probing in multimedia clients that detects the session blocks proficiently to mitigate the session overheads. Besides, a real-time system has been designed and developed for the analysis of quality metrics such as call success rate, call setup time, end-to-end session delay, RTP utilization, bandwidth consumption, and flooding attack detection rate. The examination results prove that the proposed RLPP and SSI achieve better efficiencies than other existing schemes. © 2020 Elsevier Ltd

Journal of Information Security and Applications

Robust Lightweight Privacy-Preserving and Session Scheme Interrogation for Fog Computing Systems

Due to the large and complex nature of the Internet of Things (IoT), various analysis and design methodologies are proposed. Design pattern is one of them to provide the solution for a design problem, which is recurring in nature. In this approach, we have presented design patterns for the IoT use cases such as service integration and IoT security. But the semi-formal nature of software patterns may lead to bugs in the proposed pattern. Hence, an ontology-based approach is considered for modelling of the IoT design patterns. Further, we have presented an ontology-based framework for the specification and refinement of IoT design patterns. In this approach, an analysis of meta-models and ontologies has also been performed for reducing the gap between high-level design abstraction of Unified Modeling Language (UML)-based IoT patterns and formal ontology. © 2021 World Scientific Publishing Co.

Journal of Information and Knowledge Management

Ontology-Based Modelling of IoT Design Patterns

Deep learning comes under a class of machine learning where we use it for extremely high-level output, like recognition of images, etc. It has been used in pattern recognition over a vast area such as handmade crafts to extract the data from learning procedures. At present, it has gained a great significance in robot vision. In this paper, we show how neural networks play a vital role in robot vision. Image segmentation, which is the initial step, is used to preprocess the images and videos. The multilayered artificial neural networks have a lot more applications. It can be applied in drug detection, military bases, and many more. The main objective of this paper is to review how deep learning algorithms and deep nets can be used in various areas of robot vision. There are some predefined deep learning algorithms that are available in the market, which are used here to perform this comparative study. These will help us to have a clear insight while building vision systems using deep learning. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Innovation, Systems and Technologies

Deep Learning for Robot Vision

Haze is a common atmospheric disturbance that adversely affects the quality of optical data, thus often restricting their usability. Since these effects are inherent in the process of spaceborne Earth sensing, it is important to develop effective methods to remove them. This work proposes a novel method for de-hazing satellite imagery and outdoor camera images. It is developed by modifying the transmission map used in Dark Channel Prior (DCP) method. A Weighted Variance Guided Filter (WVGF) is introduced for enhancing the image quality, which included a two-stage image decomposition and fusion process. The method also optimally combines the radiance and transmission components along with an additional stage modelling a fusion-based transparency function. A final guided filter-based image refinement scheme is incorporated to improve the processed image quality. The optimal tuning of the image-dependent parameters at various stages is achieved using the newly proposed Adaptive Black Widow Optimization (ABWO) algorithm, which makes the proposed de-hazing scheme fully automatic. Qualitative and quantitative performance analyses, and the results are compared with other state-of-the-art methods. The experimental results reveal that the proposed method performs better as compared with others, independent of the haze density, without losing the natural look of the scene. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

International Journal of Remote Sensing

Dehazing of Satellite Images using Adaptive Black Widow Optimization-based framework

Digitalization accumulates data in a short period. Smart agriculture for crop identification for cultivation is a common problem in agriculture for agronomists. The generated data due to digitalization does not provide any useful information unless some meaningful information is retrieved from it. Therefore from the existing information system, prediction of decision for unseen associations of attribute values is of challenging. This paper presents a model that hybridizes a fuzzy rough set, real-coded genetic algorithm, and linear regression. The model works in two phases. In the initial phase, the fuzzy rough set is used to remove superfluous attributes whereas, in the second phase, a real-coded genetic algorithm is used to predict the decision values of unseen instances by making use of linear regression. The proposed model is analyzed for its viability using agricultural information system obtained from Krishi Vigyan Kendra of Thiruvannamalai district of Tamilnadu, India. Further, the accuracy of the proposed model is compared with existing techniques. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.

Department of Digital Communications

School of Information Technology and Engineering

Multimedia Tools and Applications

An integrated fuzzy rough set and real coded genetic algorithm approach for crop identification in smart agriculture

The challenge of COVID-19 has become more prevalent across the world. It is highly demanding an intelligent strategy to outline the precaution measures until the clinical trials find a successful vaccine. With technological advancement, Wireless Multimedia Sensor Networks (WMSNs) has extended its significant role in the development of remote medical point-of-care (RM-PoC). WMSN is generally located on a communication device to sense the vital signaling information that may periodically be transmitted to remote intelligent pouch This modern remote system finds a suitable professional system to inspect the environment condition remotely in order to facilitate the intelligent process. In the past, the RM-PoC has gained more attention for the exploitation of real-time monitoring, treatment follow-up, and action report generation. Even though it has additional advantages in comparison with conventional systems, issues such as security and privacy are seriously considered to protect the modern system information over insecure public networks. Therefore, this study presents a novel Single User Sign-In (SUSI) Mechanism that makes certain of privacy preservation to ensure better protection of multimedia data. It can be achieved over the negotiation of a shared session-key to perform encryption or decryption of sensitive data during the authentication phase. To comply with key agreement properties such as appropriate mutual authentication and secure session key-agreement, a proposed system design is incorporated into the chaotic-map. The above assumption claims that it can not only achieve better security efficiencies but also can moderate the computation, communication, and storage cost of some intelligent systems as compared to elliptic-curve cryptography or RSA. Importantly, in order to offer untraceability and user anonymity, the RM-PoC acquires dynamic identities from proposed SUSI. Moreover, the security efficiencies of proposed SUSI are demonstrated using informal and formal analysis of the real-or-random (RoR) model. Lastly, a simulation study using NS3 is extensively conducted to analyze the communication metrics such as transmission delay, throughput rate, and packet delivery ratio that demonstrates the significance of the proposed SUSI scheme. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Fulltext

Chaotic-map based authenticated security framework with privacy preservation for remote point-of-care

Technological development expands the computation process of smart devices that adopt the telecare medical information system (TMIS) to fulfill the demands of the healthcare organization. It provides better medical identification to claim the features namely trustworthy, efficient, and resourceful. Moreover, the telecare services automate the remote healthcare monitoring process to ease professional workloads. Importantly, it is conceived to be more timesaving, economical, and easy healthcare access. Cloud-Based Medical Healthcare (CBMH) system is a standard platform that gives its support to the patients for emergency treatment from the medical experts over Internet communication. Since the medical records are very sensitive, security protection is much necessitated. In addition, patient anonymity should be well preserved. In 2016, Chiou et al. proposed a mutual authentication protocol for the Telecare Medical Information System (TMIS) using Cloud Environment (CE). They claim that their protocol satisfies patient anonymity. However, this paper proves that the Chiou et al. scheme is not only completely insecure against the patient anonymity, health-report revelation, health-report forgery, report confidentiality, and non-repudiation but also fails to validate the service access against verifiability, undeniability and unforgeability. In order to provide better mutual authenticity, this paper suggests the framework of smart service authentication to cross-examine the common secret session key among the communication entities. In order to examine the security properties, formal and informal verification was carried out. Lastly, to prove the security and performance efficiency of a system, the proposed SSA framework was implemented using FPGA and Moteiv TMote Sky-Mote. A proposed smart service authentication (SSA) framework is presented to ensure better data security between the patients and the physicians. The formal and informal security analysis proves the significance of the SSA framework model to withstand the security attacks such as health-report forgery, health-report revelation, server-spoofing etc. As a result, it is claimed that it can be well suited for TMIS. © 1983-2012 IEEE.

IEEE Journal on Selected Areas in Communications

Smart Mutual Authentication Protocol for Cloud Based Medical Healthcare Systems Using Internet of Medical Things

Context: The world is experiencing a boom of data throughout several fields such as finance, engineering, medicine, crime and security etc. More and more problems associated with these fields are starting to be modeled using networks and dynamic and/or static graphs. This has led to further exploration of these graphs, their community structures and the associated community detection methods. Objective: This paper aims at studying a set of community detection approaches and analyzing their performance in terms of both external and internal criteria, including rand index, adjusted rand index, variation of information and normalized mutual information. Method: The chosen set of algorithms is thoroughly studied. Amongst this set, each algorithm is compared with the other in the set and the result is recorded numerically. These numerical values are recorded in a tabular format for better understanding of the reader. Each table is made specific to the criterion used to compare the algorithms. Altogether, a total of 4 tables are obtained for each of the metric used for the comparative analysis. Results: On comparing the algorithms with each other, the results are tabulated in a symmetric matrix whose values along the diagonal have an associated significance, which is described in the following sections. Conclusion: This paper presents a comparative analysis of the community detection algorithms against a set of external and internal criteria. Further, it lists some challenges faced by the research community in the study of these algorithms. The last section gives a summary of the common applications of community structure and detection methods. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.