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

Internet of Vehicle (IoV) technology is a promising application of Internet of Things (IoT) employed in smart city environment for smart communication. Traffic handling and non-delay tolerant communication services are expected from the communicating vehicles to improve its application efficiency. With the consideration of IoV in healthcare applications, this manuscript introduces a rapid connection aware neighbor selection (RCNS) for improving the efficiency in handling medical information. The selection process is induced by an instantaneous reward based learning algorithm (RLA) for ease of neighbor selection. The multi-attributes of the neighbors in the information transmitting route are estimated with the knowledge of their position. Distance and reward of the neighbors in the transmission direction are assessed using a beacon-assisted vehicle discovery that minimizes the impact of collision. Different from the other neighbor selection schemes, this method is relies on renewed attribute estimated at the time of transmission. This aids the transmitter to decide transmission and modify transmission attributes. The performance of the proposed neighbor selection method is estimated through extensive simulation using the metrics: delay, delivery ratio, selection overhead and outage. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Department of Database Systems

School of Computer Science and Engineering

Vellore Campus

Department of Analytics

Peer-to-Peer Networking and Applications

A reward based connectivity-aware IoV neighbor selection for improving reliability in healthcare information exchange

Technology development has improved to its extent for mobile monitoring that mainly concentrates on a new trend that named as the Internet of Vehicles (IoV).A large number of researchers and organizations are attracted by this technological development that promises to be huge commercial interest. Several health monitoring devices were developed for the purpose of humans to identify themselves about their health problems and state of the situation about their health. This paper proposes a new architecture design for the healthcare that mainly concentrates on heart diseases using the IoV technology. Here, the continuous health monitoring device is used to monitor heart-related diseases that connected with IoV for obtaining a new way to provide and gather information about their health condition and updated automatically to the IoV based ambulance and the reports are updated to doctors using the same IoV technique. The architecture explains the several protocols that were used for communication, managing and analysis of data and transmitting from vehicle to device. © 2019, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature.

Health and Technology

A new healthcare architecture using IoV technology for continuous health monitoring system

Internet of things (IoT) incorporated with intelligent transportation systems (ITS) aided the design and development of internet of vehicles (IoV) for real-time applications. The applications of IoV in healthcare and medical field include smart monitoring and emergency vehicle transportation for improving its efficiency and improving reliability. Data handling and communication management are complex tasks in IoV aided healthcare applications due to the conventional issues in vehicular communication. To address the problem of data stagnancy and intermittent transmission in IoV communication, a multi-modal tree-driven transmission (MTT) scheme is designed. This transmission scheme balance is between neighbor selection and medical data management by preferring adaptive tree for transmitting emergency messages. By exploiting the multi-modal characteristics of the vehicle and data, the transmission scheme is scaled in an end-to-end manner. Precise neighbor selection, independent decision-process are the intermediate solutions assimilated in MTT for improving successful latency less delivery of medical data from the smart vehicles. The performance of the proposed MTT is assessed through simulations and is verified using the metrics latency, storage handling, transmission backlogs and data processing rate. © 2020 Mattingley Publishing. All rights reserved.

Test Engineering and Management

A multi-modal learning assisted vehicle selection for optimizing IOV communications in medical information handling

Recently some efforts have been made to use data from social media for prediction in various domains. Some studies also suggest public emotions in tweets may correlate with market trend. Using studies observations, public sentiments from social media helps to predict stock price of a particular company. The system can be effectively used to predict stock price movement of particular company or not. However, the current systems use to predict the overall market trend instead of predicting for individual company. Also, the public sentiment is not only the single factor which can affect the stock market so, there is need of combination of market and public sentiment to predict the stock price of the company. The system firstly mine the tweets of a particular company from different sources such as Twitter and Yahoo finance and news related to finance are also considered. The data is preprocessed to counter the noisy and missing data and sentiment of the public data of company is calculated using the natural language processing techniques. Financial data values of company will be fetched from yahoo finance. Some classifiers techniques such as SVM, Naïve Bayes and Maximum Entropy are also tested to find the best outcome. The public sentiment and market are sentiments are combined to obtain the desired outcome. The correlation between public’s sentiment and company’s stock price movements is observed. Granger causality test is used to determine whether sentiment polarity is able to predict the stock price in advance for a company. © 2016, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.