Faculty Publications
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Publications by NITK Faculty
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Item Role of smart meters in smart city development in India(Institute of Electrical and Electronics Engineers Inc., 2017) Patel, S.; Uday Kumar, R.Y.; Prasanna Kumar, B.Day by day in India we are moving towards technologies so everything started becoming advance. The proposal of smart cities in India has already came which include better way of urbanization. Smart meters are the key component in smart cities. Here we are going to discuss about how smart meters will be helpful in making our energy consumption as well as metering system smart. Concepts of smart cities and smart grid and how they are dependent on smart meters will be discussed here. Smart meters has brought big revolution in the fields of energy and power measurement. Worldwide at so many places smart meters has been already deployed but in India it is just starting of deployment of smart meters here some reports regarding that are also discussed. © 2016 IEEE.Item Vehicle Re-identification Using Convolutional Neural Networks(Springer Science and Business Media Deutschland GmbH, 2023) Kedkar, N.; Karthik Reddy, K.; Arya, H.; Sunil, C.K.; Patil, N.Vehicle re-identification is the process of matching automobiles from one place on the road (one field of vision) to the next. Important traffic characteristics like the trip duration, travel time variability, section density, and partial dynamic origin/destination needs may be acquired by performing vehicle re-identification. However, doing so without using number plates has become challenging since cars experience substantial variations in attitude, angle of view, light, and other factors, all of which have a major influence on vehicle identification performance. To increase each model’s representation ability as much as feasible, we apply a variety of strategies that will bring a major change like using filter grafting, semi-supervised learning, and multi-loss. The tests presented in this paper show that such strategies are successful in addressing challenges within this space. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Performance Analysis of Hybrid RF/FSO System Using BPSK-SIM and DPSK-SIM Over Gamma-Gamma Turbulence Channel With Pointing Errors for Smart City Applications(Institute of Electrical and Electronics Engineers Inc., 2018) Krishnan, P.The flourishing technology in wireless communication-free space optics (FSO) offers lots of merits over radio frequency (RF) links due to its license free bandwidth, ease of installation, high security features, and viable cost for short distance communication. It's high speed data rate and immunity against electromagnetic interference makes FSO the emerging technology of today. But, FSO is not always reliable especially during atmospheric conditions, such as fog, rain, mist, and snow. Hence, in account a new technique of hybrid FSO/RF, this includes advantages of both FSO and RF technologies. Through this paper intend to perform an extensive analysis of the error and misalignment effects encountered in line of sight communication. Pointing error and turbulence effects are the main drawback parameters for our analysis. For this purpose I have taken into consideration different modulation techniques-binary phase shift keying-subcarrier intensity modulation, differential phase shift keying-subcarrier intensity modulation communication system with reference to on-off keying (OOK) modulation. The novel expressions for outage probability and BER for both FSO and RF system are derived which uses Rician channel and 16QAM modulation scheme alongside hybrid FSO/RF system for weak, moderate, and strong turbulence regimes using Meijer-G function. © 2013 IEEE.Item Real-time big data analytics framework with data blending approach for multiple data sources in smart city applications(West University of Timisoara, 2020) Manjunatha, S.; Annappa, A.Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead to the continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implemented using a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification performs better in generating emergency alerts. © 2020 SCPE.Item Real-time emergency event detection system for public safety using multi-source data(Science and Engineering Research Support Society ijbsbt@sersc.org PO Box 5014Sandy Bay TAS 7005 Tasmania, 2020) Manjunatha, S.; Annappa, A.Public safety is an essential service offered in smart city projects to provide better safety and security for individuals and city infrastructure. The advancement in the field of Information Technology and the Internet of Things created much scope for using smart applications in the city to enhance the quality of service, leading to a better life in cities. This digitization generates a large amount of data within the city from distinct sources like social media, IoT, sensors, any user-generated content from smart applications. The data generated within the city are analyzed to discover valuable insights for producing better data-driven decisions and predictions, that are more crucial for efficient city administration. Making quick decisions and early predictions of crimes by real-time analysis of data help the smart policing system to provide better services in the city. This paper describes the scope of real-time big data analytics for finding appro-priate predictions and making quick decisions for public safety. A real-time big data analytics framework using multiple data sources is proposed for the smart policing service in the smart city environment. The framework is used to design a real-time emergency events detection system to help city administrators in taking quick actions for the safety of people and city infrastructure. The proposed system achieved an average accuracy of 73% for emergency event classification. © 2020 SERSC.Item Performance analysis of RoFSO links with spatial diversity over combined channel model for 5G in smart city applications(Elsevier B.V., 2020) Kumar, A.; Krishnan, P.The smart city concept improves the lives of citizens and optimizes the efficiency of city operations, services through the integration of information and communication technology with the help of the internet of things (IoT) and 5G techniques. The bandwidth demand for 5G, smart city, and IoT applications are fulfilled with wireless optical communications. Particularly, radio over free space optical (RoFSO) communication establishes a very attractive choice for interconnecting central base stations with remote antenna units. In this paper, we consider the transmission of orthogonal frequency division multiplexing (OFDM) radio signals with quadrature amplitude (QAM) modulation format through a free space optical link using spatial diversity mitigation technique. The atmosphere is modeled as the combined channel model which takes into account atmospheric attenuation, turbulence, and pointing errors. The atmospheric turbulence and pointing errors are modeled by Malaga, Beckmann, and Rayleigh distributions. The novel closed form BER expressions are derived for the proposed QAM OFDM RoFSO link with spatial diversity. The results are analyzed and plotted for different weather conditions (clear, haze, light fog), turbulence regimes (weak, moderate, strong), misalignment (weak, enhanced), the order of QAM and number of transceivers. The proposed RoFSO system is highly useful for 5G in smart city applications. © 2020 Elsevier B.V.Item Performance analysis of a RIS-assisted RoFSO communication system over Malaga distribution for smart city applications(Optica Publishing Group (formerly OSA), 2023) Kumar, A.; Krishnan, P.; Raj, A.A.B.Radio over free space optics (RoFSO) is one of the potential technologies that can satisfy the requirements of 5G services in a smart city. However, as RoFSO is line-of-sight (LOS) communication, one of its limitations is the occurrence of a skip zone in the targeted areas. In this work, a reconfigurable intelligent surface (RIS) is proposed as the solution to overcome this connection difficulty, which prevents signal blocking by generating LOS connections. These RIS modules extend the communication channel coverage, making it more intelligent and controllable. The performance analysis based on outage probability, ergodic channel capacity, and bit error rate has been performed using heterodyne detection. Malaga distribution has been used to model atmospheric turbulence. The exact closed-form expressions of the probability density function and cumulative distribution function of the end-to-end signal-to-noise ratio are derived. Exploiting these derived statistics, system performance is investigated through the ergodic channel capacity, outage probability, and average bit error rate for M-ary quadrature amplitude modulation and two binary modulation schemes: non-coherent binary frequency-shift keying and coherent binary phase-shift keying. Numerical results are compared among different turbulence conditions, link lengths, and scattering errors. The results show that the proposed RIS-assisted RoFSO technology has the potential to be effective for 5G smart city applications. © 2023 Optica Publishing Group.