Faculty Publications
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Publications by NITK Faculty
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Item Blockchain Based Artificial Intelligence of Things (AIoT) for Wildlife Monitoring(Springer Science and Business Media Deutschland GmbH, 2024) Madhusudhan, R.; Pravisha, P.Climate change poses a significant threat to wild animals and their habitats, increasing the chance of human-wildlife conflict. Traditional camera-based imaging systems are centralized and require operators to install the camera and monitor the video recording constantly. However, manually processing the massive number of images and videos gathered from camera traps is expensive and time-consuming. In this article, we will develop a framework for wildlife monitoring systems that make use of Artificial Intelligence of Things (AIoT), the Interplanetary File System (IPFS), and blockchain. A wildlife camera that uses AIoT to detect wild animal movement in real-time gathers the dynamic properties of animals. Cloud computing solutions are impractical for critical data management in wildlife monitoring due to their high latency and constant internet connectivity requirements. IPFS is a distributed file system that offers efficient data storage, distribution, and persistence, enabling offline-centric paradigms. In our framework, IPFS is used for permanent data storage, and the hash value of data is stored on a private blockchain. The data from multiple forest zones is stored on a consortium blockchain. A simulation is carried out using CNN and a method to improve the scalability of the framework is presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Item Enhancing Cybersecurity: Malicious Webpage Detection Using Machine and Deep Learning(Springer Science and Business Media Deutschland GmbH, 2025) Madhusudhan, R.; Surashe, S.V.; Pravisha, P.A wide range of techniques have been proposed for detecting malicious webpages; however, with the advent of more sophisticated webpage creation processes, it has become more challenging for these approaches to deliver satisfactory outcomes. Blacklisting and classification techniques were used in the past to identify malicious webpages. The classification of the websites becomes more challenging if they are not included on the blacklist. Machine learning techniques are gaining popularity in cybersecurity. One disadvantage of the machine learning model is that it becomes slower when using content-based features. While getting the whois feature, which gives creation, updation, and expiration dates of the webpage, the webpage is physically visited. Hence, there is a chance of malicious activity. Therefore, the process of feature extraction becomes challenging and time-consuming. This article uses the Term Frequency-Inverse Document Frequency (TF-IDF) and Natural Language Processing (NLP) methods to obtain the corpus for benign and malicious words present in the Unified Resource Locator (URL). An artificial neural network (ANN) has been employed to categorize websites as benign or malicious. A comparative analysis of artificial neural networks (ANN) with other machine learning approaches has been conducted. The experimental results demonstrate that ANN has the highest accuracy of 96.70%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Item Smart Land Registry System Using Blockchain: Challenges and Solutions(Springer Science and Business Media Deutschland GmbH, 2025) Madhusudhan, R.; Arya, D.; Pravisha, P.The land registry system fosters property investment and provides security to proprietors, thereby promoting economic development. Traditional centralized systems encounter challenges such as inefficiency, malicious activity, lack of transparency, etc. Blockchain is a distributed ledger that maintains data over a network without the control of a central authority. It uses a peer-to-peer system and consensus mechanisms to validate and permanently add transaction blocks, making data tamper-proof and immutable. The blockchain-based smart land registry system offers several benefits, including efficiency, security, transparency, and immutability. Blockchains can be configured as public, private, hybrid, or consortium, each with specific advantages and use cases. The blockchain network uses a consensus mechanism to ensure that only verified transactions are added to a block. A good combination of blockchain and consensus algorithms has solved various problems in land registration. In this article, a systematic study is conducted on blockchain-based land registry systems to identify research gaps in this area. The article highlights the technologies used for developing these systems and the classification of systems based on the services provided. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Item A Blockchain-Enabled IoT Framework for NICU Infant Health Monitoring System(Institute of Electrical and Electronics Engineers Inc., 2023) Madhusudhan, R.; Pravisha, P.According to the World Health Organization (WHO), 15 million infants are born prematurely each year. In the neonatal intensive care unit (NICU), the critical health parameters of newborn babies must be monitored precisely and in real time. Approximately one million preterm babies suffer morbidity before the age of five due to preterm birth and complications associated with preterm delivery. The neonatal intensive care unit (NICU) requires accurate, real-time monitoring of newborn infants' vital health parameters. One of the challenges encountered by the majority of hospitals is the lack of systems that can track real-time health parameters and notify doctors and parents to indicate any neonatal critical conditions. This research article presents a framework that incorporates IoT, fog, deep learning technologies, Blockchain, and decentralized cloud for NICU newborn health monitoring. The development of the Internet of Things (IoT) and blockchain technologies provides wide opportunities for enhancing the data management of neonatal intensive care units. By integrating IoT devices comprising wearable sensors and smart monitors the system gets real-time data on vital signs like heart rate, temperature, blood oxygen levels, and breathing rate. Fog computing is used for the instantaneous analysis of critical data, and an efficient deep learning algorithm will be implemented at the fog layer to classify data into either critical or non-critical data. Since fog has limited resources, a private blockchain is used to store critical data. The critical data is stored temporarily on a private blockchain and permanently on a decentralized cloud. © 2023 IEEE.Item FL-DABE-BC: A Privacy-Enhanced Decentralized Authentication and Secure Communication Framework for FL in IoT-Enabled Smart Cities(Association for Computing Machinery, Inc, 2025) Narkedimilli, S.; Pravisha, P.; Sriram, A.V.; Raghav, S.; Vangapandu, P.Federated Learning (FL) offers a distributed approach to machine learning that preserves data privacy by avoiding the exchange of sensitive IoT sensor information. This paper introduces a novel IoT framework that integrates advanced security tools to tackle key privacy and security challenges. It employs Decentralized Attribute-Based Encryption (DABE) for decentralized authentication and data encryption, Homomorphic Encryption (HE) for secure computations on encrypted data, Secure Multi-Party Computation (SMPC) for collaborative processing, and Blockchain for distributed ledger management and transparent communication. In this system, IoT devices encrypt data locally with DABE, while initial model training occurs on cloud servers within an immutable blockchain network that supports peer-to-peer authentication. Encrypted model weights are then transferred to the fog layer via HE and aggregated using SMPC, after which the FL server updates and distributes the global model to the IoT devices. This innovative framework effectively addresses the challenges of secure decentralized learning, enabling privacy-preserving, efficient, and secure federated learning for IoT applications and real-time analytics. © 2025 Copyright held by the owner/author(s).