Srinivas Koppu

Bio: Srinivas Koppu is an academic researcher from VIT University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 6, co-authored 12 publications receiving 145 citations.

Digital Twins for Healthcare 4.0 - Recent Advances, Architecture, and Open Challenges

Abstract: Recent trends have shown a widespread increase in the landscape of digital healthcare (i.e., Healthcare 4.0) services, such as personalized healthcare, intelligent rehabilitation, telemedicine, and smart diet management, among others. These healthcare services are based on a variety of diverse requirements. Fulfilling these requirements require proactive intelligent analytics and self-sustainability of networks. Self-sustainability enables the operation of a network with minimum possible interaction from the end-users/network operators, whereas proactive intelligent analytics enables efficient management of resources in response to users' requests. To enable healthcare 4.0 with proactive online analytics and self-sustainability, one can leverage digital twins. In this article, we present an overview and recent advances of digital twins for healthcare 4.0. An architecture of digital twins for healthcare is also proposed. Furthermore, we present several use cases of digital twins. Finally, we present open research challenges with possible solutions.

EC-ElGamal and Genetic Algorithm-Based Enhancement for Lightweight Scalable Blockchain in IoT Domain

Abstract: Security, data privacy and decentralization are significant challenges in the Internet of Things (IoT) domain. These challenges are inherited attribute of another emerging technology, Blockchain. This enforced convergence of IoT and Blockchain, attracting researchers to study on the effective use of Blockchain’s strength to solve the challenges of IoT. Rapid IoT adoption requires standardization and mature solution on security, data protection for compliance and performance for commercialization. These demands made a surge in variant blockchain flavours and combinations catering to different problems, and one such is Lightweight Scalable Blockchain (LSB). LSB had considerable caveats that require improvement for better adoption in the IoT domain. This paper focuses on encrypting transaction transmission, improving transaction flow, block validation, hash quality, hash rate and storage cost to improvise security and performance. The experimental evaluation is demonstrated on data from the temperature sensor to showcase superior applicability of the proposed work in the IoT domain. Implementation and result comparison with conventional LSB proves, the following achievements 1) An additional layer of transaction encryption using hybrid Elliptic Curve ElGamal (EC-ElGamal) method increases the security of the transmitted transaction for security enhancement. 2) Obtained 20% reduction in transaction processing time, 22% reduction on block validation processing time, 53% improvement on the hash operation and quality with an overall 7% saving on the storage cost thereby increased the overall performance.

A metaheuristic optimization approach for energy efficiency in the IoT networks

Abstract: © 2020 John Wiley & Sons, Ltd. Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state-of-the-art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches.

Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks

Abstract: The growing development of IoT (Internet of Things) devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cyber-attacks. Many of these attacks have effectively accomplished their malicious goals because intruders conduct cyber-attacks using novel and innovative techniques. An anomaly-based IDS (Intrusion Detection System) uses machine learning techniques to detect and classify attacks in IoT networks. In the presence of unpredictable network technologies and various intrusion methods, traditional machine learning techniques appear inefficient. In many research areas, deep learning methods have shown their ability to identify anomalies accurately. Convolutional neural networks are an excellent alternative for anomaly detection and classification due to their ability to automatically categorize main characteristics in input data and their effectiveness in performing faster computations. In this paper, we design and develop a novel anomaly-based intrusion detection model for IoT networks. First, a convolutional neural network model is used to create a multiclass classification model. The proposed model is then implemented using convolutional neural networks in 1D, 2D, and 3D. The proposed convolutional neural network model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Transfer learning is used to implement binary and multiclass classification using a convolutional neural network multiclass pre-trained model. Our proposed binary and multiclass classification models have achieved high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.

Hand gesture classification using a novel CNN-crow search algorithm

Abstract: Human–computer interaction (HCI) and related technologies focus on the implementation of interactive computational systems. The studies in HCI emphasize on system use, creation of new techniques that support user activities, access to information, and ensures seamless communication. The use of artificial intelligence and deep learning-based models has been extensive across various domains yielding state-of-the-art results. In the present study, a crow search-based convolution neural networks model has been implemented in gesture recognition pertaining to the HCI domain. The hand gesture dataset used in the study is a publicly available one, downloaded from Kaggle. In this work, a one-hot encoding technique is used to convert the categorical data values to binary form. This is followed by the implementation of a crow search algorithm (CSA) for selecting optimal hyper-parameters for training of dataset using the convolution neural networks. The irrelevant parameters are eliminated from consideration, which contributes towards enhancement of accuracy in classifying the hand gestures. The model generates 100 percent training and testing accuracy that justifies the superiority of the model against traditional state-of-the-art models.