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"Ranga, Virender"
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Image processing and intelligent computing systems
\"There is a drastic growth in multimedia data. Even during the Covid-19 pandemic, we observed that the images helped doctors immensely in fast detection of Covid-19 infection in patients. There are many critical applications where images play a vital role. These applications use raw image data to extract some useful information about the world around us. Quick extraction of valuable information from raw images is one challenge that academicians and professionals face nowadays. This is where image processing comes into action. Image processing's primary purpose is to get an enhanced image or extract some useful information from it. Therefore, there is a major need for some technique or system that addresses this challenge. Intelligent Systems have emerged as a solution to address quick image information extraction. In simple words, an Intelligent System can be defined as a mathematical model that adapts itself to deal with the problems' dynamicity. These systems learn how to act so it can reach their objectives. Intelligent System helps accomplish various image processing functions like enhancement, segmentation, reconstruction, object detection, and morphing. The advent of Intelligent Systems in the image processing field has leveraged many critical applications for humankind. These critical applications include factory automation, biomedical imaging analysis, and decision-econometrics, Intelligent Systems and challenges\"-- Provided by publisher.
Book
Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope
Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.
Journal Article
CoSec-RPL: detection of copycat attacks in RPL based 6LoWPANs using outlier analysis
The IPv6 routing protocol for low-power and lossy networks (RPL) is the standard routing protocol for IPv6 based low-power wireless personal area networks (6LoWPANs). In RPL protocol, DODAG information object (DIO) messages are used to disseminate routing information to other nodes in the network. A malicious node may eavesdrop DIO messages of its neighbor nodes and later replay the captured DIO many times with fixed intervals. In this paper, we present and investigate one of the severe attacks named as a non-spoofed copycat attack, a type of replay based DoS attack against RPL protocol. It is shown that the non-spoofed copycat attack increases the average end-to-end delay (AE2ED) and packet delivery ratio of the network. Thus, to address this problem, an intrusion detection system (IDS) named CoSec-RPL is proposed in this paper. The attack detection logic of CoSec-RPL is primarily based on the idea of outlier detection (OD). CoSec-RPL significantly mitigates the effects of the non-spoofed copycat attack on the network’s performance. The effectiveness of the proposed IDS is compared with the standard RPL protocol. The experimental results indicate that CoSec-RPL detects and mitigates non-spoofed copycat attack efficiently in both static and mobile network scenarios without adding any significant overhead to the nodes. To the best of our knowledge, CoSec-RPL is the first RPL specific IDS that utilizes OD for intrusion detection in 6LoWPANs.
Journal Article
The impact of copycat attack on RPL based 6LoWPAN networks in Internet of Things
IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is the standard network layer protocol for achieving efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Resource-constrained and non-tamper resistant nature of smart sensor nodes makes RPL protocol susceptible to different threats. An attacker may use insider or outsider attack strategy to perform Denial-of-Service (DoS) attacks against RPL based networks. Security and Privacy risks associated with RPL protocol may limit its global adoption and worldwide acceptance. A proper investigation of RPL specific attacks and their impacts on an underlying network needs to be done. In this paper, we present and investigate one of the catastrophic attacks named as a copycat attack, a type of replay based DoS attack against the RPL protocol. An in-depth experimental study for analyzing the impacts of the copycat attack on RPL has been done. The experimental results show that the copycat attack can significantly degrade network performance in terms of packet delivery ratio, average end-to-end delay, and average power consumption. To the best of our knowledge, this is the first paper that extensively studies the impact of RPL specific replay mechanism based DoS attack on 6LoWPAN networks.
Journal Article
Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security
The recent years have garnered huge attention towards the Internet of Things (IoT) because it enables its consumers to improve their lifestyles and professionally keep up with the technological advancements in the cyber-physical world. The IoT edge devices are heterogeneous in terms of the technology they are built on and the storage file formats used. These devices require highly secure modes of mutual authentication to authenticate each other before actually sending the data. Mutual authentication is a very important aspect of peer-to-peer communication. Secure session keys enable these resource-constrained devices to authenticate each other. After successful authentication, a device can be authorized and can be granted access to shared resources. The need for validating a device requesting data transfer to avoid data privacy breaches that may compromise confidentiality and integrity. Blockchain and artificial intelligence (AI) both are extensively being used as an integrated part of IoT networks for security enhancements. Blockchain provides a decentralized mechanism to store validated session keys that can be allotted to the network devices. Blockchain is also used to load balance the stressing edge devices during low battery levels. AI on the other hand provides better learning and adaptiveness towards IoT attacks. The integration of newer technologies in IoT key management yields enhanced security features. In this article, we systematically survey recent trending technologies from an IoT security point of view and discuss traditional key security mechanisms. This article delivers a comprehensive quality study for researchers on authentication and session keys, integrating IoT with blockchain and AI-based authentication in cybersecurity.
Journal Article
EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence
Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70% of cases, these drugs are not effective, and surgery is the only solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system “EpilNet” is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13% for five classes, leading to a significant increase of about 6-7% compared to related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals.
Journal Article
Swarm Intelligence-based Partitioned Recovery in Wireless Sensor Networks
The failure rate of sensor nodes in Heterogeneous Wireless Sensor Networks is high due to the use of low battery-powered sensor nodes in a hostile environment. Networks of this kind become non-operational and turn into disjoint segmented networks due to large-scale failures of sensor nodes. This may require the placement of additional highpower relay nodes. In this paper, we propose a network partition recovery solution called Grey Wolf, which is an optimizer algorithm for repairing segmented heterogeneous wireless sensor networks. The proposed solution provides not only strong bi-connectivity in the damaged area, but also distributes traffic load among the multiple deployed nodes to enhance the repaired network’s lifetime. The experiment results show that the Grey Wolf algorithm offers a considerable performance advantage over other state-of-the-art approaches.
Journal Article
Performance analysis of RPL protocol in different nodes positioning using Contiki Cooja
The routing protocol for low-power and lossy networks (RPL) protocol, tailored for the internet of things and wireless sensor networks, enhances communication efficiency in low-power and lossy networks. This study employs the Contiki Cooja simulator to analyze RPL performance across random, linear, and elliptical node positions. Parameters, including power consumption, duty cycles, inter-packet times, packet reception, and routing metrics, are evaluated. Linear positioning demonstrates superiority across critical parameters, including power consumption, inter-packet time, packet loss, and routing efficiency.
Journal Article
Restoration of lost connectivity of partitioned wireless sensor networks
The lost connectivity due to failure of large-scale nodes plays major role to degrade the system performance by generating unnecessary overhead or sometimes totally collapse the active network. There are many issues and challenges to restore the lost connectivity in an unattended scenario, i.e. how many recovery nodes will be sufficient and on which locations these recovery nodes have to be placed. A very few centralized and distributed approaches have been proposed till now. The centralized approaches are good for a scenario where information about the disjoint network, i.e. number of disjoint segments and their locations are well known in advance. However, for a scenario where such information is unknown due to the unattended harsh environment, a distributed approach is a better solution to restore the partitioned network. In this paper, we have proposed and implemented a semi-distributed approach called Relay node Placement using Fermat Point. The proposed approach is capable of restoring lost connectivity with small number of recovery relay nodes and it works for any number of disjoint segments. The simulation experiment results show effectiveness of our approach as compared to existing benchmark approaches.
Journal Article
A deep learning approach to dysarthric utterance classification with BiLSTM-GRU, speech cue filtering, and log mel spectrograms
Assessing the intelligibility of dysarthric speech, characterized by intricate speaking rhythms presents formidable challenges. Current techniques for objectively testing speech intelligibility are burdensome and subjective, particularly struggling with dysarthric spoken utterances. To tackle these hurdles, our method conducts an ablation analysis across speakers afflicted with speech impairment. We utilize a unified approach that incorporates both auditory and visual elements to improve the classification of dysarthric spoken utterances. In our quest to enhance spoken utterance recognition, we propose employing two distinctive extractive transformer-based approaches. Initially, we employ SepFormer to refine the speech signal, prioritizing the enhancement of signal clarity. Subsequently, we feed the improved audio samples into Swin transformer after converting them into log mel spectrograms. Additionally, we harness the power of the Swin transformer for visual classification, trained on a dataset of 14 million annotated images from ImageNet. The pre-trained scores from the Swin transformer are utilized as input for the deep bidirectional long short-term memory with gated recurrent unit (deep BiLSTM-GRU) model, facilitating the classification of spoken utterances. Our proposed deep BiLSTM-GRU model for spoken utterance classification yields impressive results on the EasyCall speech corpus, encompassing cognitive characteristics across spoken utterances ranging from 10 to 20, delivered by both healthy individuals and those with dysarthria. Notably, our results showcase an accuracy of 98.56% for 20 utterances in male speakers, 95.11% in female speakers, and 97.64% in combined male and female speakers. Across diverse scenarios, our approach consistently achieves remarkable accuracy, surpassing other contemporary methods, all without necessitating data augmentation.
Journal Article