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Author: search.filters.author.Chamola, VinaySubject: search.filters.subject.Internet of Things (IoT)

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Now showing 1 - 10 of 17
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    Quantum computing applications for Internet of Things
    (IET, 2023-11) Chamola, Vinay
    The rapidly developing discipline of quantum computing (QC) employs ideas from quantum physics to improve the performance of traditional computers and other devices. Because of the dramatically improved speed at which it processes data, it can be applied to various issues. QC has many potential applications, but three of the most exciting applications are unstructured search, quantum simulation, and network optimisation. Several existing technologies, such as machine learning, may benefit from its increased speed and precision. In this study, the authors will explore how the principles of QC might be applied to the Internet of Things (IoT) to improve its accuracy, speed, and security. Several approaches exist for achieving this goal, such as network optimisation in IoT using QC, faster computation at IoT endpoints, securing IoT using QC, a quantum sensor for IoT, quantum digital marketing, quantum-secured smart lock etc.
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    Three-Tier Indirect Tracing Model for Enhancing Epidemic Surveillance
    (IEEE, 2024-08) Chamola, Vinay
    In the wake of recent global health crises, effective contact tracing has emerged as a key tool in controlling infectious disease outbreaks. However, traditional contact tracing methods predominantly focus on direct tracing, often overlooking crucial indirect contacts. This study aims to address this gap by exploring scenarios where conventional tracing fails to identify all potential contacts. We argue for the necessity of indirect tracing, a component typically absent in traditional schemes, and demonstrate its importance across different stakeholders: end users, service providers, and healthcare professionals. To this end, we have designed an end-to-end application, available on GitHub, which significantly enhances the efficacy of contact tracing. Our approach effectively doubles or triples the maximum number of traceable individuals compared to traditional direct contact tracing methods, thereby offering a more comprehensive and effective tool for epidemic surveillance and control. This may lead to significant improvements in contact-tracing applications, thereby containing virus outbreaks more efficiently. In addition to the comprehensive analysis and development of the robust architecture, this study also emphasizes the broader implications and potential impact of incorporating indirect tracing into contact tracing efforts.
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    A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues and More
    (IEEE, 2024-12) Chamola, Vinay
    Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Cloud Computing, and Big Data Analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities—where they optimize traffic flow, energy consumption, and waste management—to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and Cloud Computing driving further expansion and application of Digital Twin technologies
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    Ambient Intelligence for Securing Intelligent Vehicular Networks: Edge-Enabled Intrusion and Anomaly Detection Strategies
    (IEEE, 2023-03) Alladi, Tejasvi; Chamola, Vinay
    The Internet of Things (IoT) is increasingly being deployed in smart city applications such as vehicular networks. The presence of a large number of communicating vehicles greatly increases the number and types of possible anomalies in the network. These anomalies could range from faulty vehicular data being broadcast by the vehicles to more catastrophic attacks such as disruptive attacks and Denial of Service (DoS) attacks to name a few. This calls for a need to develop robust security schemes such as intrusion detection and anomaly detection schemes. With a humongous growth in the amount of vehicular traffic data expected, artificial intelligence (AI)-based detection strategies need to be developed to address this burgeoning demand. In this article, we propose three AI-based intrusion detection strategies for vehicular network applications, leading to an effective Ambient Intelligence based vehicular network paradigm. The detection tasks are run on local edge servers deployed at the network edge. By showing the prediction results on an experimental testbed emulating the edge servers, we show the feasibility of deploying the proposed strategies in the vehicular network scenario.
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    Uniting cyber security and machine learning: Advantages, challenges and future research
    (Elsevier, 2022-09) Chamola, Vinay
    Machine learning (ML) is a subset of Artificial Intelligence (AI), which focuses on the implementation of some systems that can learn from the historical data, identify patterns and make logical decisions with little to no human interventions. Cyber security is the practice of protecting digital systems, such as computers, servers, mobile devices, networks and associated data from malicious attacks. Uniting cyber security and ML has two major aspects, namely accounting for cyber security where the machine learning is applied, and the use of machine learning for enabling cyber security. This uniting can help us in various ways, like it provides enhanced security to the machine learning models, improves the performance of the cyber security methods, and supports effective detection of zero day attacks with less human intervention. In this survey paper, we discuss about two different concepts by uniting cyber security and ML. We also discuss the advantages, issues and challenges of uniting cyber security and ML. Furthermore, we discuss the various attacks and provide a comprehensive comparative study of various techniques in two different considered categories. Finally, we provide some future research directions.
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    Blockchain-envisioned access control for internet of things applications: a comprehensive survey and future directions
    (Springer, 2022-07) Chamola, Vinay
    With rapid advancements in the technology, almost all the devices around are becoming smart and contribute to the Internet of Things (IoT) network. When a new IoT device is added to the network, it is important to verify the authenticity of the device before allowing it to communicate with the network. Hence, access control is a crucial security mechanism that allows only the authenticated node to become the part of the network. An access control mechanism also supports confidentiality, by establishing a session key that accomplishes secure communications in open public channels. Recently, blockchain has been implemented in access control protocols to provide a better security mechanism. The foundation of this survey article is laid on IoT, where a detailed description on IoT, its architecture and applications is provided. Further, various security challenges and issues, security attacks possible in IoT and their countermeasures are also provided. We emphasize on the blockchain technology and its evolution in IoT. A detailed description on existing consensus mechanisms and how blockchain can be used to overpower IoT vulnerabilities is highlighted. Moreover, we provide a comprehensive description on access control protocols. The protocols are classified into certificate-based, certificate-less and blockchain-based access control mechanisms for better understanding. We then elaborate on each use case like smart home, smart grid, health care and smart agriculture while describing access control mechanisms. The detailed description not only explains the implementation of the access mechanism, but also gives a wider vision on IoT applications. Next, a rigorous comparative analysis is performed to showcase the efficiency of all protocols in terms of computation and communication costs. Finally, we discuss open research issues and challenges in a blockchain-envisioned IoT network.
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    A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems
    (Elsevier, 2022-07) Alladi, Tejasvi; Chamola, Vinay
    With the rise of the Internet of Vehicles (IoV) and the number of connected vehicles increasing on the roads, Cooperative Intelligent Transportation Systems (C-ITSs) have become an important area of research. As the number of Vehicle to Vehicle (V2V) and Vehicle to Interface (V2I) communication links increases, the amount of data received and processed in the network also increases. In addition, networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient. Thus, there is a need to augment them with intelligent network intrusion detection techniques. Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times. However, given the expected large network size, there is a necessity for extensive data processing for use in such anomaly detection methods. Deep learning solutions are lucrative options as they remove the necessity for feature selection. Therefore, with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario, the need for deep learning-based techniques is all the more heightened. This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs). The proposed Deep Learning Classification Engines (DCLE) comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers. Vehicular data received by the Road Side Units (RSUs) is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper. The proposed classifiers identify 18 different vehicular behavior types, the F1-scores ranging from 95.58% to 96.75%, much higher than the existing works. By running the classifiers on testbeds emulating edge servers, the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.
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    Toward Safer Vehicular Transit: Implementing Deep Learning on Single Channel EEG Systems for Microsleep Detection
    (IEEE, 2023-01) Chamola, Vinay
    Technological interventions are becoming commonplace in everyday vehicles. But utilization of biosignals that can enhance the overall driving experience is still limited. Microsleep is one such issue that needs intervention, owing to the difficulty in its detection and social acceptance of using wearable BCI devices during transit. Microsleep is a short duration of sleep that lasts from few to several seconds. It could occur unconsciously without the person in context realizing it. This, therefore, happens before the deep sleep and could also occur when performing critical tasks such as driving on a highway. By using modern-day advancements in Internet of Things (IoT) and Machine Learning, we can provide efficient solutions to prevent accidents due to microsleep during vehicular transit. However, it is noteworthy that distinguishing microsleep using a single channel system is a challenge. We have explored this using datasets provided by International BCI Competition Committee. Given the fact that the participants’ values might not match the exact scenario, approaches for exploiting transitory phases using ANN/CNN have been developed and discussed in this paper. Transitory phases could include Wakefulness ↔ Non-Rapid Eye Movement-1 phase (NREM-1). Results show ≈95% increase in mean statistical agreements, which are represented by kappa values (CNN NREM 1 → CNN Transition) and ≈77% increase in mean kappa (ANN NREM 1 → ANN Transition). Hence, this work gives an initial indication whether classifiers trained on night sleep data can be used for microsleep detection in more real-world scenarios.
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    A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0
    (Elsevier, 2021-08) Chamola, Vinay; Gupta, Shashank
    There is a rapid increase in the adoption of emerging technologies like the Internet of Things (IoT), Unmanned Aerial Vehicles (UAV), Internet of Underground Things (IoUT), Data analytics in the agriculture domain to meet the increased food demand to cater to the increasing population. Agriculture 4.0 is set to revolutionize agriculture productivity by using Precision Agriculture (PA), IoT, UAVs, IoUT, and other technologies to increase agriculture produce for growing demographics while addressing various farm-related issues. This survey provides a comprehensive overview of how multiple technologies such as IoT, UAVs, IoUT, Big Data Analytics, Deep Learning Techniques, and Machine Learning methods can be used to manage various farm-related operations. For each of these technologies, a detailed review is done on how the technology is being used in Agriculture 4.0. These discussions include an overview of relevant technologies, their use cases, existing case studies, and research works that demonstrate the use of these technologies in Agriculture 4.0. This paper also highlights the various future research gaps in the adoption of these technologies in Agriculture 4.0
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    A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications
    (IEEE, 2021-02) Chamola, Vinay
    The Internet of Things (IoT) is playing a vital role in the rapid automation of the healthcare sector. The branch of IoT dedicated towards medical science is at times termed as Healthcare Internet of Things (H-IoT). The key elements of all H-IoT applications are data gathering and processing. Due to the large amount of data involved in healthcare, and the enormous value that accurate predictions hold, the integration of machine learning (ML) algorithms into H-IoT is imperative. This paper aims to serve both as a compilation as well as a review of the various state of the art applications of ML algorithms currently being integrated with H-IoT. Some of the most widely used ML algorithms have been briefly introduced and their use in various H-IoT applications has been analyzed in terms of their advantages, scope, and possible improvements. Applications have been divided into the domains of diagnosis, prognosis and spread control, assistive systems, monitoring, and logistics. In healthcare, practical use of a model requires it to be highly accurate and to have ample measures against security attacks. The applications of ML algorithms in H-IoT discussed in this paper have shown experimental evidence of accuracy and practical usability. The constraints and drawbacks of each of these applications have also been described.