Browsing by Author "Chamola, Vinay"

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5G network slice for digital real-time healthcare system powered by network data analytics
(Elsevier, 2021) Chamola, Vinay
In the wake of the COVID-19 pandemic, where almost the entire global healthcare ecosystem struggled to handle patients, it’s evident that the healthcare segment needs a virtual real-time digital support system. The recent advancements in technology have enabled machine-to-machine communication, enhanced mobile broadband, and real-time biometric data analytics. These could potentially fulfill the requirements of an end-to-end digital healthcare system. For building such a system, there is also a need for a dedicated and specialized communication network. Such a system will not only support dynamic throughput, latency and payload but also provide guaranteed QoS (Quality of Service) at every instant. The motive of our study was to define an implementable low-level architecture for the digital healthcare system by using the 5G Network Slice that incorporates all these features. Best-in-class wearable devices will collect the biometric data and transmit it via the 5G network slice. Data analytics is then applied to the collected data to build a knowledge graph used for quick predictions and prescriptions. The architecture also keeps in mind the security and integrity aspects of healthcare data.
Advancements in Yoga Pose Estimation Using Artificial Intelligence: A Survey
(Bentham Science, 2024) Chamola, Vinay; Rout, Bijay Kumar
Human pose estimation has been a prevalent field of computer vision and sensing study. In recent years, it has made many advances that have helped humanity in the fields of sports, surveillance, healthcare, etc. Yoga is an ancient science intended to improve physical, mental and spiritual wellbeing. It involves many kinds of asanas or postures that a practitioner can perform. Thus, the benefits of pose estimation can also be used for Yoga to help users assume Yoga postures with better accuracy. The Yoga practitioner can detect their own current posture in real-time, and the pose estimation method can provide them with corrective feedback if they commit mistakes. Yoga pose estimation can also help with remote Yoga instruction by the expert teacher, which can be a boon during a pandemic. This paper reviews various Machine Learning, Artificial Intelligence-enabled techniques available for real-time pose estimation and research pursued recently. We classify them based on the input they use for estimating the individual's pose. We also discuss multiple Yoga posture estimation systems in detail. We discuss the most commonly used keypoint estimation techniques in the existing literature. In addition to this, we discuss the real-time performance of the presented works. The paper further discusses the datasets and evaluation metrics available for pose estimation.
Advancing Medical Imaging Through Generative Adversarial Networks: A Comprehensive Review and Future Prospects
(Springer, 2024-06) Chamola, Vinay
In medical imaging, traditional methods have long been relied upon. However, the integration of Generative Adversarial Networks (GANs) has sparked a paradigm shift, ushering in a new era of innovation. Our comprehensive investigation explores the groundbreaking impact of GANs on medical imaging, examining the evolution from traditional techniques to GAN-driven approaches. Through meticulous analysis, we dissect various aspects of GANs, encompassing their taxonomy, historical progression, and diverse iterations such as Self-Attention GANs (SAGAN), Conditional GANs, and Progressive Growing GANs (PGGAN). Complemented by a practical case study, we scrutinize the extensive applications of GANs, spanning image generation, reconstruction, enhancement, segmentation, and super-resolution. Despite promising prospects, enduring challenges including data scarcity, interpretability issues, and ethical concerns persist. Looking ahead, we anticipate advancements in personalized and pathological image generation, cross-modal synthesis, real-time interactive image generation, and enhanced anomaly detection. Through this review, we underscore the transformative potential of GANs in reshaping medical imaging practices, while also outlining avenues for future research endeavors.
Advancing Remote Healthcare Using Humanoid and Affective Systems
(IEEE, 2022-09) Chamola, Vinay
Social distancing and remote work are becoming more prevalent in the post-covid world. At the same time, there is a huge demand for remote healthcare sessions as well. Although a growing number of such sessions are now utilizing online platforms as a medium of communication, other critical parameters such as the affective state and other feedback opportunities are lost during the transmission of this digital information. This paper presents a solution that leverages a brain-computer interface system for this affective feedback and a humanoid robot for teaching effectively during remote sessions. The solution uses Kinect as a sensing mechanism for the trainer. It utilizes state-of-the-art deep learning algorithms at the back-end to understand the emotional state of the trainee. The training poses (from humanoid’s camera feed and kinect) are calculated using AlphaPose compared using inverse kinematics. To ascertain the trainees’ state (high valence and arousal vs. low valence and arousal), a Capsule Network was used that gives an average accuracy of 90.4% for this classification with a low average inference time of 14.3ms on the publicly available DREAMER and AMIGOS datasets. The system also allows real-time communication through the humanoid, making this experience even more distinct for the trainee.
AGD-Net: Attention-Guided Dense Inception U-Net for Single-Image Dehazing
(Springer, 2023-12) Chamola, Vinay; Narang, Pratik
Image hazing poses a significant challenge in various computer vision applications, degrading the visual quality and reducing the perceptual clarity of captured scenes. The proposed AGD-Net utilizes a U-Net style architecture with an Attention-Guided Dense Inception encoder-decoder framework. Unlike existing methods that heavily rely on synthetic datasets which are based on CARLA simulation, our model is trained and evaluated exclusively on realistic data, enabling its effectiveness and reliability in practical scenarios. The key innovation of AGD-Net lies in its attention-guided mechanism, which empowers the network to focus on crucial information within hazy images and effectively suppress artifacts during the dehazing process. The dense inception modules further advance the representation capabilities of the model, facilitating the extraction of intricate features from the input images. To assess the performance of AGD-Net, a detailed experimental analysis is conducted on four benchmark haze datasets. The results show that AGD-Net significantly outperforms the state-of-the-art methods in terms of PSNR and SSIM. Moreover, a visual comparison of the dehazing results further validates the superior performance gains achieved by AGD-Net over other methods. By leveraging realistic data exclusively, AGD-Net overcomes the limitations associated with synthetic datasets which are based on CARLA simulation, ensuring its adaptability and effectiveness in real-world circumstances. The proposed AGD-Net offers a robust and reliable solution for single-image dehazing, presenting a significant advancement over existing methods.
AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture
(IEEE, 2021-04) Chamola, Vinay
Aerial inspection of agricultural regions can provide crucial information to safeguard from numerous obstacles to efficient farming. Farmland anomalies such as standing water, weed clusters, hamper the farming practices, which causes improper use of farm area and disrupts agricultural planning. Monitoring of farmland and crops through Internet-of-Things (IoT)-enabled smart systems has potential to increase the efficiency of modern farming techniques. Unmanned Aerial Vehicle (UAV)-based remote sensing is a powerful technique to acquire farmland images on a large scale. Visual data analytics for automatic pattern recognition from the collected data is useful for developing Artificial intelligence (AI)-assisted farming models, which holds great promise in improving the farming outputs by capturing the crop patterns, farmland anomalies and providing predictive solutions to the inherent challenges faced by farmers. In this work, we propose a deep learning framework AgriSegNet for automatic detection of farmland anomalies using multiscale attention semantic segmentation of UAV acquired images. The proposed model is useful for monitoring of farmland and crops to increase the efficiency of precision farming techniques.
AI-Enabled Object Detection in UAVs: Challenges, Design Choices, and Research Directions
(IEEE, 2021-08) Narang, Pratik; Chamola, Vinay
Unmanned aerial vehicles (UAVs) are emerging as a powerful tool for various industrial and smart city applications. UAVs coupled with various sensors can perform many cognitive tasks such as object detection, surveillance, traffic management, and urban planning. Deep learning has emerged as a popular technique to speed up the processing of high-dimensional data like images and videos, which has led to several applications in surveillance and autonomous driving. However, the area of aerial object detection has been understudied. This work proposes a deep learning approach for detection of objects in aerial scenes captured by UAVs. Our work first categorizes the current methods for aerial object detection using deep learning techniques and discusses how the task is different from general object detection scenarios. We delineate the specific challenges involved and experimentally demonstrate the key design decisions that significantly affect the accuracy and robustness of models. We further propose an optimized architecture that utilizes these optimal design choices along with the recent Res-NeSt backbone to achieve superior performance in aerial object detection. Lastly, we propose several research directions to inspire further advancement in aerial object detection.
AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges
(Springer, 2021-03) Narang, Pratik; Narang, Pratik; Chamola, Vinay
The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients’ symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques.
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.
An analysis of energy consumption and carbon footprints of cryptocurrencies and possible solutions
(Elsevier, 2023-02) Chamola, Vinay; Sangwan, Kuldip Singh
There is an urgent need to control global warming caused by humans to achieve a sustainable future. CO2 levels are rising steadily, and while countries worldwide are actively moving toward the sustainability goals proposed during the Paris Agreement in 2015, we are still a long way to go from achieving a sustainable mode of global operation. The increased popularity of cryptocurrencies since the introduction of Bitcoin in 2009 has been accompanied by an increasing trend in greenhouse gas emissions and high electrical energy consumption. Popular energy tracking studies (e.g., Digiconomist and the Cambridge Bitcoin Energy Consumption Index (CBECI)) have estimated energy consumption ranges from 29.96 TWh to 135.12 TWh and 26.41 TWh to 176.98 TWh, respectively for Bitcoin as of July 2021, which are equivalent to the energy consumption of countries such as Sweden and Thailand. The latest estimate by Digiconomist on carbon footprints shows a 64.18 MtCO2 emission by Bitcoin as of July 2021, close to the emissions by Greece and Oman. This review compiles estimates made by various studies from 2018 to 2021. We compare the energy consumption and carbon footprints of these cryptocurrencies with countries around the world and centralized transaction methods such as Visa. We identify the problems associated with cryptocurrencies and propose solutions that can help reduce their energy consumption and carbon footprints. Finally, we present case studies on cryptocurrency networks, namely, Ethereum 2.0 and Pi Network, with a discussion on how they can solve some of the challenges we have identified.
Applications of blockchain in unmanned aerial vehicles: A review
(Elsevier, 2020-06) Alladi, Tejasvi; Chamola, Vinay
The recent advancement in Unmanned Aerial Vehicles (UAVs) in terms of manufacturing processes, and communication and networking technology has led to a rise in their usage in civilian and commercial applications. The regulations of the Federal Aviation Administration (FAA) in the US had earlier limited the usage of UAVs to military applications. However more recently, the FAA has outlined new enforcement that will also expand the usage of UAVs in civilian and commercial applications. Due to being deployed in open atmosphere, UAVs are vulnerable to being lost, destroyed or physically hijacked. With the UAV technology becoming ubiquitous, various issues in UAV networks such as intra-UAV communication, UAV security, air data security, data storage and management, etc. need to be addressed. Blockchain being a distributed ledger protects the shared data using cryptography techniques such as hash functions and public key encryption. It can also be used for assuring the truthfulness of the information stored and for improving the security and transparency of the UAVs. In this paper, we review various applications of blockchain in UAV networks such as network security, decentralized storage, inventory management, surveillance, etc., and discuss some broader perspectives in this regard. We also discuss various challenges to be addressed in the integration of blockchain and UAVs and suggest some future research directions.
Artificial Intelligence (AI)-Empowered Intrusion Detection Architecture for the Internet of Vehicles
(IEEE, 2021-06) Alladi, Tejasvi; Chamola, Vinay
Recent advances in the Internet of Things (IoT) and the adoption of IoT in vehicular networks have led to a new and promising paradigm called the Internet of Vehicles (IoV). However, the mode of communication in IoV being wireless in nature poses serious cybersecurity challenges. With many vehicles being connected in the IoV network, the vehicular data is set to explode. Traditional intrusion detection techniques may not be suitable in these scenarios with an extremely large amount of vehicular data being generated at an unprecedented rate and with various types of cybersecurity attacks being launched. Thus, there is a need for the development of advanced intrusion detection techniques capable of handling possible cyberattacks in these networks. Toward this end, we present an artificial intelligence (AI)-based intrusion detection architecture comprising Deep Learning Engines (DLEs) for identification and classification of the vehicular traffic in the IoV networks into potential cyberattack types. Also, taking into consideration the mobility of the vehicles and the realtime requirements of the IoV networks, these DLEs will be deployed on Multi-access Edge Computing (MEC) servers instead of running on the remote cloud. Extensive experimental results using popular evaluation metrics and average prediction time on a MEC testbed demonstrate the effectiveness of the proposed scheme.
Artificial Intelligence Empowered Digital Twin and NFT-Based Patient Monitoring and Assisting Framework for Chronic Disease Patients
(IEEE, 2024-03) Chamola, Vinay
People suffering from chronic diseases require continuous support in monitoring their nutrition, diagnostic tests, medication, and daily activity tracking. Given the low ratio of patients to healthcare providers, it becomes infeasible to provide one-to-one support to patients. Furthermore the existing online medical consultation platforms are costly for regular approach. Addressing these issues, we propose to monitor and assist patients suffering from chronic disease using a novel Al-based and IoT-supported digital twin platform. A digital twin of a patient grows with the patient, and it helps in continuous and remote patient monitoring. Further, the digital twin enables the creation of patient-specific personalized treatment models, enabling doctors to conduct virtual simulations of the suitability of certain drugs and procedures. The data collected from the digital twin is fed to machine learning models for intelligent analysis, feedback, and support. The proposed solution incorporates five essential machine learning models using novel algorithms for drug recommendation, chronic disease stage detection, nutrition tracking and recommendation, patient activity tracking, and patient data anonymization. Addressing patients' lack of motivation to participate in emerging patient monitoring frameworks, we incorporate an incentive mechanism rooted in Non-Fungible Tokens (NFTs) to encourage active participation in patients, which also has the added benefit of helping patients to store their historical medical data securely.
Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management
(Springer, 2022) Chamola, Vinay
Healthcare professionals, patients, and other stakeholders have been storing medical prescriptions and other relevant reports electronically. These reports contain the personal information of the patients, which is sensitive data. Therefore, there exists a need to store these records in a decentralized model (using IPFS and Ethereum decentralized application) to provide data and identity protection. Many patients recurrently visit doctors and undergo treatments while receiving different prescriptions and reports. In case of an emergency, the doctors and attendants may need and benefit from the patients’ medical history. However, they are unable to go through medical history and a wide range of previous reports and prescriptions due to time constraints. In this paper, we propose an AI-assisted blockchain-based framework in which the stored medical records (handwritten prescriptions, printed prescriptions, and printed reports) are stored and processed using various AI techniques like optical character recognition (OCR) to form a single patient medical history report. The report concisely presents only the crucial information for convenience and perusal and is stored securely over a decentralized blockchain network for later use.
Artificial Intelligence-Empowered Optimal Roadside Unit (RSU) Deployment Mechanism for Internet of Vehicles (IoV)
(IEEE, 2022) Gupta, Shashank; Chamola, Vinay
Currently, the world is witnessing a huge growth in additional computing proficiency and extensive network coverage capability, which resulted in a paradigm shift from VANETs to Internet of Vehicles (IoV). Moreover, enhanced network capabilities facilitate enabling of IoV technology for latency-critical applications in energy-constrained smart IoT devices. However, IoV networks demand energy efficiency due to its dynamic nature for which Roadside Units (RSUs) are critical. However, in cities, huge deployment of RSUs and their maintenance is expensive in IoV infrastructure, requiring a trade-off between the network coverage and installation-related expenses. Also, the latency issues in IoV are highly dependent on the positioning of accessible RSUs. Motivated by the above highlighted issues, we propose an upgraded RSU placement method to boost network efficiency through placement of RSUs in optimal locations in a given road map. The Memetic Framework-based Optimal RSU Deployment (MFRD) algorithm is proposed to maximize the coverage area among the vehicles in an IoV and minimize the overlap in the coverage of the different RSUs. We observed from simulation results based on real-world maps that MFRD yields a significantly higher fitness score as compared to the existing state-of-the-art in terms of optimal positioning of the RSUs.
Artificial Intelligence-Empowered Optimal Roadside Unit (RSU) Deployment Mechanism for Internet of Vehicles (IoV)
(IEEE, 2022) Chamola, Vinay
Currently, the world is witnessing a huge growth in additional computing proficiency and extensive network coverage capability, which resulted in a paradigm shift from VANETs to Internet of Vehicles (IoV). Moreover, enhanced network capabilities facilitate enabling of IoV technology for latency-critical applications in energy-constrained smart IoT devices. However, IoV networks demand energy efficiency due to its dynamic nature for which Roadside Units (RSUs) are critical. However, in cities, huge deployment of RSUs and their maintenance is expensive in IoV infrastructure, requiring a trade-off between the network coverage and installation-related expenses. Also, the latency issues in IoV are highly dependent on the positioning of accessible RSUs. Motivated by the above highlighted issues, we propose an upgraded RSU placement method to boost network efficiency through placement of RSUs in optimal locations in a given road map. The Memetic Framework-based Optimal RSU Deployment (MFRD) algorithm is proposed to maximize the coverage area among the vehicles in an IoV and minimize the overlap in the coverage of the different RSUs. We observed from simulation results based on real-world maps that MFRD yields a significantly higher fitness score as compared to the existing state-of-the-art in terms of optimal positioning of the RSUs.
Automotive Cybersecurity Scheme for Intrusion Detection in CAN-Driven Artificial Intelligence of Things
(Wiley, 2024-12) Chamola, Vinay
The Artificial Intelligence of Things (AIoT) is applicable for various domains, that is, smart healthcare, smart cities, industrial sectors, transportation systems, and many more. Controller area network (CAN) facilitates the integration of the sensing devices, thus enables them to send their data for analysis to various artificial intelligence (AI) algorithms. CAN also provides reliability and fault tolerance to the AIoT applications, as it has been designed to deal with noisy environments. Thus, CAN-driven AIoT improves the efficiency, reliability, and functionalities of the devices and systems, which is very much needed for various AIoT applications. However, it is vulnerable to various cyber-attacks like message replay, modification attack, fuzzy attack, denial of service, and spoofing the RPM gauge or drive gear. Therefore, an intrusion detection system (IDS) is required to detect attacks on the CAN bus. In this paper, we propose a lightweight and efficient intrusion detection system which successfully detects multiple intrusions based on the type of attack on CAN bus without causing additional traffic overhead to the ongoing communications (in short, ACID-CAN). The presented mechanism is very much needed for the CAN-driven AIoT applications. Experimental results show that the proposed ACID-CAN successfully detects intrusions even when the amount of intrusion data is reduced to of normal data. The obtained results were compared with those of previous studies in the field of CANs intrusion detection, and it has been noted that the proposed ACID-CAN offers comparable and better results.
Balancing consistency and performance in edge-cloud transaction management
(Elsevier, 2025-06) Chamola, Vinay
The proliferation of Internet of Things (IoT) devices has led to edge-cloud computing paradigms where resource-constrained edge devices connect to cloud servers. However, traditional concurrency control methods like two-phase locking (2 PL) and optimistic concurrency control (OCC) are inefficient in these heterogeneous environments. This paper presents adaptive transaction management protocols for edge-cloud systems. We propose EC-Lock which transitions between non-blocking and blocking phases, and EC-OCC which distinguishes edge and cloud transactions during timestamp validation. These hybrid techniques reduce unnecessary blocking and restarts. Simulation studies demonstrate that EC-Lock and EC-OCC provide substantial performance gains over traditional protocols under diverse workloads. By balancing consistency and efficiency, the proposed protocols enable scalable edge-cloud transaction processing. Our results show EC-Lock and EC-OCC better utilize scarce edge resources while minimizing cloud transaction impact. This work delivers innovative adaptive concurrency control optimized for emerging IoT-based edge-cloud computing architectures.
Battery lifetime estimation for energy efficient telecommunication networks in smart cities
(Elsevier, 2021-08) Chamola, Vinay
There has been a surge in telecommunication network deployments across the globe to facilitate advanced communication infrastructure which is necessary for smart cities. This has in turn increased the power consumption of telecommunication networks, thus motivating the need to adopt green energy solutions like solar energy to power them. Base stations (BSs) are the primary entities contributing to the power consumption in the telecommunication network. To efficiently deploy solar powered base stations, it is imperative to optimally provision them with appropriate Photo Voltaic (PV) panel and battery resources. The ultimate goal of such dimensioning is to provide best possible quality of service (QoS) to the consumers while maintaining an optimal cost of deployment and operation. Both PV panels and the batteries are major contributors while calculating the overall cost of deployment and operation for a solar powered BSs. Therefore an accurate calculation of battery lifetime with respect to different PV panel dimension and battery sizes is an important step in cost optimal resource provisioning for the solar powered BSs. This issue is addressed in this paper by presenting an analytical scheme to estimate the battery lifetime for a particular resource provisioning of PV panels and batteries. This is then used for evaluating the cost-optimal photo-voltaic panel dimensions and battery size for the base station with acceptable limit of outage probability. The proposed methodology would find great relevance in developing energy efficient sustainable telecommunication networks for upcoming smart cities.
A Bee Colony-Based Algorithm for Task Offloading in Vehicular Edge Computing
(IEEE, 2023-02) Chamola, Vinay
Complex vehicular applications, such as automatic driving and augmented reality are delay sensitive and require massive computational resources. Despite being more connected and smarter, vehicles still cannot appropriately meet the demands of these applications. By allowing neighboring vehicles and edge servers coupled to base stations to share their available computing resources, vehicular edge computing systems help to handle these applications. Then, vehicles can use the task offloading technique by sending application tasks to be executed remotely and receiving the processing results later. Although this technique aims to reduce application execution time, performing it in vehicular scenarios is challenging. In such scenarios, network nodes vary their computing and energy loads and move quickly, causing frequent disconnections and failures. Thus, we propose an algorithm called Bee colony-based Task offloading in Vehicular edge computing (BTV) to reliably reduce the execution time of applications in vehicular edge computing systems. The BTV algorithm provides task scheduling solutions to different servers in a feasible time, using several contextual parameters and wireless access in vehicular environments and fifth-generation networks. Experimental results show that our solution can reduce the average execution time of applications by up to 74.4% and with up to 0.0% of failures, outperforming other existing solutions.