Joy Dutta
@kiit.ac.in
Assistant Professor, School of Computer Engineering, KIIT Deemed to be University
KIIT University
Dr. Joy Dutta is presently working as an Assistant Professor in the School of Computer Engineering, Bhubaneswar, Orissa, India. He holds a BSc degree in Physics (Honours), followed by Post BSc BTech and MTech in Computer Science & Engineering from Calcutta University. He is a recipient of Government of India’s prestigious full-time Research Fellowship, viz., “Visvesvaraya PhD Fellowship” of Ministry of Electronics & Information Technology (MeitY) for pursuing his full-time research from the Department of Computer Science and Engineering, Jadavpur University and has received his PhD (Engg.) Degree in February 2022.
Dr. Dutta has the exposure of working in both the industry as well as in academia. He is a member of IEEE and an active researcher in the field of IoT and related applications for social good. He has rich experience in the domain of Cloud Computing, Machine Learning, Artificial Intelligence, Data Analytics and Smart City based applications.
EDUCATION
Dr. JOY DUTTA did his B.Sc in Physics, followed by Post B.Sc B.Tech and M.Tech in Computer Science & Engineering from Calcutta University. He is is the recipient of Government of India’s prestigious full-time Research Fellowship, namely “Visvesvaraya PhD Fellowship” of Ministry of Electronics & IT (MeitY) for pursuing his full-time research from the Department of Computer Science and Engineering, Jadavpur University and has received his PhD (Engg.) Degree in February 2022.
RESEARCH INTERESTS
IoT, Machine Learning, Data Analytics, Cloud Computing, Smart City
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Darwish Al Neyadi, Deepak Puthal, Joy Dutta, and Ernesto Damiani
Springer Nature Switzerland
Joy Dutta, Debojit Chakraborty, Sarbani Roy, and Deepak Puthal
IEEE
The issue of noise pollution in urban environments is a prevalent concern that necessitates careful resolution. The detrimental impacts of noise pollution on human health are manifold, yet this issue remains largely overlooked, with a dearth of comprehensive mechanisms in place for its widespread measurement. Honking in urban areas constitutes a significant contributor to the overall presence of ambient noise pollution, thereby warranting careful consideration and attention. The initial step that necessitates careful monitoring is the accurate identification of honking from a vehicle. To address this issue, a Judicious Urban Honk Detection Module (JUHDM) has been developed. To achieve the implementation of the JUHDM, two separate units working on intrusive and non-intrusive principles have been developed here. This paper provides a comprehensive analysis of both the operational mechanism and the efficacy of both devices in real-world scenarios. Further, it integrates this honking information with other driving-related information to provide drivers with a novel driving score mechanism based on their driving style.
Joy Dutta, Deepak Puthal, and Chan Yeob Yeun
IEEE
This article provides in-depth experimental studies of XAI (EXplainable Artificial Intelligence) in the IoT-Edge-Cloud continuum. Within the different available XAI frameworks, such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) frameworks are utilized here as they are the most suitable feature map-based, model-agnostic, posthoc frameworks that match our requirements for getting real-time prediction explanations in the healthcare domain. In order to evaluate LIME and SHAP in this continuum and to make black box AI (BBAI)-based decisions interpretable, we have considered the real-world electronic health record (EHR)-based large cloud database (which could be a very large database–VLDB) and IoMT based real-time streams as edge databases for the prediction of cardiac arrest in the real-world. We have also verified the effectiveness of automated counterfactual explanations in this context for taking remedial actions. Thus, our proposed model is capable of making significant advancements in the healthcare industry by offering conscious healthcare monitoring automation along with an AI-based self-explanatory system that serves as a personalized health assistant for individuals, paving the way for the next major upgrade in healthcare.
Saeed Alqubaisi, Deepak Puthal, Joy Dutta, and Ernesto Damiani
IEEE
To facilitate the Edge AI paradigm in distributed networks, we propose novel collaborative learning methodologies for a connected network of edge nodes. Our proposed methodologies tackle the challenges in distributed learning where there are constraints on data privacy and a low degree of overlap between the classes observed by the nodes. These approaches entail sharing class distribution information between nodes, computing nodes, and class weights, training local models on each node, then aggregating the models using the determined weights. It favors nodes that have encountered unique or less common classes in their local datasets. Through a series of experiments using an activity recognition dataset, we demonstrate the effectiveness and scalability of our proposed approaches. We show the adaptive nature of the proposed approach by achieving classification accuracy above the baseline, even with little overlap between the observed classes. This study serves as a foundation for future advancements in collaborative learning on edge networks, and encourages the development of scalable solutions.
Joy Dutta and Deepak Puthal
IEEE
Explainable Artificial Intelligence (XAI) is a new paradigm of Artificial Intelligence (AI) that is giving different AI/ Machine Learning (ML) models a boost to penetrate sectors where people are thinking about adopting AI. This work focuses on the adoption of XAI in the health sector. It portrays that careful integration of XAI in both cloud and edge could change the whole healthcare industry and make humans more aware of their present health conditions, which is the need of the hour. To demonstrate the same, we have done an experiment based on the prediction of a particular medical condition called "cardiac arrest" in a specific subject group (patients who are 70 years old). Here, based on the explanation provided by the XAI model (e.g., SHAP, LIME) at Cloud and Edge, our system can predict the chances of a "cardiac arrest" for the subject with a valid explanation. This type of model will be the next big upgrade in the healthcare industry in terms of automation and a self-explanatory system that works as a personal health assistant for individuals.
Joy Dutta and Deepak Puthal
IEEE
In the present era, data plays a crucial role across various disciplines, serving as the foundation for exploration and advancements. However, in the domain of eHealth, a readily available dataset for training AI models to predict cardiac arrest using the internet of medical things (IoMT) is lacking. To bridge this gap, this research article addresses the need for a synthesized dataset that can be utilized by researchers in the eHealth field to evaluate the effectiveness of their AI/ML models. The article presents a synthesized IoMT dataset specifically designed for cardiac arrest prediction, incorporating valid ranges of IoMT-based medical features sourced from peer-reviewed journals and articles. This study offers the capability to generate synthetic datasets of varying sizes, catering to the specific requirements of researchers focused on cardiac arrest prediction for individual subjects (patients). The availability of such a dataset will contribute to the advancement of AI-driven research in the eHealth domain.
Sultan Almansoori, Mohamed Alzaabi, Mohammed Alrayssi, Deepak Puthal, Joy Dutta, and Aamna Al Shehhi
IEEE
The focus of our paper is to explore the concept of blockchain, which is a digitalized, shared, and decentralized network where every transaction can be viewed by all users with access to the blockchain. Our main objective is to develop an access control mechanism for our private blockchain, which is implemented using Ethereum. This mechanism will use a machine learning-based security layer to regulate user access, allowing or denying access based on predetermined rules. Our ultimate goal is to create a mechanism that is highly secure, maintains data confidentiality, and improves user authenticity. To achieve the objectives, we construct a client-side model with an appealing graphical user interface that enables users to take advantage of the unique functionalities offered by the private blockchain. This paper will help to determine the most effective strategies for building a secure and reliable access control mechanism for blockchain networks.
Joy Dutta and Sarbani Roy
Elsevier BV
Joy Dutta, Deepak Puthal, and Ernesto Damiani
IEEE
Artificial Intelligence (AI) is gaining popularity in the Internet of Things (IoT) based application-based solution development. Whereas, Blockchain is become unavoidable in IoT for maintaining the end-to-end process in the decentralized approach. Combining these two current-age technologies, this paper details a brief comparative study with the implementations and further analyzes the adaptability of the AI-based solution in the Blockchain-integrated IoT architecture. This work focuses on identifying the of block data in the block validation stage using AI-based approaches. Several supervised, unsupervised, and semi-supervised learning algorithms are analyzed to determine a block's data sensitivity. It is identified that machine learning techniques can identify a block's data with very high accuracy. By utilizing this, the block's sensitivity can be identified, which can help the system to reduce the energy consumption of the block validation stage by dynamically choosing an appropriate consensus mechanism.
Joy Dutta and Sarbani Roy
Springer Science and Business Media LLC
Asif Iqbal Middya, Sarbani Roy, Joy Dutta, and Rituparna Das
Springer Science and Business Media LLC
Participatory sensing has become an effective way of sensing urban dynamics due to the widespread availability of smartphones among citizens. Traditionally, separate urban sensing applications are designed to monitor different urban dynamics like environment, transportation, mobility, etc. However, combining these applications to aggregate information can lead to various new inferences. The main objective of this work is to improve urban sensing applications by overcoming their individual limitations. A unified framework called JUSense (Judicious Urban Sensing) is proposed that can derive benefits from these applications by combining their functionalities. JUSense provides the opportunity for applications to tackle the challenges associated with data collection, aggregation of data in cloud, calibration, data cleaning, and prediction. A multi-view fusion model is proposed for spatiotemporal urban air and noise pollution map generation. Further, a random forest classifier is built to classify the driving events. Here, large scale experiments are performed to evaluate the efficacy of JUSense on real-world dataset. Both the fusion model and the random forest classifier yield better accuracies compared to the baseline methods. Additionally, case studies are conducted to show the advantages that can arise out of the mutual interactions among the applications.
Joy Dutta, Sarbani Roy, and Chandreyee Chowdhury
Springer Science and Business Media LLC
By embracing the potential of IoT and smartphones, traditional cities can be transformed to smart cities. The success of such smart city mission is firmly vested in populace and thus it should have a bottom-up nature, initiated by the citizens. This paper focuses on the design and development of a unified framework, which can provide a platform to empower all the applications across different dimensions of urban life in a smart city. The aim of this framework is to connect citizens, data, knowledge and services related to IoT as well as smartphone based applications. Here, we categorize all the applications for the smart city in three representative types, viz. IoT based, IoT and smartphone based and smartphone as IoT based applications. We have also developed and tested one prototype following this architecture for each of these three representative category type, i.e, IoT based smart classroom, IoT and smartphone based air quality monitoring system and only smartphone based noise monitoring system to demonstrate the effectiveness of the proposed framework for the smart city scenario.
Beepa Bose, Joy Dutta, Subhasish Ghosh, Pradip Pramanick, and Sarbani Roy
IEEE
Driving style analysis and road anomaly detection have a remarkable impact on road safety. They directly influence road accidents and have been a vital area of research in order to address road safety problems. In this paper, a system called D&RSense have been proposed that uses GPS and accelerometer of smartphones to categorize driving style of drivers, assess the road quality as well as to give real-time warnings to drivers in order to make driving safer. D&RSense does the categorization through detection of driving events like acceleration and braking and road anomalies like bumps and potholes by using the popular machine learning technique, Support Vector Machine (SVM) and gives real-time warning and instructions to drivers using a locally running Fast Dynamic Time Warping (FastDTW) algorithm. Extensive experiments have been conducted to evaluate the effectiveness of the proposed system.
Joy Dutta, Yong Wang, Tanmoy Maitra, SK Hafizul Islam, Bharat S. Rawal, and Debasis Giri
IEEE
In the modern world, the Internet-of-Things (IoT) has become a buzzword. The sole impetus behind the advent of IoT is the fact that the people require more advanced and automated systems to improve the quality of their lives on a regular basis. The automated technology, which was only confined to the walls of a factory or a production unit has dismantled those walls and reached to the everyday household of the common man of today, but a deaf ear is turned to the security and safety of the mankind in a majority of cases which results in the increasing crime rates despite having state-of-the-art technology. Our system, called 'enhanced security system for smart building using IoT (ES3B)' is an effort to improve the quality of life of a common man of today as well as to improve his safety and security by resorting to the most common and available resources in the existing century. The proposed idea is implemented in open source platform Android, which is the most eminent IoT platform of the modern days.
Joy Dutta, Pradip Pramanick, and Sarbani Roy
IEEE
Noise pollution in urban areas is a subject of grave concern and it is being recognized globally in different countries and cities. People are facing many health-related problems because of this. Therefore, in the proposed work, we envisioned to tackle the challenge of acquiring real time and spatially fine-grained noise pollution data with a community-driven sensing infrastructure. Mobile crowdsourcing over smartphones presents a new paradigm for collecting context aware sensing data of a vast area like a city. Thus, the proposed system exploits the power of mobile crowdsourcing. The proposed system monitors the present noise level in the surroundings of the user and also generates city's noise pollution footprints. The noise map reflects the real-time pollution scenario of the city which changes with time. The prototype of the system has been evaluated with extensive experiments based on crowdsourced sensing data collected by volunteers in Kolkata city.
Beepa Bose, Joy Dutta, Subhasish Ghosh, Pradip Pramanick, and Sarbani Roy
ACM
Integration of the physical world with the computerized world has led to the manifestation of Cyber-Physical Systems (CPSs) in an attempt to build a better and smarter world. In this paper, such a CPS named D&RSense has been proposed to promote smart transportation in order to make travelling more comfortable and safe. By studying driving patterns of drivers, D&RSense can get valuable insights to their braking and accelerating styles which can help to give them real-time warnings when they drive aggressively. Detection of rash driving prone areas across the city can help to recommend which areas of the city need stricter surveillance. D&RSense involves smartphones of commuters and utilizes their accelerometer and GPS sensors to detect driving events like braking and acceleration as well as poor road conditions like bumps and potholes by applying the ensemble learning method for classification, Random Forest (RF). The accuracy of the same has been compared to other supervised machine learning classifiers like Naive Bayes, k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). Rash-driving prone areas and poor road segments during the course of the experiment have been plotted using Density-based spatial clustering of applications with noise (DBSCAN) algorithm. Effectiveness of the proposed application has been evaluated through extensive testing.
Subhasish Ghosh, Joy Dutta, and Sarbani Roy
ACM
In recent years, advancement in mobile phone technology along with their exhaustive use in daily life leads towards a new paradigm of sensing called participatory sensing. Using the power of participatory sensing, we can develop people and environment centric sensing societal applications that helps citizen and policy maker in better decision making. In this paper, we present a participatory sensing based application called SenseDCity that discover travel pattern of the individual user, crowd flow of the city from the citizen's GPS trajectories. The system notifies users about the congestion, and current popular places near them. It takes the power of "people as a sensor" for identifying the road condition of the city, common health related symptoms among the citizen which in turn coming as a feedback and is a very valuable knowledge for both the citizen and the city administrator. The system is tested in the real-world scenarios and outcomes of the system are showing a glimpse of the huge potential of the proposed approach.
Joy Dutta, Pradip Pramanick, and Sarbani Roy
Springer Singapore
GPS is one of the most used services in any location-based app in our smartphone, and almost a quarter of all Android apps available in the Google Play store are using this GPS. There are many apps which require monitoring your locations in a continuous fashion because of the application’s nature, and those kinds of apps consume the highest power from the smartphones. Because of the high-power draining nature of this GPS, we hesitate to take part in different crowd-sourced applications which are very much important for the smart city realization as maximum of these applications use GPS in real time or in a very frequent manner for the realization of participatory sensing in a smart city scenario. To resolve this, we have introduced an energy-efficient context-aware approach which utilizes user’s mobility information from the user’s context and as well smartphone’s sensing values from the inbuilt accelerometer, magnetometer, and gyroscope of the smartphone to provide us a very close estimation of the present location of the user without using continuous GPS. It is an energy-efficient solution without sacrificing the accuracy compared to energy saving which will boost the crowd to take part in the smartphone-based crowd-sourced applications that depend on participatory sensing for the smart city environment.
Joy Dutta and Sarbani Roy
IEEE
Here, we present a prototype of a smart building using newly surfacing technologies like IoT (Internet of Things), fog and cloud for the smart city. The demand for everything smart is increasing daily, but the main stumbling block is its high price. So, our aim is to improve the standard of living in home and in office with newly improved working facilities where the whole system will be automatic, efficient and will be under the control of the user via his/her smartphone or computer but the cost will stay within the budget of a common man. All these are done by the incorporation of IoT, fog and cloud. The assimilation is done using open source hardwares and softwares to reduce the cost dramatically than the other existing solutions and implement it in an impressive and ingenious way without compromising QoS (Quality of Service) of any of the functionalities provided by other existing solutions.
Joy Dutta, Chandreyee Chowdhury, Sarbani Roy, Asif Iqbal Middya, and Firoj Gazi
ACM
Cities are expanding and more and more citizens are exposed to air pollutants both indoors and outdoors. This may have adverse effects on citizens' health. In this paper, we present AirSense, an opportunistic crowd-sensing based air quality monitoring system, aimed at collecting and aggregating sensor data to monitor air pollution in the vicinity (building/neighbourhood) and the city. We introduce a light weight, low power and low cost air quality monitoring device (AQMD) and demonstrate how AQMD and smartphones in a crowd collaboratively gather and share data of interest to the cloud. In cloud, collected data are analyzed and an aggregate view is generated from data collected from various sensors and from different users for providing an air pollution heat map of the city. Unlike previous works, both micro and macro level air quality monitoring is possible with Airsense. End user can view his/her pollution footprint for the whole day, the neighborhood (local) air quality and AQImap (air quality index map) of the city on his/her smartphone. The system is implemented and the prototype is also evaluated.
Joy Dutta, Firoj Gazi, Sarbani Roy, and Chandreyee Chowdhury
IEEE
Citizens are exposed to air pollutants both indoors and outdoors due to their activities, which may result in a variety of health effects. In this paper, we present AirSense, an opportunistic crowd-sensing based air quality monitoring system, aimed at collecting and aggregating sensor data to monitor air pollution in the vicinity and the city. We introduce a light weight, low power and low cost air quality monitoring device (AQMD) and demonstrate how AQMD and smart phone devices in a crowd collaboratively (through offloading) gather and share data of interest to the cloud. In cloud, collected data will be analyzed and an aggregated view will be generated for providing an air pollution heat map of the city. End user can view both the neighborhood (local) air quality and AQImap (air quality index map) of the city on his/her smartphone.