diff --git a/content/authors/admin/_index.md b/content/authors/admin/_index.md index 2eb0b3f0..a3be7d26 100644 --- a/content/authors/admin/_index.md +++ b/content/authors/admin/_index.md @@ -89,7 +89,7 @@ highlight_name: true I am presently pursuing Ph.D. in the Department of Computer Science and Engineering at the Indian Institute of Technology Kharagpur, India. I am a member of [UbiNet: Ubiquitous Networked Systems Lab](https://cse.iitkgp.ac.in/resgrp/ubinet/). I have been awarded [Prime Minister's Fellowship](https://www.pmrf.in/) for my Doctoral Research. **Open source and open data** - +* [Dalton-Dataset](https://github.com/stilllearningsoumya/bus_trajectory_dataset) on Indoor Air pollution and Activites, India * [Bus Trajectory](https://github.com/stilllearningsoumya/bus_trajectory_dataset) Dataset for [54 feet-route](https://www.google.com/maps/dir/durgapur/54+Feet/@23.5347909,87.2863414,15z/data=!3m1!4b1!4m13!4m12!1m5!1m1!1s0x39f7710b47a89171:0x429e1bdb57e009dd!2m2!1d87.3119227!2d23.5204443!1m5!1m1!1s0x39f7720a874929a9:0x38b8e0691e176312!2m2!1d87.2837139!2d23.5482543?entry=ttu) in Durgapur, India * [Air Quality](https://github.com/prasenjit52282/AQuaMoHo/tree/master/Data) Dataset for two cities Durgapur and Delhi, India * [BuStop](https://github.com/prasenjit52282/BuStop) framework detects stay-location types for public bus diff --git a/content/home/experience.md b/content/home/experience.md index a23d969b..420c307a 100644 --- a/content/home/experience.md +++ b/content/home/experience.md @@ -48,7 +48,7 @@ experience: description: |2- Responsibilities include: - * Full Stack Development + * Frontend Development - title: Research Intern company: National Institute of Technology Durgapur diff --git a/content/news/StealthVPN/featured.png b/content/news/StealthVPN/featured.png new file mode 100644 index 00000000..aa0be236 Binary files /dev/null and b/content/news/StealthVPN/featured.png differ diff --git a/content/news/StealthVPN/index.md b/content/news/StealthVPN/index.md new file mode 100644 index 00000000..83e10881 --- /dev/null +++ b/content/news/StealthVPN/index.md @@ -0,0 +1,23 @@ +--- +date: 2024-10-22 +publishDate: 2024-10-22 +external_link: "" +image: + caption: Stealth-VPN + focal_point: Smart +slides: example +summary: A stunnel based virtual private network service for unrestricted internet access +tags: +- Opensource +title: Stealth-VPN for Unrestricted Network Access +links: + - icon_pack: fab + icon: github + name: Source + url: 'https://github.com/prasenjit52282/docker-stealth-openvpn' +--- +The repository implements a scalable non-blockable VPN service to enable unrestricted internet within an organisation that restricts network via firewalls. The clients are oblivious to the interanl working process and only route traffic through the local socks5 proxy deployed within the organisation. The proxy host forward all packet via a stunnel to the open internet (VPN server). Moreover, the local openssh server allows outgoing ssh connections from the restricted network. + +* VPN server must allows inbound connection to port: 443 +* Local Proxy must allows outbound connection to port: 443, and inbound connection to ports: 2000 (socks5), 2002 (sshd) +* End user must use appropiate proxy clients to connect to the local proxy server. Some tested clients are shown later \ No newline at end of file diff --git a/content/news/nptelpython/featured.png b/content/news/nptelpython/featured.png new file mode 100644 index 00000000..638f45e8 Binary files /dev/null and b/content/news/nptelpython/featured.png differ diff --git a/content/news/nptelpython/index.md b/content/news/nptelpython/index.md new file mode 100644 index 00000000..1c6d90cc --- /dev/null +++ b/content/news/nptelpython/index.md @@ -0,0 +1,24 @@ +--- +date: 2024-10-22 +publishDate: 2024-10-22 +external_link: "" +image: + caption: NPTEL Python Course + focal_point: Smart +slides: example +summary: Organizing live interaction sessions for NPTEL course - The Joy of Computing using Python +tags: +- Teaching +title: TA for The Joy of Computing using Python Course +links: + - icon_pack: fas + icon: newspaper + name: Recorded Sessions + url: 'https://www.youtube.com/watch?v=gUtRrjyB4mw&list=PL4OzPVnKOQIPsrWrQvsXB_ACI0Qpaq9kP' +--- +Offering Teaching Assistanceship in [The Joy of Computing using Python](https://onlinecourses.nptel.ac.in/noc24_cs113/preview), NPTEL Course in Fall 2024 semester. The course will provide: +* intermediate level knowledge of python programming language +* hands-on problem solving experience (case-studies) with various open source libraries such as numpy, pandas, matplotlib etc. + +Prerequisites: Basic concepts of Programming, beginner level C.
+Mode: Online Every Tuesday, 6:00 PM - 8:00 PM (26 hours in total) \ No newline at end of file diff --git a/content/publication/2021-cani/index.md b/content/publication/2021-cani/index.md index 2b8eaa16..9550f501 100644 --- a/content/publication/2021-cani/index.md +++ b/content/publication/2021-cani/index.md @@ -5,7 +5,7 @@ publishDate: 2021-11-17 authors: ["Praveen Kumar Sharma", "**Prasenjit Karmakar**", "Soumyajit Chatterjee", "Abhijit Roy", "Santanu Mandal", "Sandip Chakraborty", "Subrata Nandi", "Sujoy Saha"] publication_types: ["1"] abstract: "Smart cities are generally equipped with Air Quality Monitoring Stations (AQMS) as public infrastructure to have an overall perception of the air quality. However, the spatial density of the samples from the available public AQMS infrastructure is low, with a high cost of deployment and maintenance. Due to the spatial variation of the air quality and sparse deployment of AQMSs within a city, it is impossible to reliably obtain the air quality of a location far from a deployed AQMS. This paper provides a framework called AQuaMoHo that augments this existing system with a low-cost alternative that can even help the residents of a city to accurately monitor the air quality at any location in the town. AQuaMoHo relies on a low-cost thermo-hygrometer (THM) along with a GPS to populate various meteorological and demographic features, which are then used to predict the air quality reliably from any location. From a thorough study over two different cities, we observe that the proposed framework can significantly help annotate the air quality data at a personal scale." -featured: true +featured: false publication: "ACM BuildSys 2021" links: - icon_pack: fas diff --git a/content/publication/2022-reliant/index.md b/content/publication/2022-reliant/index.md index 2ac159dd..c8827c1c 100644 --- a/content/publication/2022-reliant/index.md +++ b/content/publication/2022-reliant/index.md @@ -5,7 +5,7 @@ publishDate: 2022-08-08 authors: ["**Prasenjit Karmakar**", "Vijay K Shah", "Satyaki Roy", "Krishnandu Hazra", "Sujoy Saha", "Subrata Nandi"] publication_types: ["2"] abstract: "Unmanned Aerial Vehicles (UAVs) can be utilized as aerial base stations to establish wireless communication networks in various challenging scenarios, such as emergency disaster areas and rural areas. Under large regions, the aerial communication networks would require UAVs to form wireless (backhaul) links among each other to provide end-to-end wireless services between two or more ground users (via one or more UAVs). Such UAV backhauling in aerial communication networks may be severely compromised if one or more UAVs are knocked off during the time of operation – it may be due to UAV hardware/software faults, limited battery, malicious attacks, etc. Deep reinforcement learning (DRL) has emerged as a powerful tool for learning tasks with large state and continuous action spaces. In this paper, we leverage emerging DRL to achieve reliable backhauling in an aerial communication network that remains functional and supports end-to-end wireless services even under various random and/or targeted UAV node failures. The proposed method (i) maximizes the reliability of UAV backhauling with joint consideration for communication coverage, (ii) learns the complex environment and its dynamics, and (iii) makes 3D positioning decisions for each UAV under the guidance of two deep neural networks. Our performance evaluation reveals that the proposed DRL approach outperforms the baseline method in terms of wireless coverage and network reliability against UAV failures." -featured: true +featured: false publication: "IEEE Transactions on Network and Service Management, Vol. 19, Issue. 3" links: - icon_pack: fas diff --git a/content/publication/2023-AQuaMoHo/index.md b/content/publication/2023-AQuaMoHo/index.md index c5e5d11d..47391c67 100644 --- a/content/publication/2023-AQuaMoHo/index.md +++ b/content/publication/2023-AQuaMoHo/index.md @@ -5,7 +5,7 @@ publishDate: 2023-03-01 authors: ["Prithviraj Pramanik", "**Prasenjit Karmakar**", "Praveen Kumar Sharma", "Soumyajit Chatterjee", "Abhijit Roy", "Santanu Mandal", "Subrata Nandi", "Sandip Chakraborty", "Mousumi Saha", "Sujoy Saha"] publication_types: ["2"] abstract: "Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this article propose a framework named AQuaMoHo that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core, AQuaMoHo exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that AQuaMoHo can significantly help annotate the air quality data on a personal scale." -featured: true +featured: false publication: "ACM Transactions on Sensor Networks, Vol. 19, No. 3" links: - icon_pack: fas diff --git a/content/publication/2024-jcss/index.md b/content/publication/2024-jcss/index.md new file mode 100644 index 00000000..f13c9e23 --- /dev/null +++ b/content/publication/2024-jcss/index.md @@ -0,0 +1,15 @@ +--- +title: "Exploring Indoor Air Quality Dynamics in Developing Nations: A Perspective from India" +date: 2024-09-16 +publishDate: 2024-09-16 +authors: ["**Prasenjit Karmakar**", "Swadhin Pradhan", "Sandip Chakraborty"] +publication_types: ["2"] +abstract: "Indoor air pollution is a major issue in developing countries such as India and Bangladesh, exacerbated by factors such as traditional cooking methods, insufficient ventilation, and cramped living conditions, all of which elevate the risk of health issues such as lung infections and cardiovascular diseases. With the World Health Organization associating around 3.2 million annual deaths globally to household air pollution, the gravity of the problem is clear. Yet, extensive empirical studies exploring these unique patterns and indoor pollution’s extent are missing. To fill this gap, we carried out a 6-months long field study involving over 30 households, uncovering the complexity of indoor air pollution in developing countries, such as the longer lingering time of volatile organic compounds (VOCs) in the air or the significant influence of air circulation on the spatiotemporal distribution of pollutants. We introduced an innovative Internet of Things (IoT) air quality sensing platform, the Distributed Air QuaLiTy MONitor (DALTON), explicitly designed to meet the needs of these nations, considering factors such as cost, sensor type, accuracy, network connectivity, power, and usability. As a result of a multi-device deployment, the platform identifies pollution hot spots in low- and middle-income households in developing nations. It identifies best practices to minimize daily indoor pollution exposure. Our extensive qualitative survey estimates an overall system usability score of 2.04, indicating an efficient system for air quality monitoring." +featured: true +publication: "ACM Journal on Computing and Sustainable Societies, Vol. 2, No. 3" +links: + - icon_pack: fas + icon: scroll + name: Link + url: 'https://dl.acm.org/doi/full/10.1145/3685694' +--- \ No newline at end of file diff --git a/content/publication/2024-mobilehci/index.md b/content/publication/2024-mobilehci/index.md new file mode 100644 index 00000000..e4bf5db3 --- /dev/null +++ b/content/publication/2024-mobilehci/index.md @@ -0,0 +1,15 @@ +--- +title: "Exploiting Air Quality Monitors to Perform Indoor Surveillance: Academic Setting" +date: 2024-09-21 +publishDate: 2024-09-21 +authors: ["**Prasenjit Karmakar**", "Swadhin Pradhan", "Sandip Chakraborty"] +publication_types: ["1"] +abstract: "Changing public perceptions and government regulations have led to the widespread use of low-cost air quality monitors in modern indoor spaces. Typically, these monitors detect air pollutants to augment the end user's understanding of her indoor environment. Studies have shown that having access to one's air quality context reinforces the user's urge to take necessary actions to improve the air over time. Thus, user's activities significantly influence the indoor air quality. Such correlation can be exploited to get hold of sensitive indoor activities from the side-channel air quality fluctuations. This study explores the odds of identifying eight indoor activities (i.e., enter, exit, fan on, fan off, AC on, AC off, gathering, eating) in a research lab with an in-house low-cost air quality monitoring platform named DALTON. Our extensive data collection and analysis over three months shows 97.7% classification accuracy in our dataset." +featured: true +publication: "ACM MobileHCI 2024" +links: + - icon_pack: fas + icon: scroll + name: Link + url: 'https://dl.acm.org/doi/10.1145/3640471.3680243' +--- \ No newline at end of file diff --git a/content/publication/2024-neurips/index.md b/content/publication/2024-neurips/index.md new file mode 100644 index 00000000..3153f2ce --- /dev/null +++ b/content/publication/2024-neurips/index.md @@ -0,0 +1,19 @@ +--- +title: "Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities" +date: 2024-10-21 +publishDate: 2024-10-21 +authors: ["**Prasenjit Karmakar**", "Swadhin Pradhan", "Sandip Chakraborty"] +publication_types: ["1"] +abstract: "In recent years, indoor air pollution has posed a significant threat to our society, claiming over 3.2 million lives annually. Developing nations, such as India, are most affected since lack of knowledge, inadequate regulation, and outdoor air pollution lead to severe daily exposure to pollutants. However, only a limited number of studies have attempted to understand how indoor air pollution affects developing countries like India. To address this gap, we present spatiotemporal measurements of air quality from 30 indoor sites over six months during summer and winter seasons. The sites are geographically located across four regions of type: rural, suburban, and urban, covering the typical low to middle-income population in India. The dataset contains various types of indoor environments (e.g., studio apartments, classrooms, research laboratories, food canteens, and residential households), and can provide the basis for data-driven learning model research aimed at coping with unique pollution patterns in developing countries. This unique dataset demands advanced data cleaning and imputation techniques for handling missing data due to power failure or network outages during data collection. Furthermore, through a simple speech-to-text application, we provide real-time indoor activity labels annotated by occupants. Therefore, environmentalists and ML enthusiasts can utilize this dataset to understand the complex patterns of the pollutants under different indoor activities, identify recurring sources of pollution, forecast exposure, improve floor plans and room structures of modern indoor designs, develop pollution-aware recommender systems, etc." +featured: true +publication: "NeurIPS 2024" +links: + - icon_pack: fas + icon: scroll + name: Link + url: 'https://arxiv.org/abs/2407.14501' + - icon_pack: ai + icon: open-data + name: Open data + url: 'https://github.com/prasenjit52282/dalton-dataset' +--- \ No newline at end of file