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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Pagoti, Yashwant Krishna Nikumbh, Sarthak Karmakar, Prasenjit Chakraborty, Sandip Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

Air quality monitoring is gaining increasing importance as awareness about the health impacts of air pollution continues to grow. These monitors typically track air pollutants to give users a clearer picture of their environment. Accurate and real-time monitoring increases our understanding of the surrounding air we breathe and allows us to improve it. The proposed system helps users minimize their exposure to harmful pollutants by providing real-time data on the concentration of various gases. The system triggers alerts directly to the subscribed user's mobile phones in case of any hazardous situations so that the user can take appropriate actions immediately to resolve the issue. Our extensive data collection and evaluation with off-the-shelf machine learning models indicated an overall F1-score of 99.54 (±0.5)% with a simple random forest model in classifying burning, cooking, occupancy in a closed room, using aerosol products, based on the sensor's readings. © 2025 IEEE.

关键词： Indoor air pollution

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TCP-Cubiwood: Enhanced Cubic Congestion Control for mmWave and Future Networks 17

TCP-Cubiwood: Enhanced Cubic Congestion Control for mmWave a...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Prakash, Ujjayant Baswade, Anand M. Indian Institute of Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798331531195

The rise in mobile internet usage especially using cellular networks demands efficient performance for web traffic, primarily made up of short TCP flows. For TCP, Cubic is the most widely deployed congestion control algorithm. mmWave and THz offer very high bandwidth and low latency, enabling faster speeds and reduced delay for companies deploying innovative applications, positioning them as the future of wireless technology. As such, we are compelled to thoroughly evaluate the performance of Cubic in various mmWave specific NLoS/LoS scenarios. In the process, we find Cubic really struggling when it comes to static NLoS scenarios and long connections particularly because Cubic keeps on attempting recovery from slow-start but fails due to the NLoS condition. This is where TCP Cubiwood comes to the rescue with an improvement of 189%in a simulated static NLoS scenario with results comparable to Cubic in wired conditions as well, thus becoming a suitable contender for replacing the historical algorithm. Delayed Congestion Response (DCR) tries to make TCP more robust towards channel errors in wireless networks so we also propose an additional flavor of Cubiwood, called Cubiwood-DCR which increases performance slightly in both wired and wireless high frequency scenarios. We extensively evaluate Cubiwood against Cubic, both with and without DCR enabled and make our code available for reproducibility and further research in the same direction. Moreover, the simplicity of this method makes our proposal a much more attractive solution. We believe the proposed solution is easy to implement, can replace Cubic and has the potential to improve performance in THz communication as well which is one of the competing spectrum bands for 6G networks. © 2025 IEEE.

关键词： Congestion control (communication)

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An Approach for Soil Fertility Assessment Using Geospatial Data and Environmental Covariates 5

An Approach for Soil Fertility Assessment Using Geospatial D...

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5th International Conference on information Management and Machine Intelligence, ICIMMI 2023

作者： Mundada, Shyamal Jain, Pooa Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India

ISBN: (纸本)9798400709418

Agriculture is one of the most important industries for the country in terms of sustainability and the economy. Agriculture practises must be improved to preserve a sustainable balance because agriculture's importance and requirement are increasing along with the population. A vital part of the agricultural system is the soil. The accurate and thorough spatial soil information provided by soil composition detection can assist farmers in making decisions that will promote the growth of their crops. As a result, researchers concentrated on using AI and remote sensing to create models for the prediction of soil nutrient information that will aid farmers in exact fertilisation. A precise and economical solution for soil nutrient mapping is now available due to recent developments in sensor technology and machine learning-based soil analysis methods. Therefore, using machine learning (ML) techniques on remote sensing data can aid in developing a precision farming decision support system to increase crop output. For environmental modelling and risk assessment, complete and accurate geographic soil information is crucial. © 2023 ACM.

关键词： Soils

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Convergence of Geo-IoT with Advanced Technologies

Convergence of Geo-IoT with Advanced Technologies

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Sinha, Monalisha Shalini Thejaswini, M. Indian Institute of Information Technology Department of Computer Science and Engineering Bhagalpur India

ISBN: (纸本)9798350323887

IoT is an emerging digital technology where every physical object is connected to one another via the internet. IoT devices embedded with GPS sensors are called Geo-IoT systems where spatial data is the most prominent requirement in developing any IoT services and applications. Geo-IoT data can be used in various IoT applications for optimizing routes, tracking assets, real-time traffic notification, auto-driving, precision agriculture, anti-theft prevention, etc. This paper provides a review of the convergence of Geo-IoT with advanced technologies such as artificial intelligence, machine learning, and blockchain technology. This paper mainly discusses the current status and applicability of artificial intelligence and machine learning methods in solving and computing location-based IoT data for developing new advanced Geo-IoT applications, routing protocols, and security issues. © 2023 IEEE.

关键词： Blockchain

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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A literature study and performance gaps on Centralized server-based load balancing and routing strategies under cloud-IoT-WSN

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Australian Journal of Electrical and Electronics engineering 2024年第2期21卷 138-160页

作者： GSR, Yogaraja M N, Thippeswamy K, Venkatesh Information Science and Engineering S J C Institute of Technology Chickaballapura Karnataka India Ramaiah Institute of Technology Karnataka Bangalore India Computer Science & Engineering Nitte Meenakshi Institute of Technology Karnataka Bangalore India

Wireless Sensor Network (WSN) infrastructure has to be balanced for maintaining the Quality of Service (QoS) in the network. However, load balancing technology is complex for meeting the requirements of high flexibility and adaptability due to the conventional WSN architecture. The major complication for developing distributed techniques for these models is load balancing that is the major aim of this study. These distributed methodologies use routing decisions that must be efficient without the need for more information about the system, where one of the efficient techniques is probabilistic routing. This paper plans to conduct a critical literature review on a diverse centralised server for controlling the load balancing in the cloud and routing strategies in WSN as well as linked with an IoT systems. This study reviews a set of contributions regarding load balancing in the cloud and routing strategies in WSN with clear algorithmic analysis. The simulation tools and recorded performance measures are also analysed and sorted out. Finally, the existing research gaps and the future directions are expedited that to be adaptable to attain better protocol performance with a centralised server in balancing node traffic, load balancing in the cloud and improving the throughput of the entire network. ©, Engineers Australia.

关键词： Internet of things

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RISE: Modular Blockchain-Integrated Enhancements for Reducing Food Waste and Boosting Retail Accountability 17

RISE: Modular Blockchain-Integrated Enhancements for Reducin...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Rishika Banda, Gourinath Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

Effective management of semi-perishable food items is essential for minimising waste, maintaining food safety, and supporting sustainability efforts. This paper introduces the Responsibility Index for Sustainability and Efficiency (RISE), a set of modular components designed to seamlessly integrate with existing invoicing systems and enhance their performance by improving transparency, accountability, and waste management practices in retail stores. Semi-perishable food items, such as flour, bread, packaged meals, and beverages made from unprocessed grains, have a shorter shelf life than their raw counterparts, which can be stored for longer periods before processing. Ineffective management of these items often results in wastage and inflates the prices of raw materials due to imbalanced demand. Our solution holds retailers accountable for their role in demand creation and food waste by integrating a Responsibility Index Score, encouraging them to optimise their product offerings and reduce waste. These modules enhance consumption practices, supporting UN Sustainable Development Goals 2 (Zero Hunger) and 12 (Responsible Consumption and Production). They also help build consumer trust and reinforce retail stores' economic and social accountability. The modular approach ensures adaptability and ease of adoption across various invoicing platforms without requiring a complete system overhaul. © 2025 IEEE.

关键词： Sales

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Advancing Software-Defined Vehicles: An End-to-End Framework with Digital Twin Based Attestation for OTA Updates 17

Advancing Software-Defined Vehicles: An End-to-End Framework...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Agrawal, Krish Rohan, Jha Luthra, Nishkarsh Sekhar, Pilla Venkata Sharma, Hrishesh Banda, Gourinath Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331531195

Software-defined vehicles (SDVs) are an emerging paradigm in the automotive industry where vehicles' functionality, performance, and safety can be enhanced and updated through software, even after production. Unlike traditional vehicles, which rely primarily on physical components, SDVs leverage advanced connectivity, real-time data analytics, and cloud integration to adapt to changing regulations, driver preferences, and environmental conditions. This shift enables vehicles to evolve continuously, responding to new technological advances and customer needs. In this paper, we propose an end-to-end framework to demonstrate and realize the full benefits of SDVs. We incorporate the concept of digital twin (of the vehicle) driven software update authorization to download or update an application on the vehicle. In our framework, attestation refers to the verification of each software update's compatibility and functionality with the vehicle's current ECUs before deployment. Any impending/requested update is first verified for its compatibility with the vehicle's architecture (as per the twin) on the cloud. The idea is via the SDVs' digital twin - since each and every ECU is virtualized - the simulation and testing of the actual hardware setup with the new application software can be done without direct impact on the physical vehicle. Once the application is installed across all relevant virtualized ECUs, the framework confirms compatibility, ensuring smooth deployment and functionality in the real-world vehicle. Post this successful attestation, the installation will be done on the real vehicle corresponding to that digital twin. Through this proposed framework, we intend to ensure the safety of the updation, while the new updates contribute to the functionality and performance improvements. © 2025 IEEE.

关键词： Automotive industry

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A Knowledge Discovery Framework for Maritime Intelligence on Dark Vessels 17

A Knowledge Discovery Framework for Maritime Intelligence on...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Jayaswal, Bharat Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

Maritime Intelligence is essential for safeguarding maritime interests of coastal nations. Traditionally, vessel movements are tracked using self-reported positions via Automatic Identification System (AIS) transponders. However, vessels engaged in illicit activities often disable their AIS transponders to evade detection, thereby going dark. Satellite-based sensors, such as Synthetic Aperture Radar (SAR) and Visible Infrared Imaging Radiometer Suite (VIIRS) can be used to detect Dark Vessels. However, extraction of actionable insights for maritime intelligence on Dark Vessels is posed with several challenges. The spatial estimation of activities of vessels over various time periods is complicated due to spatial and temporal overlaps in satellite scans. This poster presents our ideas on knowledge discovery framework that integrates spatial analytics methods to gain actionable insights on Dark Vessels. Preliminary results are shown on Global Fishing Watch (GFW) Dataset of Dark Vessels. © 2025 IEEE.

关键词： Radar imaging

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mmWave Based User Identification using Gait Signatures 17

mmWave Based User Identification using Gait Signatures

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Das, Deepraj Mandal, Soumik Golande, Ashutosh Sen, Argha Chakraborty, Sandip Indian Institute of Technology Kharagpur Department of Computer Science and Engineering India

ISBN: (纸本)9798331531195

In this work, we present milliGait, a user identification using gait patterns captured by millimeter wave (mmWave) radar technology. milliGait takes into account the unique movement signatures of individuals to enable privacy secured accurate user identification. Using a Commercial-Off-the-self (COTS) mmWave radar we have collected mmWave features, particularly the range information and range-doppler profile features, extracted from the raw I/Q samples. To categorize people according to their leg movement patterns, we developed a Convolutional Neural Network (CNN) model using the mmWave features. The two features namely the range information and the range-doppler profile is fed into two concurrent CNN models (1D and 2D CNN models respectively). Finally a fully-connecyed classifier is employed to identify the subjects. We have evaluated milliGait with 3 different subjects with an authentication accuracy of over 80%. © 2025 IEEE.

关键词： Convolutional neural networks

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