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Optimizing Air Pollution Forecasting Models Through Knowledge Distillation: A Novel GCN and TRANS_GRU Methodology for Indian Cities
Sudhir Kumar;Vaneet Kour;Ankit Raj;Tagru Tapung;Shivendu Mishra;Rajiv Misra;T. N. Singh
As air pollution remains a pressing issue affecting environmental and public health, precise forecasting and monitoring are essential. Machine learning and deep learning models are increasingly applied in air pollution studies, using their advanced capabilities to decode intricate pollutant data. A review of the existing literature on air quality forecasting reveals significant gaps: first, a lack...Show More
Big Data-Driven Long Term Accurate CO2 Emission Forecasting Using Advanced Recurrent Neural Networks
Anil Verma;Lalit Chandra Routhu;Rishikant Chigrupaatii;Anurag Choubey;Rajiv Misra;T.N. Singh
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Global climate change driven by carbon dioxide (CO2) emissions necessitates accurate forecasting for effective mitigation strategies. Traditional forecasting models, such as ARIMA and statistical methods, often struggle to handle the non-linear and long-term temporal dependencies in CO2 emission data. This paper evaluates the performance of advanced Recurrent Neural Network (RNN) architectures, sp...Show More
Dueling Double DQN with Attention for Optimized Offloading in Wireless-Powered Edge-Enabled Mobile Computing Networks
Shivani Tripathi;Praveen Kumar;Mailram Sai Chaitanya;Nagireddy Sai Tarun Teja;Rajiv Misra;T.N. Singh
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
This research work introduces a new strategy to enhance computation offloading in wireless-powered Edge-Enabled Mobile Computing (EEMC) networks by utilizing Dueling Double Deep Q-Networks with Attention (Dueling DDQN-A). EEMC facilitates the offloading of computational tasks from mobile devices to local edge servers, leading to decreased latency and improved energy efficiency. However, the dynami...Show More
Placement of Swarm UAV for Data Collection: A Deep Reinforcement Learning Approach
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Rapid technical advancements in recent times have made it possible to produce a class of affordable unmanned aerial vehicles (UAVs), which can serve a wide range of public and private users for different industrial or non-industrial requirements. It is very complex to set up a communication network among the UAVs, which is very essential for completing any task like collecting data in extreme weat...Show More
Statistical Modeling of Air Pollutants for Predicting AQI Levels
Sudhir Kumar;Sourabh Mehra;Harsh Jha;Vaneet Kour;Rajiv Misra
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Air quality is a critical factor influencing public health, with pollutants such as particulate matter (PM10, PM2.5), ground-level ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO2) posing significant health risks. Understanding the relationship between these pollutants is essential for effective air quality management. In this study, we apply a comprehensive range ...Show More
A Novel Hybrid CNN-Transformer Framework for Spatiotemporal Prediction of O3
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Air pollution presents serious threats to public health and environmental sustainability, especially when it comes to concentrations of ozone (O3). Even though they are accurate, traditional monitoring methods have spatial limitations that cause gaps in the coverage of data. In order to tackle this issue, we provide a hybrid deep learning model that predicts O3 levels in different parts of India b...Show More
MEC- Assisted Task offloading using Meta-Reinforcement Learning for B5G/6G Network
Priyadarshni Priyadarshni;Praveen Kumar;Shivani Tripathi;Akshun Pratap Dubey;Rajiv Misra
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
The rapid growth of mobile data and computing demands has strained resource-constrained edge devices, particularly in supporting IoT applications. Mobile Edge Computing (MEC) offloading alleviates these challenges by shifting complex tasks to edge-cloud servers, reducing computational burdens and enhancing efficiency. The integration of 5G and 6G technologies further enhances MEC by providing ultr...Show More
Performance Comparison of Deep Learning Models for CO2 Prediction: Analyzing Carbon Footprint with Advanced Trackers
Anil Verma;Sumit Kumar Singh;Rupesh Kumar Sah;Rajiv Misra;T. N. Singh
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Climate change and global warming demand immediate global action to protect future generations. Although deep learning is crucial for solving complex tasks, its high energy consumption and carbon emissions from extensive training processes necessitate improvements in efficiency to reduce its environmental impact. This research addresses the need for accurate carbon emission tracking to mitigate cl...Show More
Advancing PM2.5 and NO2 Prediction Accuracy through the ConvTrans Model: A Novel Application of Hybrid Deep Learning Technologies
2024 IEEE International Conference on Big Data (BigData)
Year: 2024 | Conference Paper |
Accurate forecasting of critical pollutants such as NO2 and PM2.5 is essential in the ongoing battle against air pollution. This paper introduces ConvTrans, a novel hybrid architecture that integrates convolutional and Transformer technologies to enhance the precision of PM2.5 and NO2 predictions. Our evaluations demonstrate that ConvTrans improves PM2.5 prediction accuracy, increasing the R-squar...Show More
Scalable and Accurate Anomaly Detection in 5G Networks with ConvLSTM
2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)
Year: 2024 | Conference Paper |
With the ongoing rise in network security threats stemming from the Internet of Things (IoT), the attack surface broadens and network heterogeneity grows, rendering future network protection more complex and challenging. This paper suggests transforming 5G slice traffic into formats compatible with ConvLSTM model analysis to identify anomalous network traffic. By leveraging Software Defined Securi...Show More
Enhanced Forecasting Accuracy: Leveraging GRU+BiLSTM with Attention for Precise Floodwater Level Prediction
Sudhir Kumar;Vaneet Kour;Anil Verma;Rupesh Kumar Sah;Rajiv Misra
2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN)
Year: 2024 | Conference Paper |
Floods, recognized as one of the most destructive natural disasters, inflict significant loss of life and extensive damage globally. In response to the escalating impact of floods, researchers have made significant advancements in floodwater level prediction accuracy using deep learning models. However, the existing studies in this domain do not incorporate prior forecasts with higher accuracy, pa...Show More
GAN-Based Multi-Task Learning Approach for Prognostics and Health Management of IIoT
Health assessment (HA) & remaining useful life (RUL) estimation, the two essential pillars of the prognostics and health management (PHM) paradigm, help improve industrial equipment reliability while reducing maintenance costs. However, reported works treat HA and RUL estimation as disjoint problems though there exist unexploited similarities among these related issues. Additionally, in practical ...Show More
Deep Meta Q-Learning Based Multi-Task Offloading in Edge-Cloud Systems
Resource-constrained edge devices can not efficiently handle the explosive growth of mobile data and the increasing computational demand of modern-day user applications. Task offloading allows the migration of complex tasks from user devices to the remote edge-cloud servers thereby reducing their computational burden and energy consumption while also improving the efficiency of task processing. Ho...Show More
Analysis and Forecasting of Carbon Emission in SAARC Countries using Attention-based LSTM
Anil Verma;Harshit Dhankhar;Rajiv Misra;T.N. Singh;Om Prakash Dhakal
2023 IEEE International Conference on Big Data (BigData)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Climate change and global warming are urgent environmental issues demanding immediate action to safeguard future generations. The major contributor to the greenhouse effect, carbon dioxide $\left(\mathrm{CO}_{2}\right)$, primarily originates from industrial and transportation fossil fuel combustion. International agreements, like the Paris Agreement, call for a 30-35% reduction in CO2 emissions co...Show More
Proximal Policy Optimization based computations offloading for delay optimization in UAV-assisted mobile edge computing
2023 IEEE International Conference on Big Data (BigData)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
UAVs have the potential to enhance wireless systems by improving range and quality, and this can be achieved by utilising the Mobile Edge Computing provided by the unmanned aerial vehicle. In this system, the MEC server mounted on the unmanned aerial vehicle can offer offload services to all the User Equipment in a given space. By offloading some proportion of its tasks to unmanned aerial vehicle ...Show More
Workload Shifting Based on Low Carbon Intensity Periods: A Framework for Reducing Carbon Emissions in Cloud Computing
Shivani Tripathi;Praveen Kumar;Priyadarshni Gupta;Rajiv Misra;T.N. Singh
2023 IEEE International Conference on Big Data (BigData)
Year: 2023 | Conference Paper |
Datacenter carbon emissions are rising, which poses a serious issue that must be addressed quickly. We may see differences in emissions when we look at the carbon intensity of the electrical system since various areas have different energy sources. We can time the execution of workloads to occur during periods when the carbon intensity is lower by using this temporal variability. This paper aims t...Show More
Energy-Efficient k-Hop Clustering in Cognitive Radio Sensor Network for Internet of Things
IEEE Internet of Things Journal
Cited by: Papers (30)
The design and development of energy and spectrum-efficient solutions are important in the success of the Internet of Things (IoT). Due to the presence of an enormous number of smart devices, such as sensors, actuators, and different household devices achieving such scalable and efficient solutions are challenging. A wireless sensor network (WSN) with dynamic spectrum access (DSA) capability, know...Show More
Maximizing Fairness for Resource Allocation in Heterogeneous 5G Networks
In this article, we first formulate the joint resource allocation, interference minimization, user-level, and cell-level fairness for maximum resource reuse in 5G heterogeneous small cell networks as an NP-hard problem. We then propose three algorithms - centralized, distributed, and randomized distributed algorithms - to efficiently solve the formulated resource allocation problem while minimizin...Show More
Deep Learning Based Resource Allocation For Auto-Scaling VNFs
2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
With the advancement in Software-Defined Networking (SDN), the virtualization of network functions (VNFs) (such as virtual firewalls, virtual routers, etc.) in the cloud often becomes complex to scale VNFs and to deal with reroute traffic demands to cope with the increasing dynamicity of network services. Therefore, the auto-scaling of VNFs has been receiving significant research attention. In con...Show More
Machine Learning-based Slice allocation Algorithms in 5G Networks
2019 International Conference on Advances in Computing, Communication and Control (ICAC3)
Year: 2019 | Conference Paper |
Cited by: Papers (18)
One of the primary aims for 5G networks is the flexibility to support vertical services as the primary requirement. The use cases of 5G networks are AR/VR, eHealth, Video Streaming, automation works, automated vehicles, etc. All use cases are associated with its requirements like availability, reliability, latency, and throughput in terms of Quality of Services (QoS) and Quality of Experience (QoE...Show More
Truthful Double Auction Based VM Allocation for Revenue-Energy Trade-Off in Cloud Data Centers
2019 National Conference on Communications (NCC)
Year: 2019 | Conference Paper |
Cited by: Papers (5)
With the advances in virtualization technologies, cloud has emerged as a flexible and cost-effective service paradigm by provisioning on-demand VM resources to users via a pay-per-use business model. In cloud data centers, effective resource provisioning is required with the aim of minimizing energy consumption and maximizing cloud provider's revenue. However, the existing mechanisms have either f...Show More
Factor Graph-based Message Passing Technique for Distributed Resource Allocation in 5G Networks
2019 11th International Conference on Communication Systems & Networks (COMSNETS)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
Next generation 5G cellular networks are intended to provide higher data rates, excellent user coverage, low latency, and low power consumption. Since 5G is expected to have a multitier architecture consisting of macro cells, small cells and Device-to-Device communication altogether but the reported works so far have considered either one of these but not all in resource allocation (RA) in heterog...Show More
EnergyTradingRank Algorithm for Truthful Auctions among EVs via Blockchain Analytics of Large Scale Transaction Graphs
Anurag Choubey;Sourajit Behera;Yashwant Singh Patel;Karanam Mahidhar;Rajiv Misra
2019 11th International Conference on Communication Systems & Networks (COMSNETS)
Year: 2019 | Conference Paper |
Cited by: Papers (7)
In a decentralized vehicle-to-vehicle (V2V) energy trading among electric vehicles (EVs), there is a need of a highly secure and privacy protected transaction environment. With the advent of digital currencies like Bitcoin, most of the blockchain applications incorporate a similar cryptocurrency system for secure processing. In this work, we propose a permissioned blockchain based energy trading a...Show More
Cognitive radio networks (CRNs) involve an extensive exchange of control messages to coordinate spectrum sensing, medium access, routing, etc. Static control channel allocation uses preassigned common control channel (CCC) contrary to the opportunistic access paradigm. We propose a k-hop clustering scheme for the optimal CCC assignment problem (k-CCCP) for CRNs. We prove that the k-CCCP problem is...Show More
Minimum Interference Topology Control in Cognitive Radio Networks through Channel Assignment
2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Year: 2018 | Conference Paper |
Connectivity is a fundamental problem in wireless network. Connection between any two random nodes in a cognitive radio ad hoc network cannot be determined by their distance and transmission powers only. It also depends on the whether they have common available channel to open for communication. Due to primary users' (PUs') activities, the nature of available spectrum is non-homogeneous and dynami...Show More
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