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Network Coding Techniques for Resource Allocation in Wireless Relay Networks

Network Coding Techniques for Resource Allocation in Wireles...

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Automation and Computation (AUTOCOM), International Conference on

作者： Lalit Khanna Ranganathaswamy M K Balamurugan V Ankita Agarwal Maharishi Rajat Saini Sarmistha Nanda Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Mechanical Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Applications Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Network coding is an applicable technique to enhance the performance of wireless relay networks, in which intermediate nodes are utilized to expand the coverage range and increase communication reliability. The utilization of network coding in these networks should be well addressed by resource allocation such as bandwidth and power allocation. Among them, network coding for resource allocation in wireless relay networks is the most attractive research work. In particular, dynamic network coding is a strategy that varies how we code data according to current conditions in the network so as to achieve the maximum possible throughput during relaying. Dual-joint power and resource combinations have relatively better performance gains due to the efficient channel gain with an additional level of resource utilization for enhanced resource allocation. In addition, network coding schemes, including opportunistic and homomorphic network coding, have been introduced to perform well in an environment with fading channels and enhance data transmitting reliability. These techniques take advantage of the intrinsic diversity in wireless pathways to improve overall network throughput and reduce latency.

关键词： Wireless communication Fading channels Relay networks Bandwidth Quality of service Network coding Performance gain Throughput Resource management Reliability

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Optimizing Battery Energy Efficiency and Power Consumption for Extended Range in Electric Vehicles 3

Optimizing Battery Energy Efficiency and Power Consumption f...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Bhatnagar, Tarang Mohanty, Prithviraj Sood, Gourav Krishnan, Gopala Jalall, Kehkeshan S Agarwal, Ankita Centre for Interdisciplinary Research for Business and Technology Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

Electric-powered motors (EVs) are gaining a reputation as an alternative to conventional fuel-powered motors because of their environmental advantages and rising fuel charges. However, their restrained battery range and excessive energy intake are giant limitations to their substantial adoption. To deal with those problems, this venture aims to optimize the battery power performance and energy consumption of EVs for a prolonged variety. First of all, the battery utilization may be optimized through superior electricity control algorithms. This will involve predicting and managing the energy needs of the car via sensible, manipulated structures. By optimizing battery utilization, the overall strength performance of the vehicle will be expanded, making an allowance for an extended range. Secondly, the power intake of the EV can be optimized by using lightweight materials, green motor and drivetrain designs, and advanced regenerative braking systems. This technology will lessen the strength required to strengthen the automobile, further increasing its variety. Moreover, the project will develop clever charging strategies for EV proprietors to price their motors at some stage in off-height hours while energy is cheaper and the grid is less congested. This may reduce energy consumption and improve battery life. © 2025 IEEE.

关键词： Energy utilization

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Unlocking the Full Potential of Battery Energy Storage Systems in Electric Vehicles

Unlocking the Full Potential of Battery Energy Storage Syste...

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Automation and Computation (AUTOCOM), International Conference on

作者： Sankar Kumar Mridha Kanika Seth Srikrishna Baskar Rao Natya S Savita Neelesh Singh Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Aerospace Engineering JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The arena shifts closer to sustainable transportation, electric-powered vehicles (EVs) have emerged as a promising solution to reduce greenhouse gasoline emissions. However, the great adoption of EVs faces numerous challenges, such as confined riding ranges and lengthy charging instances. Battery electricity storage systems were diagnosed as a critical technology to address those barriers and free up the total capacity of EVs. BESS allows for the efficient garage and control of power in EVs, allowing quicker charging instances and extended driving variety. This gadget now not only benefits the vehicle proprietor but also offers grid flexibility and balance by helping smart charging and car-to-grid (V2G) capabilities. BESS additionally has the potential to reduce the strain on the present power grid by moving charging demand to off-peak hours. To completely leverage the benefits of BESS in EVs, sizable advancements are wanted in battery technology, including elevated energy density, quicker charging skills, and longer lifespan. Additionally, studies and development are required to optimize the integration of BESS with the vehicle and the grid. With speedy improvements in the development of BESS and expanded investments in EV infrastructure, the total potential of battery power garage structures in electric-powered motors can be unlocked. It may no longer be the best way to accelerate the transition toward sustainable transportation; however, it provides full-size financial and environmental blessings.

关键词： Vehicle-to-grid Electric potential Electricity Transportation Motors Power grids Protection Petroleum Strain Smart charging

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Cloud Computing Improved Clustering using Intrusion Detection

Cloud Computing Improved Clustering using Intrusion Detectio...

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Advances in Power, Signal, and Information Technology (APSIT), International Conference in

作者： Saloni Bansal Amandeep Singh Sheela Bijlwan Bhawna Goyal Ayush Dogra Dawa Chyophel Lepcha Department of Electronics and Communication Engineering GLA University Mathura India Computer Science and Engineering Gulzar Group of Institutions Ludhiana Punjab Computing Sciences Uttaranchal Institute of Technology Uttaranchal University Dehradun India University Center for Research and Development Chandigarh University Mohali Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electronics and Communication Engineering Chandigarh University Mohali Punjab India

It is one of the more difficult tasks and calls for highly skilled specialists to deal with the aspects and systems of action of intruders and then unauthorized users who attempt to restrict the data with the computer network. The development of the computer network, the number of intruders, and then the infiltration are all possible effects of incorporating cloud computing. Generally speaking, the updating that comes from properly managed vocations is wasted through its count above the time and it rises before it manifests with the technique. There are many unthinkable state gaps for spotting invasions, and then the numerous communications are what matter most. In order to achieve infiltration through the capture of many capability systems (maximum and less expensive tasking), the usage of advanced detection with the new then the discovering systems is utilized with many than the previous in the cloud. The network warning identification and variation systems based on machine learning, which is a demanding warning identification technology, were separately examined in research and development with research precision. Where there is still room for improvement, the work detailed here elaborates on the forest by choosing its merits and then uses an improved density-based spatial clustering of applications with noise (DBSCAN) through grouping and then the intrusion identification with the cloud environment to remove these flaws.

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The Role of Flexible Batteries in Enabling Electric Vehicle Adoption 3

The Role of Flexible Batteries in Enabling Electric Vehicle ...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Rekha, K. Bhanu Singh, B.P. Kaur, Prabhjot Mahalat, Mahabub Hasan Ahluwalia, Gunveen Premkumar, R. Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre for Interdisciplinary Research for Business and Technology Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

Flexible batteries, also known as thin-movie batteries, have emerged as a promising technology in the race toward the giant adoption of electrical automobiles (EVs). These batteries provide several blessings over conventional lithium-ion batteries, which currently electricity most EVs, including decreased weight, advanced safety, and better electricity garage capability. One of the foremost demanding situations dealing with EV adoption is the restrained use variety presented through conventional batteries, which require frequent recharging. This has been a major barrier for ability customers, who may have yet to get admission to charging stations or may be deterred by the prolonged charging process. Bendy batteries, with their lightweight and excessive electricity storage potential, can appreciably grow the use of a variety of EVs, making them an extra feasible opportunity for fuel-powered motors. Moreover, bendy batteries may be incorporated into the design of the car, making it possible to create extra aerodynamic and space-green EVs. This allows for greater flexibility in car design and reduces the general value of production. Similarly, as EVs become more extensively used, the call for bendy batteries will grow due to economies of scale and doubtlessly decrease the fee of manufacturing even in addition. © 2025 IEEE.

关键词： Lithium-ion batteries

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The Novel use of AI and ML System Integration in Developing Spruce Metropolis

The Novel use of AI and ML System Integration in Developing ...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Rahul Singh K Maheswari G Sridevi Ruby Pant Laith H. Alzubaidi Mahalakshmi Shetty Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of CSE CMR Technical Campus Hyderabad Telangana India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem India Department of Mechanical Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India The Islamic university Najaf Iraq Department of computer science engineering R.L Jallappa institute of technology doddabalapura

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

This paper presents a pioneering routing protocol, particularly in urban infrastructures, by the utilization of a combination of SDN and machine learning technologies, mostly Naïve Bayes, to the programmer. The emerging Internet of Things and 5 G will offer new improvements in connectivity and infrastructure management in smart cities. This paper therefore targets transportation systems of smart cities in order to reduce end-to-end delays and reduce air congestion through predictive routing optimization. The proposed framework is conducted through several steps, among them the generation of a complete dataset derived from the real-world traffic simulations, and realized through the utilization of a mechanism that is commonly known as the Simulation of Urban Mobility (SUMO). This will help the proposed protocol to know the good approaches based on predictive routing with the help of defined Naïve Bayes algorithms in the SDN controller. This architecture introduces a central controller responsible for coordinating communication in the network. The evaluation of the performance of existing routing methods, including multipath routing, optimized link state routing (OLSR), and along with Q learning, shows the importance of the proposed SDN-Naïve Bayes approach. The metrics are packet delivery ratio, throughput, packet loss ratio, end-to-end delay, packet jitter, etc., which will show the effectiveness of the new protocol when enjoying the high quality of the network. The proposed protocol has great potentials in improving the efficiency and reliability of communications in smart city environments using the capacities of SDN and ML. Introducing changes in the route in a predictive manner will not only optimize the use of resources, but also help in the achievement of reliable and reliable connectivity, which holds importance for the development of urban infrastructure.

关键词： Smart cities Transportation System integration Traffic control Routing Throughput Routing protocols

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Intelligent Energy Management System for Microgrids with Energy Storage Integration 3

Intelligent Energy Management System for Microgrids with Ene...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Nanda, Simranjeet Kumar, Maddu Naveen Baxi, Paramjit John, Monoo Manaswini, R. Yadav, Dhananjay Kumar Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

The items are a software solution, it is designed for micro grids with energy storage integration to monitor, control, and best utilize available grid resources. Also called a mini-grid, the micro grid is a distributed energy system consisting of local small-scale generation and storage facilities that could operate independently from the main grid in an emergency. Micro grids are frequently paired with solar panels and wind turbines, making them more sustainable. The capacity of energy storage systems in micro grids has been playing an important role, as renewable energy in devices with temporal availability generates the need for their more efficient use. Based on real-time data, the items apply energy transmission and distribution management algorithms to control where the power is used in a micro grid. The items continuously monitor the demand and supply of energy to investigate the ideal combination of renewable generation and stored energy for peak shaving. In doing so, the micro grid can run optimally and minimize energy waste without running up bills. The items can also predict how and when individuals use energy, thus allowing better scheduling of the available resources. This enables the items to conduct predictive maintenance on those micro grid components to long and function as efficiently as possible. © 2025 IEEE.

关键词： Marketing

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Reinforcement Learning-Based Optimal Power Flow Control in Islanded Microgrids 3

Reinforcement Learning-Based Optimal Power Flow Control in I...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Agarwal, Ankita Kumar, Amit Maity, Dipankar Ojha, Varun Nalini Sunil, M. Dore, Aruna Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798331542375

One of the popular paradigms for control problems in different applications is Reinforcement Learning. In this paper, we develop an RL-based technique for OPF control in islanded micro grids. Micro grids are small power systems for remote or rural regions that can function independently of the national grid and serve as a dependable alternative. The dependence on their decentralized power generation over DERs is a major complication for micro grid system stability and noteworthy optimization of reactive power flow. The operation of micro grids is both dynamic and stochastic;as a result, operational models based on traditional control methods at the model level or rule-based conflicts with optimized controllers that need to work in an environment where the future long-term forecasts remain uncertain. RL is suggested as a data-driven and adaptive control method to address these issues and optimize power flow in islanded micro grids. The RL-based control proposed in this paper uses realtime data from the micro grid to learn an optimal policy. The presented micro grid environment is for the reinforcement learning agent with which it interacts, and on success or failure of its actions, it will be given a reward signal to learn. The RL agent, through its interactions and learning process by taking actions during the operation of islanded micro grids, can optimize power flow in a distributed manner given DERs availability, load demand dynamics, and network constraints. © 2025 IEEE.

关键词： Reinforcement learning

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Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Microgrid Control 3

Deep Learning-Based Forecasting of Solar Photovoltaic Genera...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Kumar, K. Ankith Garg, Mohan Shah, Manita D. Ahmed, Kalyandurg Rafeeq Agarwal, Trapty Saraswat, Vivek Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India

ISBN: (纸本)9798331542375

Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Micro grid Control is a new approach to accurately predicting solar photovoltaic generation in micro grids. These feature renewable energy such as Solar PV and are a responsive approach for providing efficient, sustainable power solutions. In contrast, solar PV power production is not dispatch able because it depends on the changing weather and other factors. The new method applies deep learning algorithms, mainly artificial neural networks, to historical data to accurately forecast solar PV output. The latter sorts the variation due to a broad array of meteorological and operational parameters, including temperature, humidity level, cloud cover, and shading Moreover, the model can forecast results online and keep learning with new data for real-time micro grid control. The deep learning-based method has shown superior performance to traditional forecasting methods in improving the precision of PV generation forecasts. Benefits include more efficient energy management, better integrating renewable sources into the micro grid, and lower costs. This means that the Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Micro grid Control could significantly improve the reliability and efficiency of micro grid operations and promote a more sustainable and resilient energy system. © 2025 IEEE.

关键词： Marketing

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Analysis of Convolutional Neural Network Architectures for Image Denoising and Restoration 3

Analysis of Convolutional Neural Network Architectures for I...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Mishra, Mukul Gupta, Rahul Frank, Anilloy Augustine Agarwal, Trapty Mahajan, Vibhor Priyadarshini, Sushree B. B. Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

Convolutional Neural Networks have performed excellently in image processing applications such as Image Demising and Restoration. CNN's performance and accuracy depend on its design and architecture. This work analyzes delousing and image restoration using different CNN architectures. In this experiment, three popular CNN architectures, U-Net, Res Net, and Dense Net, are trained on different noise levels. In this section, we analyze each parameter for the proposed CNN models, i.e., filter size and depth, etc., to evaluate performance variations due to these hyper parameters. Our results indicate that with a longer network and large filter sizes, it may be possible to deny the images more accurately for restoration. This development set opened as we experimented with fine-tuning the other tasks (DE noising and restoration) using pretrained models. We provide empirical evidence that has shown that incorporating pretrained models can result in a substantial improvement in the performance of CNNs with a limited amount of data. This work represents a complete investigation of CNN architectures, such as DE noising and restoration, and explains why specific engineering choices are effective. These results can be useful in enhancing accuracy and performance for image processing tasks using CNN models. © 2025 IEEE.

关键词： Internet

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