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An energy-efficient task and virtual machine placement in virtualised cloud server using FY-SFLA and RMMS-DLVQ

International Journal of networking and Virtual Organisations 2023年第2期29卷 144-167页

作者： Sudhakar, E.P. Saravanan, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Creating infrastructures, virtual servers, computing resources, along with devices is termed virtualisation. In this methodology, to augment resource usage along with to mitigate the total power consumption, mapping of a group of virtual machine (VM) onto a set of physical machines (PM) is performed in a data centre (DC). Nevertheless, a crucial challenge is presented by the VM allocation together with the higher energy consumption (EC) of cloud data centres (CDC). Thus, to alleviate the resource wastage along with to mitigate the DCs' EC, an effectual Fisher Yates-Shuffled frog leaping algorithm (FY-SFLA) is proposed here: 1) task feature extraction;2) resource information extraction;3) task separation by utilising cosine distance - K means algorithm (CD-KMA);4) task placement in VM by employing the FY-SFLA task;5) VM status identification by deploying random mutation monkey search deep learning vector quantisation (RMMS - DLVQ) are '5' phases comprised in the proposed methodology. © 2023 Inderscience Enterprises Ltd.

关键词： Virtual machine

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Latency and Cost-Aware Deployment of Dynamic Service Function Chains in 5G Networks

Latency and Cost-Aware Deployment of Dynamic Service Functio...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Karim, Amrin Ema, Jannatul Yasmin, Tasnia Roy, Palash Razzaque, Md. Abdur Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Green Networking Research Group Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350335590

Efficient deployment of a service function chain (SFC) on virtual network functions (VNFs) and their mapping to virtual machines (VMs) in 5G networks is highly important to reduce user application service latency as well as to minimize the cost of VM migration. Existing works in the literature have either considered service latency or migration cost while deploying an SFC on VMs. In this paper, the problem of dynamically mapping VNFs running a SFC to different virtual machines in a 5G network has been formulated as a multi-objective linear Programming (MOLP) problem. The developed optimization model, namely Trade-Lcm, minimizes user application service latency while reducing the cost of migrating virtual network functions associated with an SFC. The numerical performance analysis results demonstrate a significant improvement in minimizing service latency of user applications and cost of VM migration as high as 30% and 10%, respectively, compared to the state-of-the-art work. © 2023 IEEE.

关键词： Multiobjective optimization

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Energy Consumption Aware Delay Minimization for UAV Enabled Internet of Vehicle 5

Energy Consumption Aware Delay Minimization for UAV Enabled ...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Emon, Shariar Hossain Rahman, Md Lotifur Omi, Sumiya Siddika Roy, Palash Razzaque, Md. Abdur Shadman Wadith, K.M. Ahmed, Jargis Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Green Networking Research Group Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

Because of its adaptable nature and excellent maneuvering capabilities, the Unmanned Aerial Vehicle (UAV) can develop a potential solution for executing the Internet of Vehicles (IoV) applications. However, the limited battery capacity of the UAV devices imposes significant barriers to realtime task execution. Existing works are limited to either considering the executing tasks at the UAVs or Road Side Units (RSUs) without considering the collaborative relationship between them for minimizing service delay. In this work, we have presented an optimization framework for task execution of the Internet of Vehicles by exploiting Multi-Uav,Task Executing of Internet of Vehicle by exploiting Multi-UAV namely TEIMU, which minimizes the total average latency by considering energy consumption. The developed TEIMU framework is a combinatorial optimization problem that offloads the tasks by collaboration of RSUs and UAVs. According to the experimental findings, the developed system, TEIMU, surpasses state-of-the-art works in terms of overall average latency and energy usage by up to 10% and 25 %, respectively. © 2023 IEEE.

关键词： Energy utilization

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Enhancing WRSN Sustainability through On-demand Directional Wireless Charging with Multiple Green-powered Mobile Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 18019-18030页

作者： Ma, Xiongbo Liu, Xilong Ansari, Nirwan Ding, Jun The School of Information Science and Engineering Yunnan University Yunnan Kunming650500 China The Advanced Networking Laboratory Helen & John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Current research on scheduling mobile charging vehicles (MCVs) generally focuses on periodic and omnidirectional charging of sensor nodes (SNs). However, this approach leads to significant energy wastage, especially when relying on fossil energy sources. In this work, we propose to schedule multiple green energy-powered MCVs equipped with directional antennas to efficiently charge SNs. We first develop an on-demand and directional charging model based on far-field wireless charging. Then, we formulate the SN survival maximization problem to efficiently prolong the lifetime of wireless rechargeable sensor networks (WRSNs). Given the NP-hard nature of this optimization problem, we develop the three-Step directIonal wireless charGiNg (SIGN) algorithm to efficiently solve this problem. SIGN strategically determines the MCVs’ anchor points (APs), traveling paths and wireless energy emitting directions. Finally, we conduct extensive simulation experiments to validate the superiority of our proposed algorithm in optimizing energy usage and prolonging network lifetime. © 2014 IEEE.

关键词： Wireless charging

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User Quality of Experience and Profit Aware Task Allocation in Mobile Device Cloud

User Quality of Experience and Profit Aware Task Allocation ...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Sharmin, Zeneya Roy, Palash Razzaque, Md. Abdur Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Green Networking Research Group Department of Computer Science and Engineering University of Dhaka Bangladesh

ISBN: (纸本)9798350335590

Mobile Device Cloud (MDC) is a promising and lucrative cloud environment that uses the idle resources of nearby mobile devices to improve the performance of compute-intensive applications. By computing code on nearby devices instead of a distant master cloud, the MDC system can improve the performance of real-time applications. It is challenging for the MDC system to efficiently use the resources because of cost and QoE. While allocating high-quality resources to mobile applications might be effective in decreasing the latency, it will increase the cost, and the allocation of low-quality resources will increase execution latency, diminishing the quality of experience (QoE) of the users. The works in the literature didn't come up with methods of trading off these two conflicting objectives in a fair way. In this paper, we present an optimization framework, namely QCMDC, for allocating tasks in an MDC environment, which brings a trade-off between maximizing users' QoE and minimizing the execution cost of the worker devices. Empirical evaluations have been carried out in Python and the results demonstrate significant performance improvement in terms of QoE and execution cost compared to other state-of-the-art works. © 2023 IEEE.

关键词： Multiobjective optimization

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham Science Publishers.

关键词： Convolutional neural networks

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Energy and Latency-aware Computation Load Distribution of Hybrid Split and Federated Learning on IoT Devices 23

Energy and Latency-aware Computation Load Distribution of Hy...

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10th International Conference on networking Systems and Security, NSysS 2023

作者： Hossan, Sakhaouth Mahmud, Farhan Roy, Palash Razzaque, Md. Abdur Rahman, Md. Mustafizur Green Networking Research Group Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798400708787

Split learning (SL) and Federated Learning (FL) are popular distributed learning frameworks used to increase data privacy and reduce computation loads of Internet of Things (IoT) devices. However, one of the major challenges of distributed learning on IoT devices is determining the portion of computation load to be assigned for the devices compared to the server side. The contributions of the existing works in the literature are either limited by consideration of homogeneous resources available at all IoT devices or by not distributing computation loads among the devices in an efficient way. In this paper, we propose an adaptive clustering-based computation load distribution method for IoT devices, with heterogeneous resource capacities, participating in the model training. The clustering makes the optimal determination of the split point of the learning model, which is scalable even for a large number of devices. The numerical evaluation of the proposed learning model implemented using Python 3.0 and the comparative performance results show that the proposed load distribution policy for the learning models reduces the time by 160 times on average compared to the usual brute force method. © 2023 ACM.

关键词： Internet of things

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SMART DRIVING LICENSING SYSTEM AND AUTHORIZATION OF AUTONOMOUS VEHICLE INTEGRATING WIRELESS SENSOR NETWORKS AND IoT

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 69-85页

作者： Santhakumar, D. Sudhan, M.B. Prasanna, R. Manikandan, S.P. Prathaban, Banu Priya Kanna, Rajesh R. Saravanan, G. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Ramapuram India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sree Sastha Institute of Engineering and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Tamil Nadu Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This article presents an effective smart Driving Licensing System (SDLS), a digital framework designed to regulate the existing licensing system and authorize licensing for autonomous vehicles. Through a comprehensive assessment process, the system assesses the drivers and the autonomous vehicle’s ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. SDLS integrates high-level security through facial and fingerprint verification for identity verification to preserve licensing data. Hacking and unauthorized access to autonomous vehicles is prevented by robust cybersecurity protocols. The key benefits of SDLS include automated data collection, integration of wireless sensor networks (WSN) and Internet of Things (IoT), smart application processing, real-time license checks, cybersecurity measures, automated testing, and digital certification. The proposed system offers a comprehensive approach to safely integrating autonomous vehicles into our transportation ecosystem. The autonomous vehicles are integrated with antennas at rooftops or into bumpers, depending upon the vehicle design which can be connected with the SDLS for signal communication. Furthermore, the SDLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2024 by Begell House, Inc.

关键词： Autonomous vehicles

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