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Multi-hop emergency message dissemination through optimal cooperative forwarder in grid-based 5G-VANETs

Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4461-4476页

作者： Ullah, Sami Abbas, Ghulam Waqas, Muhammad Abbas, Ziaul Haq Halim, Zahid Telecommunications and Networking Research Center GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Computer Science Shaheed Benazir Bhutto University Sheringal Upper Dir Pakistan Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan School of Engineering Edith Cowan University PerthWA6027 Australia Department of Computer Engineering College of Information Technology University of Bahrain Zallaq32038 Bahrain Faculty of Electrical Engineering GIK Institute of Engineering Sciences and Technology Topi23460 Pakistan

Emergency message (EM) dissemination is a significant process in vehicular ad hoc networks (VANETs) and plays a vital role in road safety. Nonetheless, EMs’ dissemination while avoiding broadcast storms poses a considerable challenge. Moreover, selecting optimal cooperative forwarders is also challenging because of unreliability, high end-to-end delay, and packet loss caused by localization errors. Besides, the conventional broadcast-based schemes usually choose forwarder vehicles based on distance, neglecting the influence of links’ qualities. To that end, we propose multi-hop EM dissemination (MEMD) through optimal cooperative forwarder in grid-based fifth-generation (5G) VANET environment. To ensure reliable EMs’ dissemination, we incorporate vehicle-to-everything communication instead of the conventional short-range vehicle-to-vehicle communication. The proposed scheme obtains vehicle positions by integrating GPS measurements with a map-matching technique to avoid vehicle localization errors. Instead of a single parameter, the proposed scheme selects an optimal cooperative forwarder by using our proposed fitness function, which considers different mobility and social parameters. The proposed scheme categorizes EMs’ dissemination into single-hop and multi-hop broadcasting to avoid unnecessary rebroadcasting and communication congestion in the network. Simulation results demonstrate that for varying density, the proposed scheme achieves an average increase of 12%, 9%, and 8.9% in coverage, and 7.95%, 4%, and 3.4% in PDR, and an average reduction of 13.5%, 7.2%, and 12.7% in E2E delay, and 20.9%, 11.3%, and 4% in rebroadcast ratio, as compared to LCL, REMD, and EMDZoI, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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A Deep Learning Model-Based Neural Network Framework for Diabetic Foot Ulcer Classification 5

A Deep Learning Model-Based Neural Network Framework for Dia...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Shanmugam, P. Kumar, P.K. Manoj Hussain, Mohammad Manzoor Revathi, R. Deepa, P. Sakthivelu, U. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Subbalakshmi Lakshmipathy College of Science Department of Computer Science Tamil Nadu Madurai India B v Raju Institute of Technology Department of Computer Science and Engineering Medak Telangana Narsapur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Texcity Arts and Science College Department of Computer Science Madukkarai Market Coimbatore India College of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350379945

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes, frequently resulting in amputation and having a significant impact on patients' quality of life. Early and accurate DFU classification is essential for prompt and efficient therapy. This study introduces an innovative neural network framework that utilizes deep learning techniques to categorize DFU based on thermograms. The proposed framework employs the pre-trained EfficientNet-B0 deep learning model, well-known for its exceptional performance in image classification tasks. The framework underwent training and assessment using a dataset sourced from Kaggle, consisting of 1866 colored images. Among them, 890 images depict plantar thermograms with negative DFU, whereas 976 images depict foot thermograms with positive DFU. The proposed framework demonstrated a higher accuracy of 98.71, outperforming previous approaches by a wide margin. The findings demonstrate the ability of deep learning to categorize DFU, paving the way for the development of automated testing techniques to assist healthcare practitioners in the timely identification and treatment of this incapacitating ailment. © 2024 IEEE.

关键词： Ability testing

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Methods for Predictive Performance Improvement of Deep Learning Systems for Aerial Building Roof Detection with Multispectral Images

Methods for Predictive Performance Improvement of Deep Learn...

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2023 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2023

作者： Churi, A. Megherbi, D.B. University of Massachusetts Center for Human/Machine Intelligence Networking Distributed Systems Department of Electrical and Computer Engineering LowellMA United States

ISBN: (纸本)9781665434454

In this paper, we present a dilated convolutional neural network-based deep learning system for the challenging problem of building accurate roof pixel detection from city aerial multispectral images in the visible and infrared spectral bands. The proposed system uses an automatic spectral band selection strategy and an automatic histogram-based threshold-finding method to improve the network's prediction accuracy performance. Current existing systems train a single model on all the cities of interest data at once. These systems also usually use only RGB bands and avoid post-processing of the predicted images for increased prediction accuracies. In this paper, we present the following solutions to these object detection challenges, 1) we propose and implement a separate model for each city dataset, 2) we propose an optimal band selection strategy to choose the most informative visible-infrared bands from multispectral images, 3) we propose an adaptive and automatic histogram-based optimal threshold finding technique for building roof object segmentation. Our results show that having a separate model for each city rather than only one model for all the combined cities increases the system's performance by 20.77% in building roof pixel prediction in any of the cities considered. We show that our system also has a 35.6% increase in F1 score compared to its state-of-The-Art counterpart, the winning implementation of the SpaceNet building detection national challenge, which took place in 2017. © 2023 IEEE.

关键词： Object recognition

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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

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

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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Optimizing Logistics with Regularization Techniques: A Comparative Study

Optimizing Logistics with Regularization Techniques: A Compa...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Grace, Anne Lourdu Thenmozhi, M. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350310061

Optimising logistics operations is critical for modern businesses as it can significantly impact supply chain efficiency, customer satisfaction, and overall profitability. One way to enhance logistics performance is by employing machine learning models, such as logistic regression, to predict and optimise various logistics outcomes. However, these models often face the issue of overfitting, which can result in poor generalisation and inaccurate predictions. This paper presents a comparative study of various regularisation techniques, such as Ridge and Lasso, applied to logistic regression models for optimising logistics operations. Our results indicate that regularisation techniques can significantly improve the performance of logistic regression models in predicting and optimising logistics outcomes. The Lasso regularisation technique is particularly effective in feature selection, reducing the number of variables needed to achieve high accuracy. Our analysis furthers to the research on machine learning applications in logistics and provides practical insights for logistics managers to optimise their operations. © 2023 IEEE.

关键词： Forecasting

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An Effective Deep Learning based Recommender System with user and item embedding

An Effective Deep Learning based Recommender System with use...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Nareshkumar, R. Agalya, K. Arunpandiyan, A. Vijayalakshmi, M. Ranjani, V. Ramya, A. SRM Institute of Science and Technology Department of Networking and Communication Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India A. v C. College of Engineering Department of Computer Science and Engineering Mayiladuthurai India Care College of Engineering Artificial Intelligence and Data Science Trichy India Care College of Engineering Department of Computer Science and Engineering Trichy India Indra Ganesan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350334357

The success of deep learning in a variety of fields has spurred a rush of desire to develop new novel recommender systems. As a result, our research introduces new RecDNNing, a ground-breaking technique that combines integrated people and products with a deep learning model. There are two phases to the suggested suggestion method. We begin by constructing a unique mathematical version for each human and item, which we refer to as user and item embedding. The embeddings of the goods and users then are aggregated, concatenated, and sent into the deep learning model. In the second phase, the spliced people and items implants are utilised as sources, and the ratings scores are forecasted using the forward propagation technique. The suggested new RecDNNing algorithm outperforms state-of-the-art techniques on MovieLens, according to the results. © 2023 IEEE.

关键词： Recommender systems

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Cryptographic Ledger for Cyber Security in Smart Grid 2

Cryptographic Ledger for Cyber Security in Smart Grid

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Elanchezhiyan, E. Kalaiselvi, K. Banumathy, D. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur603203 India Paavai Engineering College Department of Computer Science and Engineering Tamilnadu Namakkal637018 India

ISBN: (纸本)9798350347579

In addition to the physical security of energy networks, cyber security is essential to protecting these systems as well. Cyber threats can stem from malicious hackers who have infiltrated the networks to gain unauthorized access to sensitive data, or from vulnerabilities within the systems themselves. It is increasingly important that smart grid companies invest in cyber security solutions, such as strong passwords, two-factor authentication, encryption, and regular software updates, to counteract these threats. Additionally, it is beneficial for organizations to create incident response plans that are tailored to their specific needs, and which define the chain of command and actions to take in the case of an incident. To details the usage of smart grids in various domains and places in an effective way and explains the efficient way of consuming power for smart ventilators by monitoring and providing cyber security against cyber-attacks. The distributed power from various regions is collected from the less predominant places and supplied to the smart ventilators through smart inverters. © 2023 IEEE.

关键词： Cybersecurity

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Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mURLLC Wireless Networks 57

Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mUR...

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57th Annual Conference on Information Sciences and Systems, CISS 2023

作者： Zhang, Xi Zhu, Qixuan Poor, H. Vincent Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

ISBN: (纸本)9781665451819

The sixth generation (6G) mobile wireless networks are expected to provide massive ultra-reliable and low-latency communications (mURLLC) for data services, which require extremely stringent quality-of-services (QoS) with massive connectivity. Rate splitting (RS) multiple access schemes have been proposed recently for tackling the difficulty of the massive access yielded by a large number of mobile users (MUs) requesting to connect to a single base station. Massive multiple-input and multiple-output (massive-MIMO) communication is considered a promising solution to serve massive MUs in 6G wireless networks. However, how to apply RS schemes into massive-MIMO techniques to support the massive access has not been sufficiently studied. In this paper, we propose to integrate RS schemes with massive-MIMO techniques to achieve mURLLC transmissions under a Nakagamir $m$ fading wireless channel. First, we establish the system model for the integrated RS and massive-MIMO schemes. Then, we derive closed-form expressions for the signal-to-interference-plus-noise ratios (SINRs) under a Nakagami- $-m$ fading channel and obtain the channel capacity. Finally, we use numerical analyses to validate and evaluate our proposed RS and massive-MIMO integrated schemes. © 2023 IEEE.

关键词： Wireless networks

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The ϵ-Effective Capacity for Statistical Delay and Error-Rate Bounded QoS Provisioning Over 6G CF M-MIMO Wireless Networks Using HARQ-IR 57

The ϵ-Effective Capacity for Statistical Delay and Error-Ra...

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57th Annual Conference on Information Sciences and Systems, CISS 2023

作者： Zhang, Xi Wang, Jingqing Poor, H. Vincent Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

ISBN: (纸本)9781665451819

Significant effort has been devoted to the problem of guaranteeing stringent ultra-reliable and low-latency communications (URLLC) while introducing new requirements for better quality-of-services (QoS) over next generation wireless networks. One of the major design issues raised by URLLC is how to support explosively growing demands for delay-sensitive multimedia applications while guaranteeing ultra-reliability. Towards this end, there have been a wide spectrum of promising techniques, including statistical delay and error rate-bounded QoS provisioning, cell-free (CF) massive multiple-input-multiple-output (m-MIMO), finite blocklength coding (FBC), hybrid automatic repeat request with incremental redundancy (HARQ-IR) protocol, etc. However, when being integrated with FBC, how to rigorously and efficiently characterize the dynamics of mobile wireless networks in terms of statistical delay and error-rate bounded QoS provisioning for CF m-MIMO has imposed many new challenges not encountered before. To overcome these challenges, in this paper we develop statistical delay and error-rate bounded QoS analytical modeling schemes to characterize the FBC-based ϵ-effective capacity over 6G CF m-MIMO wireless networks using HARQ-IR. In particular, first we establish CF m-MIMO based system architecture models. Second, we apply a HARQ-IR protocol for deriving the channel coding rate, outage probability, and FBC based ϵ-effective capacity function using the Mellin transform. Finally, we conduct a set of simulations to validate and evaluate our proposed schemes. © 2023 IEEE.

关键词： Wireless networks

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A Comprehensive Analysis of Intrusion Detection System using Machine Learning and Deep Learning Algorithms

A Comprehensive Analysis of Intrusion Detection System using...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Varaprasad, R. Prabhu Chakkaravarthy, A. Veeresha, M. Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Dr. K.V. Subba Reddy Institute of Technology Department of Computer Science and Engineering Kurnool India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

The Internet of Things (IoT) plays an essential character in both domestic and corporate sectors by facilitating connectivity and automation through a network of devices and sensors. IoT wearable gadgets, sensors, and embedded devices are widely utilized for data collection and transmission. However, privacy and security are challenging tasks to be achieved while the standard implementation of monitoring and security operations is not possible because of large number of IoT devices, their heterogeneity and technical specification limitations. In order to solve this issue, Intrusion Detection System (IDS) is introduced to secure the network against various vulnerabilities and threats. To identify intrusion attacks accurately, Machine learning (ML) and Deep Learning (DL) algorithms namely, Random Forest (RF), Logistic Regression (LR), Decision Tree (DL), AdaBoost, XGboost, Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and Long Short-Term Memory (LSTM), Deep Neural Network (DNN) are used. This research aims to identify the strengths, relationship, and challenges of the ML and DL-based prediction approaches. © 2024 IEEE.

关键词： Network intrusion

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