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Networking and communications (ICNWC), International Conference on

作者： Khushi Bhansali Praveena Akki Atturu Venkat Heeneth Sai Naga Deepak Bodepudi Arthi Krishnan Dept. Computer Science and Engineering SRM Institute of Science and Technology Chennai India Dept. Networking and Communication SRM Institute of Science and Technology Chennai India WA98008 Microsoft Seattle USA

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

Agriculture plays a vital role in the development of our country. In India, about 70 percent of the population depends on farming and one-third of the nation’s capital comes from farming. Issues concerning agriculture have been always hindering the development of the country. The only solution to this problem is agriculture by modernizing the current traditional methods of farming. Hence, this project aims at monitoring the temperature and moisture sensor using the necessary sensors. Measurement of these factors using these sensors in agriculture can be referred to as smart agriculture. Therefore, modern agriculture can positively impact the planet in immeasurable ways. It improves the yield of crops of all kinds, decreasing the dependence on pesticides, reducing operational costs, opti- mizing water usage, and ensuring better land management and crop rotation. The project addresses challenges in traditional agriculture such as limited data access, inefficient resource management, pesticide dependency, manual labor practices, and environmental impact, proposing modernization through IOT- based smart agriculture systems.

关键词： Temperature measurement Smart agriculture Temperature sensors Temperature Sociology Crops Pesticides

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An Energy-Efficient Jt-Comp Enabled Framework with Adaptive Oma/Noma in Hetnets

SSRN

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SSRN 2024年

作者： Akbar, Aamina Ahmed, Ashfaq Zafar, Adnan Jangsher, Sobia Islamabad Pakistan Dept. of Computer & Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electronic Engineering Dublin City University Dublin Ireland

Driven by massive Internet of Things (IoT) adoption, heterogeneity and diverse network requirements in 5G and beyond 5G (B5G), energy efficiency (EE) has also emerged as a crucial design objective. Having a single multipleaccess scheme that works for all users with diverse network constraints and requirements is not a resource efficient solution. While coordinated multi-point (CoMP) non-orthogonal multiple access (NOMA) addresses the requirements of users, particularly at the cell edges, in terms of spectral efficiency (SE), connectivity, and capacity. CoMP orthogonal multiple access (OMA) can mitigate intercell interference. Thus, a hybrid joint transmission (JT)-CoMP enabled OMA/NOMA technique that combines the advantages of CoMP OMA and CoMP NOMA and provides an easily adaptable solution is the way forward. This paper focuses on studying the EE of a JT-CoMP enabled OMA/NOMA heterogeneous network (HetNet). The paper jointly optimizes the selection of CoMP/non CoMP, selection of OMA/NOMA, power allocation and NOMA user pairing. We propose a heuristic Dynamic CoMP User Selection with Energy-Efficient Adaptive Multiple Access (DCEAMA) algorithm to solve the formulated problem. Our Monte Carlo simulations show that the JT-CoMP OMA/NOMA scheme surpasses both the pure CoMP OMA and CoMP NOMA schemes in terms of EE, with an average increase of 38% and 26% respectively. We compare our heuristic technique to an exhaustive search strategy to evaluate its efficiency. The findings indicate that our strategy produces comparable EE across various power levels with reduced computational complexity. © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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Mining Road Traffic Accident Data for Prediction of Accident Severity

Mining Road Traffic Accident Data for Prediction of Accident...

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2023 International Conference on Intelligent Data communication Technologies and Internet of Things, IDCIoT 2023

作者： Bahiru, Tadesse Kebede Manjula, V.S. Akele, Tadesse Birara Tesfaw, Engdaw Ayalew Belay, Tadesse Destaw Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information Technology Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Pg Dept of Computer Science Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information System Amhara Kombolcha Ethiopia Wachamo University School of Computing and Informatics College of Engineering and Technology Dept. of Information Technology SSNPR Hosaena Ethiopia

ISBN: (纸本)9781665474511

As World Health Organization reports show more than 1.3 million people die each year from traffic accidents and more than twenty to fifty million people are harmed by non-fatal accidents. Understanding and identifying the major factors of road traffic accidents can help the stakeholders to take appropriate measures to minimize the accidents. Support Vector Machine (SVM), J48, and Naïve Bayes (NB) classification techniques are implemented to build a model that predicts accident severities and to identify road traffic accident factors. As experimental findings showed weather conditions, number of lanes, road lighting conditions, and speed limit are the determinant factors that cause more accident severity. Finally, the classification and prediction performance of the models are compared using various performance evaluation techniques. J48 classifier has better classification performance than Support Vector Machine and Naïve Bayes classifiers. Naïve Bayes is the classifier that showed the lowest performance than others, but it has good performance in classifying fatal accident severities rather than serious and slight accident severities. © 2023 IEEE.

关键词： Support vector machines

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Reinforcement Learning Based Collaborative Perception for Vehicular Networks

Reinforcement Learning Based Collaborative Perception for Ve...

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IEEE Conference on Global communications (GLOBECOM)

作者： Hongyi Chen Zhiping Lin Yunjun Zhu Jieling Li Liang Xiao Yuliang Tang Yanyong Zhang Institute of Artificial Intelligence Xiamen University Dept. Information and Communication Engineering Xiamen University School of Computer Science and Technology University of Science and Technology of China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Reinforcement learning (RL)-based collaborative perception in vehicular networks chooses the sub-frame of radio channel resources for connected autonomous vehicles (CAVs) to exchange sensing data to enhance the perception performance, but leads to inaccurate detection in the light detection and ranging (LiDAR)-based object detection due to the asynchronous scan period of the LiDAR point clouds. This paper proposes a RL-based collaborative perception scheme to choose the transmit power and sub-frame to share the feature maps extracted from point clouds. Based on the estimated packet timestamp, the network topology and the channel gains among CAVs, this scheme enhances the perception accuracy and latency against path-loss and interference. The collaborative risk in the policy distribution is formulated as a weighted sum of the perception latency and packet loss rate to avoid the time asynchronization and information loss of the feature map exchange. The performance bound of the perception accuracy and latency is provided based on a Nash equilibrium of the cooperative game among CAVs. Simulation results based on five CAVs show the performance gain of the perception accuracy and latency over the benchmarks.

关键词： Point cloud compression Accuracy Laser radar Simulation Collaboration Packet loss Reinforcement learning Performance gain Feature extraction Sensors

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Power and Rate Allocation for Energy-Efficient Rate-Splitting Multiple Access via Deep Q-Learning

Power and Rate Allocation for Energy-Efficient Rate-Splittin...

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IEEE Globecom Workshops

作者： Maria Diamanti Georgios Kapsalis Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

Rate-Splitting Multiple Access (RSMA) has recently emerged as an effective technique for increasing network capacity by smartly controlling the tradeoff between decoding and treating interference as noise. In this paper, aligned with the need for sustainable wireless networks, we study the energy-efficient power and rate allocation of the common and private messages in the downlink of a network where rate-splitting is adopted. The corresponding energy efficiency maximization problem is transformed into a multi-agent Deep Reinforcement Learning (DRL) problem, based on which each private stream transmitted in the downlink constitutes a different DRL agent. The formulated DRL problem is solved by using Deep Q-Learning (DQL) algorithm and training a single Deep Q-Network (DQN) from the cumulative experiences gained from the DRL agents and their exploration of the environment, i.e., the exploration of different private-message power allocations. Numerical results obtained via modeling and simulation verify the effectiveness of the proposed DQL algorithm, demonstrating that it concludes solutions that outperform existing approaches from the literature in the achieved energy efficiency.

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Performance Evaluation of Blockchain systems Based Chaotic Multimedia Encryption

Performance Evaluation of Blockchain Systems Based Chaotic M...

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Jordanian Cybersecurity Conference (IJCC), International

作者： Rania A. Abdulkadhim Hamsa A Abdullah Dept. of Information and Communication Engineering College of Information Eng. Al-Nahrain University Baghdad Iraq Department of system Engineering College of Information Eng. Al-Nahrain University Baghdad Iraq

ISBN: (数字)9798331518462

ISBN: (纸本)9798331518479

Nowadays, we generate millions of data in the form of text, images, etc. Among them, images and text messages are widely used in communication. Due to their vulnerabilities, they are difficult to transmit in a secure way. Therefore, the data encryption algorithm is based on a 3D logistic map. Traditional data security solutions are diminishing due to the increasing role of third parties. The modern solution to these difficulties is blockchain technology. The cryptographic pixel values of the image and text files are saved on the blockchain in this system, which ensures the privacy and security of data. This paper proposes a blockchain architecture based on chaotic encryption to ensure data integrity. In this paper, the proposed blockchain framework will be tested using different sets of texts to evaluate the performance metrics such as (NPCR) equal to 100%, (UACI) equal to 38.54%, and entropy equal to 7.58 in different attack scenarios. The performance metric for the peer-to-peer blockchain, such as Transactions Per Second (TPS), is tested.

关键词： Performance evaluation Data privacy Three-dimensional displays Chaotic communication Scalability Robustness Blockchains Encryption Servers Logistics

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Low-PAPR Joint Channel Estimation and Data Detection in ZP-OTFS system

arXiv

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arXiv 2023年

作者： Aghda, Omid Abbassi Omidi, Mohammad Javad Saeedi-Sourck, Hamid The Dept. of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran The Dept. of Electronics and Communication Engineering KCST Kuwait Doha35003 Qatar The Dept. of Electrical Engineering Yazd University Yazd89158-18411 Iran

Orthogonal Time Frequency Space (OTFS) systems face significant challenges in channel estimation due to high pilot overhead and peak-to-average power ratio (PAPR). To address these issues, we propose a two-step channel estimation method for Zero-Pad OTFS (ZP-OTFS), a modified OTFS system characterized by multiple zero rows along the delay axis. This method strategically inserts pilot sequences into the zero bins of the ZP-OTFS system, effectively mitigating overhead and PAPR. Comprehensive simulation results validate the effectiveness of our proposed method, demonstrating its superior performance over traditional embedded pilot estimation in high Signal-to-Noise Ratio (SNR) scenarios. Specifically, our method achieves a lower normalized mean square error (NMSE) and better bit error rates (BER) at high SNRs. © 2023, CC BY.

关键词： Peak to average power ratio

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Design of a 28nm CMOS Self-Biased Ring Oscillator for Intrinsically Robust PVT TRNG

Design of a 28nm CMOS Self-Biased Ring Oscillator for Intrin...

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Ph.D. Research in Microelectronics and Electronics (PRIME)

作者： João Cabacinho João Casaleiro Luís Oliveira Dept. of Electrical and Computer Engineering NOVA School of Science and Technology Caparica Portugal Center for Technologies and System (CTS) - UNINOVA Dept. Electrical Telecommunications and Computers Engineering ISEL Lisboa Portugal

In this paper we present a dynamic entropy generator based on the oscillator jitter, which uses a self-biasing technique to minimize process, voltage, and temperature (PVT) variations. The proposed circuit has the advantage of its intrinsic robustness due to self-biasing design, which eliminates the need of additional calibration steps and simplifies the circuit topology, with a negligible impact in area and power consumption. A frequency deviation below 15 % and a maximum jitter difference of 32 % under PVT variations, is obtained, in a 28 nm CMOS technology circuit prototype. For validation of the proposed circuit technique the IC prototype is compared with a standard ring oscillator and with traditional calibration techniques.

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3D Face Reconstruction Based on Weakly-Supervised Learning Morphable Face Model

3D Face Reconstruction Based on Weakly-Supervised Learning M...

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IEEE International Conference on Image Processing

作者： Kai-Wen Liang Pin-Hsuan Li Chung-Hsun Lo Chien-Yao Wang Yung-Fang Chen Jia-Ching Wang Pao-Chi Chang Dept. Communication Engineering National Central University Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Dept. Computer Science and Information Engineering National Central University Taiwan

In this paper, we propose a system for 3D face model reconstruction. Earlier studies on reconstruction methods included the software modeling methods or the instrument scanning modeling methods. But both of the above methods require a lot of development resources and time costs. Therefore, we develop a reconstruction system using a weakly supervised approach combining Convolutional Neural Networks (CNN) and 3D Morphable Face Models (3DMM). Given a sufficient number of 2D face images to train and learn the main features of the face, our system is capable of rapidly constructing 3D face models. The proposed method enhances the efficiency of preprocessing and improves the performance of loss function through image dept. feature extraction and regression coefficients. Using two datasets for model evaluation and analysis, this study efficiently reconstructs faces without ground-truth labels.

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A New Era in Weather Forecasting: Harnessing the Potential of Machine Learning 5

A New Era in Weather Forecasting: Harnessing the Potential o...

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5th IEEE International Conference on Computing, Power, and communication Technologies, IC2PCT 2024

作者： Bhatia, Sandeep Naib, Bharat Bhushan Goel, Neha Agarwal, Bhawna Mallikarjun, Basetty Arvindhan, M. Galgotias University School of Computing Science and Engineering Uttar Pradesh Greater Noida India Galgotias University School of Computer Science and Engineering Uttar Pradesh Greater Noida India Raj Kumar Goel Institute of Technology Department of Electronics and Communication Engineering Ghaziabad India Birla Institute of Technology Ranchi Mesra India Institute of Aeronautical Engineering Dept. of Information Technology Dundigal India Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

The use of machine learning techniques has steadily increased in improving the precision of weather forecasting. This study attempts to investigate the effectiveness of various machine learning algorithms in weather prediction by analyzing various meteorological data, including temperature, and precipitation. Machine learning offers a complementary strategy that can assist address some of the issues with traditional numerical weather prediction models. The proposed work compares the performance of several machine learning techniques by examining how well they perform about variables including evaporation, sunlight, wind speed, wind direction, cloud cover, humidity, and dataset size. Neural Networks, Naive Bayes, Logistic Regression, Random Forest, Support Vector Machines (SVM), and other approaches are actively being examined and tested. These algorithms are analyzed and evaluated by academics to establish their relative efficacy. The Naive Bayes Algorithm is a simple probabilistic algorithm used to compute the likelihood of an event occurring given the input data. Logistic Regression, on the other hand, is a modelling approach that assesses the likelihood of an event occurring based on input factors, and it is especially successful when the dependent variable is binary. According to the study, the Naive Bayes Algorithm beats other machine learning algorithms in weather condition prediction, displaying greater accuracy, precision, recall, and F1-score. The study also highlights the importance of including all relevant weather variables in the analysis to ensure accurate predictions. The results have implications for the use of machine learning techniques in weather prediction, suggesting that Naive Bayes Algorithm is an effective technique for predicting weather conditions and can improve the accuracy of weather forecasts. The study provides insightful information on using machine learning methods in the field of forecasting the weather and can inform future researc

关键词： Meteorological Data Neural Networks Prediction Accuracy Random Forest Support Vector Machines

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