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IEEE Conference on Global Communications (GLOBECOM)

作者： Xi Zhang Qixuan Zhu Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type to support communications among massive numbers of mobile users (MUs) demanding the stringent delay and error-rate bounded quality-of-services (QoS) requirements in 6G. Correspondingly, the integrated sensing and communications (ISAC) technique can improve various QoS performances through spectrum/hardware sharing between target-sensing and data-communications. In addition, the massive multiple-input multiple-output (massive MIMO) and finite blocklength coding (FBC) have been also widely recognized as the powerful 6G techniques to efficiently support mURLLC. However, how to apply the ISAC technique to accurately sense and estimate the wireless channel state while transmitting the information from the base station to MUs to support mURLLC using massive MIMO and FBC has imposed many new difficulties not encountered before. To conquer these challenges, in this paper we propose the new ISAC performance metrics for statistical-QoS guaranteed mURLLC over 6G massive MIMO mobile networks using FBC. First, we establish system models for ISAC-based architectures. Second, we detect targeted MUs and estimate the channel state using the radar sensing echo. Third, we develop a new ISAC performance metric as a function of the data-communication rate and channel state estimation distortion. Finally, we use the numerical analyses to validate and evaluate our developed ISAC performances in supporting mURLLC-oriented QoS requirements.

关键词： 6G mobile communication Measurement Wireless sensor networks Wireless networks Channel estimation Quality of service Massive MIMO Integrated sensing and communication Distortion Testing

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Federated Learning Based Integrated Sensing, Communications, and Powering Over 6G Massive-MIMO Mobile Networks

Federated Learning Based Integrated Sensing, Communications,...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

To sense mobile users' positions and channel states while simultaneously conveying information for supporting the 6G traffic, the integrated sensing and communication (ISAC) has been proposed as a key technique for sensing and communicating using the same radio frequency and hardware. Moreover, to sup-port energy-constrained and battery-limited wireless networks, simultaneous wireless information and power transfer (SWIPT) has also emerged to simultaneously deliver information and energy to a receiver. To integrate these two techniques, integrated sensing, communications, and powering (ISACP) has been developed to simultaneously sense the targeted mobile users, transmit the information, and deliver the power. However, how to efficiently optimize the power allocation in ISACP schemes to support the 6G traffic has imposed many new challenges not encountered before. To overcome these challenges, in this paper we propose a federated-learning (FL) enabled ISACP scheme to maximize the energy-efficiency over 6G massive MIMO wireless networks. First, we establish the system models for our proposed ISACP and FL schemes. Second, we design the training data set collection in the FL algorithm by sending the pilot signal from mobile users to the massive MIMO base station, and formulate an energy-efficiency maximization problem for the developed ISACP scheme. Third, To solve the formulated energy-efficiency maxi-mization problem, we propose an actorcritic enabled multi-agent based FL mechanism. Finally, we use the numerical analyses to validate and evaluate our developed schemes.

关键词： 6G mobile communication Wireless sensor networks Federated learning Wireless networks Training data Massive MIMO Integrated sensing and communication

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman 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 Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad 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

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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Integrated Sensing, Communications, and Powering for Statistical-QoS Provisioning Over Next-Generation Massive-MIMO Mobile Networks

Integrated Sensing, Communications, and Powering for Statist...

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Annual Conference on Information Sciences and systems (CISS)

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

The integrated sensing and communication (ISAC) has been proposed as a key technique to support the next generation traffic with stringent quality-of-service (QoS) requirements through sensing and communicating using the same radio frequency and hardware. Simultaneous wireless information and power transfer (SWIPT) has also emerged to simultaneously deliver information and energy to a receiver. However, how to integrate ISAC with SWIPT to support the stringent QoS traffic has imposed many new challenges not encountered before. To overcome these challenges, in this paper we propose an integrated sensing, communications, and powering (ISACP) technique for supporting both sensing and communication QoS provisioning while delivering the power over the next-generation wireless networks using massive multiple-input and multiple-output (massive MIMO) communications. First, we develop the system models for the ISACP scheme to simultaneously sense the targeted mobile users (MUs), transmit the information, and deliver the power. Second, we propose a hypothesis testing based scheme to estimate the MU’s angle-of-arrival using the sensing signal. Third, we employ the Cramér-Rao bound (CRB) to measure the performance of sensing and maximize the energy-efficiency with satisfying requirements of both sensing and communication performances. Finally, we use numerical analyses to validate and evaluate our proposed ISACP scheme.

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Hierarchical Caching for Digital Twins Communications Over mURLLC-Based 6G Distributed-Computing Mobile Networks Using FBC

Hierarchical Caching for Digital Twins Communications Over m...

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International Conference on Distributed Computing systems Workshop

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

ISBN: (数字)9798350354713

ISBN: (纸本)9798350354720

The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type to support massive numbers of mobile users (MUs) demanding the stringent delay and error-rate bounded quality-of-services (QoS) requirements over 6G. Among multiple 6G mURLLC services, digital twins (DT) has been widely envisioned as a major intelligent application to support efficient interactions between physical and virtual objects. Moreover, multi-tier caching, which is one of the key distributed computing techniques, stores the frequently-demanded data items at different wireless network tiers to efficiently reduce mURLLC streaming delay and data move. However, how to efficiently cache mURLLC-based DT data items at different caching tiers of wireless networks and how to statistically upper-bound both delay and error-rate for DT communications remain challenging problems. To overcome these difficulties, in this paper we propose a multi-tier caching mechanism to support DT communications over 6 G mobile networks. First, we propose the DT data adaptive collection scheme applying finite blocklength coding (FBC) to dynamically encode a physical object into its virtual representation according to the current network and wireless channel statuses. Second, we develop inter-tier and intra-tier collaborative caching mechanisms, where DT data items are selectively cached at different wireless network caching tiers according to their popularities including: router tier, massive-multiple-input-multiple-output (MIMO) basestation tier, and mobile device tier. Third, our proposed intertier collaborative caching mechanisms maximize the aggregate $\epsilon$ effective capacity across all three caching tiers, and our proposed intra-tier collaborative caching mechanisms minimize the sum of data transmission delay for all DT data items cached in each caching tier. Finally, we numerically validate and evaluate our developed multi-tier hierarchical caching schemes over 6G DTe

关键词： 6G mobile communication Wireless networks Aggregates Collaboration Distributed databases Quality of service Encoding

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering 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

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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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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Twitter Sentimental Analysis using Machine Learning Approaches for SemeVal Dataset 1

Twitter Sentimental Analysis using Machine Learning Approach...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Imran, Azhar Fahim, Marwa Alzahrani, Abdulkareem Fahim, Safa Alheeti, Khattab M. Ali Rehman, Saif Ur Air University Dept. of Creative Technologies Islamabad Pakistan Al Baha University Faculty of Computer Science and IT Albaha Saudi Arabia University of Anbar Computer Networking Systems Dept. Ramadi Iraq University Institute of I.T. Arid Agriculture University Rawalpindi Pakistan

ISBN: (纸本)9781665472753

In recent years, Sentimental Analysis has enticed many researchers in this field. Due to the lack of suitable datasets, many scientists and researchers faced hindrances in their research. We used Semeval dataset because it's the authentic dataset for computational sentimental Analysis. When it comes to natural catastrophes, political turmoil, and terrorism, social scientists and psychologists are interested in learning how individuals express their feelings and opinions. In this paper, we present the approach to the SemEva1-2017 dataset. As we know, with the advancement of technology, social media have established strong worldwide connectivity and information sharing. The wide use of social media and media-networking sites produced an unprecedented amount of data. Sharing information using these websites has become very common. To detect the triggering factors has become necessary to understand the behavioural and emotional state to avoid anti-social behaviour and extreme or impulsive responses. We reveal to identify the emotional textual data using different strategies. Classification of the Tweets according to the Sentimental Analysis has an important role in the social, economic, and political world s. The effective strategy for tackling and coping with it is to use computational techniques to identify the speech type. For feature extraction, we use a variety of machine learning classifiers. It's crucial to detect related features in a text correctly. As a result, using and improving NLP approaches can aid in improved understanding and analysis of data. Briefly, we use an unsupervised. TF-IDF for the Feature Extraction to train the word Embedding Techniques that are tuned into transform training data and transformed Test data. The model is finally initialized using vectorization on Twitter sentiment analysis to train the latter. Then, transformed the model to create the transformed dataset. The major findings and outcomes of SemEva1-2017 on Identifying and Catego

关键词： Embeddings

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Dramatic Increase in Fear-Related Discussion on Twitter during COVID-19: Analysis, Topic Modeling and Tweets Classification

Dramatic Increase in Fear-Related Discussion on Twitter duri...

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2022 International Conference on Electrical Engineering and Sustainable Technologies, ICEEST 2022

作者： Karam, Muhammad Waleed Imran, Azhar Alzahrani, Abdulkareem Ali Alheeti, Khattab M. Abbas, Ahsan Najem Al-Aloosy, Abdulkareem A. Air University Dept. of Creative Technology Islamabad Pakistan Al Baha University Faculty of Computer Science and IT Albaha Saudi Arabia University of Anbar Ramadi Computer Networking Systems Dept. Iraq

ISBN: (纸本)9781665462099

COVID-19 is one the most lethal virus, causing millions of death to date. It was initially detected in Wuhan, China. It then spread rapidly around the globe, which resultantly created major setbacks in the public health sector. The reason of millions of deaths is not only due to the virus itself but it is also linked to peoples' mental state, and sentiments triggered by the fear of the virus. These sentiments are predominantly available on posts/tweets on social media. This paper presents a novel approach for exploratory data analysis of twitter to understand the emotions of general public;country wise, and user wise. Firstly K-Means clustering is employed for topic modeling to categorize the emotions in each tweet. Further supervised machine learning techniques are used to categorize the multi-label tweets. This research concluded that Fear was the most common emotion in twitter discussion. Furthermore, we classified the dataset by performing decision tree (DT), logistic regression (LR), and support vector machine (SVM), finally this paper concluded the results of classification, which shows that SVM can attain better classification accuracy (99%) for COVID-19 text classification. © 2022 IEEE.

关键词： COVID-19

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Multi-Agent Moth-Flame Reinforcement Learning based Broadcast Beam Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Huang, Shan Yao, Haipeng Mai, Tianle Wu, Di Xu, Jiaqi Yu, F. Richard Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Carleton University Depart. Systems Computer Engineering Ottawa Canada

Currently, beamforming antenna array technologies are of utmost importance in 5G communication systems. These technologies are essential for optimizing the coverage and signal quality of the cellular network. However, the optimization of broadcast beams presents significant challenges due to the complex strategy profile space. Each beam can be configured with different widths and heights, making it difficult for conventional algorithms to handle. To address this issue, we propose a novel approach called Multi-Agent Moth-Flame Reinforcement Learning (MAMF-RL) algorithm for broadcast beam optimization. MAMF-RL combines reinforcement learning and moth-flame optimization algorithms to interactively search for the optimal broadcast beams. By decomposing the problem into multiple single-sector antenna configuration problems, MAMF-RL effectively reduces the algorithm complexity. We conducted experiments utilizing real data in an 18-sector wireless coverage area. To evaluate the performance of our proposed method, we compared it with traditional methods such as the particle swarm algorithm. The results demonstrate that our MAMF-RL model achieves an average coverage rate of 1.82% higher and a 13.74% lower overlapping coverage rate compared to traditional methods. © 2025 IEEE. All rights reserved.

关键词： 5G mobile communication systems

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