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Joint Optimization for Cooperative Service-Caching, Computation-Offloading, and Resource-Allocations Over EH/MEC 6G Ultra-Dense Mobile Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Chen, Zhian Wang, Fei Zhang, Xi Southwest University College of Electronic and Information Engineering Chongqing400715 China Texas A&M University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

Service-caching, computation-offloading, and mobile edge-computing (MEC) have been widely recognized as three key 6G wireless technologies which can efficiently support implementing the ultra-dense networks (UDNs) with massive small-cell base stations (SBSs). But, these impose the new challenges for the UDNs to solely rely on grid power for energy supplying and to jointly optimize service-caching, computation-offloading, and resource-allocations. To overcome the above described difficulties, integrating energy-harvesting (EH) techniques with MEC-enabled 6G UDNs, we propose to develop the joint optimization schemes for cooperative service-caching, computation-offloading, and resource-allocations. In our considered UDNs, there exist a large number of EH-based stationary users (SUs) or mobile users (MUs), and a mixture of on-grid SBSs powered by electric grid and off-grid SBSs power-supplied by solar, radio frequency (RF) energy, etc. Specifically, first we formulate an energy minimization problem under a non-linear RF-energy EH model to minimize the sum of weighted energy consumption of users and off-grid SBSs. Second, for scenarios with SUs, we develop a two-timescale based joint cooperative service-caching, computation-offloading, and resource-allocations scheme using the hierarchical multi-agent deep reinforcement learning. We derive cooperative service-caching in each time frame, and then derive computation-offloading and resource-allocations in each time slot. Third, we extend our work to scenarios with MUs, where MUs can move with certain trajectories at low speeds. Finally, we validate and evaluate the performances of our proposed schemes through the extensive simulations. © 2002-2012 IEEE.

关键词： Energy utilization

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An Intrusion Detection System for IoT Using KNN and Decision-Tree Based Classification

An Intrusion Detection System for IoT Using KNN and Decision...

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2021 International Conference of Modern Trends in Information and Communication Technology Industry, MTICTI 2021

作者： Abdaljabar, Zainab Hussam Ucan, Osman Nuri Ali Alheeti, Khattab M. University of Altinbaş Institute of Graduate Studies Department of Electrical and Computer Engineering Turkey University of Anbar College of Computer Science and Information Technology Department of Computer Networking Systems Iraq

ISBN: (纸本)9781665414449

The Internet of Things (IoT) has grown rapidly in recent years, intending to affect everything from everyday life to large industrial systems. Regrettably, this has attracted the attention of hackers, who have turned the Internet of Things into a target for malicious activity, exposing end nodes to attack. IoT devices' sheer volume and diversity make protecting the IoT infrastructure with a traditional intrusion detection system difficult. So to protect IoT devices, the data flow was investigated in an IoT context to protect these devices from hackers. We used two machine learning classifiers in this work: KNN (K-Nearest Neighbors) and DT (Decision Tree). We calculated the Error Rate, Accuracy, Precision, Recall, and F1 score for each method. When we combined these two classifiers, we obtained outstanding results (100 %). We have a high rate of detection of attacks. The findings are summarized. © 2021 IEEE.

关键词： Decision trees

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A Highly Scalable Instruction Scheduler Design based on CPU Stall Elimination

A Highly Scalable Instruction Scheduler Design based on CPU ...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Sari, Alparslan Butun, Ismail University of Delaware Computer Systems and Networking Department of Electrical and Computer Engineering NewarkDE19716 United States Chalmers University of Technology Network and Systems Division Department of Computer Science and Engineering Gothenburg412 96 Sweden

ISBN: (纸本)9781665404174

In this paper, by targeting low-level code optimization, an instruction scheduler is designed and experimented with a synergistic processor unit (SPU) to show its effectiveness on a basic block and data dependency graph (DDG) called compiler instruction scheduler (CIS). In our methodology, a source C/C++ file is converted to an assembly file via spu-gcc to detect stalls in basic code blocks and CIS generates the DDG of executable code to eliminate stalls to find optimization opportunities and increase the program performance. The CIS simply shuffles the instruction sequences of the assembly code to eliminate CPU stalls in a given basic instruction block. Random and sliding window schedulers are implemented to generate a new assembly code sequence based on DDG and a basic block in parallel. Finally, this paper describes how CIS finds the optimized code sequence for a given file without any conflicts and hazards. Compared to the original code compilation process, we have shown that CIS improves the code execution metrics, and also our evaluated speedup results are found to be promising. © 2021 IEEE.

关键词： Scheduling

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Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-Based 6G Mobile Wireless Networks 57

Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-B...

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

作者： Wang, Fei Zhang, Xi Southwest University Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

ISBN: (纸本)9781665451819

We propose the novel scheme to solve a multi-objective optimization problem over an unmanned aerial vehicle (UAV) communications system to jointly minimize the energy consumption of the UAV and ground users (GUs). In particular, the UAV communicates with multiple GUs using active intelligent reflecting surfaces (IRSs), which can actively amplify and thus significantly enhance the strengths of reflected signals. We develop an energy minimization scheme based on the multi-objective hierarchical deep reinforcement learning (DRL), by decomposing the formulated optimization problem into two-layered subproblems. By solving the upper-level subproblem, we derive the optimal UAV trajectory and GUs scheduling strategies to minimize the UAV's energy consumption. By solving the lower-level subproblem, we obtain the IRS's phase shifts and amplification factors and GUs' transmit/receive beamforming to minimize the GUs' energy consumption. Finally, we validate and evaluate the proposed schemes through simulations, which show that the UAV's and GUs' energy consumption can be significantly reduced by using the active IRS, when the thermal noise powers at the IRS are much smaller than those at the UAV and GUs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Enhancing Road Safety with Real-Time Helmet Detection and E-Challan Issuance using YOLO and OCR 1

Enhancing Road Safety with Real-Time Helmet Detection and E-...

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Deepa, S. Vinoth Kumar, C.N.S. Duraivelu, Vivek Easwaran, Balamurugan Suguna, R. Aparna, H. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Srm Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Tamilnadu Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur502313 India Texila American University School of It Zambia Bannari Amman Institute of Technology Department of Computer Science and Business Systems Tamilnadu Sathyamangalam India Kongu Engineering College Perundurai Tamilnadu Erode638 060 India

ISBN: (纸本)9798350372748

This study proposes an integrated system combining Optical Character Recognition (OCR) and YOLO (You Only Look Once) for enforcing helmet laws and improving traffic safety. A user-friendly PyQT GUI facilitates vehicle registration, creating an organized CSV database for future reference. YOLOv5, an advanced object detection algorithm, identifies key elements like motorcycles, helmets, and number plates from input images. The primary objective is detecting motorcyclists without helmets, followed by OCR extraction of text from license plates. The extracted text is compared with registered bike information in the CSV file, generating e-challans and fines for matches. Automatic email notifications ensure timely communication and compliance with traffic regulations. The system's implementation not only aids helmet enforcement but also acts as a deterrent for future violations, fostering a safer driving environment. With scalability and practicality, this approach can integrate seamlessly into existing traffic control systems, leveraging AI technologies like YOLOv5 and OCR for effective problem-solving and enhanced road safety. © 2024 IEEE.

关键词： Object detection

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Detecting Malicious Behaviour for SANET Based on Artificial Intelligence Algorithms

Detecting Malicious Behaviour for SANET Based on Artificial ...

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2021 International Conference on Communication and Information Technology, ICICT 2021

作者： Al-Janabi, Mustafa I. H. Alheeti, Khattab M. A. Alaloosy, Abd Al-Kareem A. University of Anbar College of Computer Sciences and Information Technology Department of Computer Science Iraq University ty of Anbar College of Computer Sciences and Information Technology Department of Computer Networking Systems Ramadi Iraq

ISBN: (纸本)9781665439145

Attack detection is important for wireless networks and communications generally. Ship ad hoc networks (SANET) are a subset of wireless networks that are vulnerable to denial-of-service attacks. These attacks are one of the main challenges facing maritime networks, specially dedicated networks because of their weak infrastructure, which makes it easier for these networks to be exposed to this type of attack. To maintain a secure connection and increase the durability of that connection, an accurate attack detection system must be built. In this paper, we used machine learning algorithms to classify data as either attack or safe. we generated the dataset by building a scenario for the SANET in the network simulator (ns-2). Ad hoc On-demand Distance Vector (AODV) was used as the routing protocol in this simulation, AODV reduces the burden on the network compared with the other protocols (reduces messages flooding in the network). Three machine learning algorithms which are Logistic Regression, Stochastic Gradient Descent and K-Nearest Neighbors were applied to the dataset and compared with each other in terms of precision in detection, the results show that the machine learning algorithms have the ability to detect attacks with higher performance. The experimental results showed that the data set that was generated with Knn as the base classifier produced the best performance in terms of classification precision by 89%. © 2021 IEEE.

关键词： Classification (of information)

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Detection of Autism Spectrum Disorder in Children Using Efficient Pre-Processing and Machine Learning Techniques

Detection of Autism Spectrum Disorder in Children Using Effi...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Anas D. Sallibi Khattab M. Ali Alheeti Dept. dept. of Computer Sciences College of Computer and Information Technology (University of Anbar) Anbar Iraq Dept. of Computer Networking Systems College of Computer and Information Technology (University of Anbar) Anbar Iraq

Early diagnosis of the brain condition known as autism spectrum disorder (ASD) could significantly reduce the high healthcare costs typically involved with treating ASD. Autism's fiscal toll emphasizes the importance of developing easy and effective detection instruments. Therefore, a quick ASD evaluation is essential to helping healthcare professionals and letting people know if they should seek a formal clinical diagnosis. Currently, there are very few autism datasets related to screening accessible, most of which are based on genetics. To expose critical information about autism screening, this work suggests applying a novel machine learning framework that contains vital characteristics and performing predictive analysis utilising a Support vector machine, logistic regression, K-nearest neighbour, and random forest. The main objective of this study is to develop a system for the early detection of children at risk for autism spectrum disorders (ASD).

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Designing a Distributed Multi-agent System for Compiler Optimization

Designing a Distributed Multi-agent System for Compiler Opti...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Sari, Alparslan Sahin, Cagri Butun, Ismail University of Delaware Electrical and Computer Engineering Computer Systems and Networking NewarkDE United States Gazi University Computer Engineering Software Research Group Ankara Turkey Chalmers University of Technology Computer Science and Engineering Network and Systems Division Gothenburg Sweden

ISBN: (纸本)9781665404174

This paper explores the run time performance improvements using different GCC optimization flags in program compilation. As multi-core microprocessor systems replacing legacy single-core ones, tremendous effort is needed to address to optimize the associated compilers for newly designed architectures in order to suit them for running parallel programming on multiple cores. Therefore, the aim of this paper is to address this challenge by designing an optimum distributed multi-agent system to perform compiler optimization. A multi-agent framework is adopted to utilize random and genetic algorithm-based search algorithm to find the best GCC optimization flags for a given program. The framework is highly scalable and can be extended with distributed system concept to perform code compilation in parallel to find the best-optimized code sequence in a short amount of time. The initial performance results have promising indicators which clearly show that the performance improvement is achieved. © 2021 IEEE.

关键词： Multi agent systems

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Intelligent Object Recognition System for Autonomous and Semi-Autonomous Vehicles

Intelligent Object Recognition System for Autonomous and Sem...

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2021 International Conference on Communication and Information Technology, ICICT 2021

作者： Alfahdawi, Mohammed G. Alheeti, Khattab M A. Al-Rawi, Salah S. College of Computer Sciences and Information Technology University of Anbar Department of Computer Science Anbar Iraq College of Computer Sciences and Information Technology University of Anbar Department of Computer Networking Systems Ramadi Iraq

ISBN: (纸本)9781665439145

Road accidents have long been a significant issue involving loss of life and property. So recent years have seen rapid developments in autonomous and semi-autonomous vehicles. Autonomous vehicles are a comprehensive solution built for safety and comfort on the roads. This solution has many challenges. One of these challenges is to spot and recognize obstacles. As humans do, the only way to discover and recognize these obstacles is to see them. Therefore, vision systems are an essential part of this type of vehicle. This paper proposed a vision-based approach for autonomous vehicles to recognize objects and traffic lights on the road. The proposed system contains three phases: image pre-processing, feature extraction, and classification. In the first phase, some image pre-processing techniques are applied, which consists of three stages: Convert Color images to Grayscale, Histogram Equalization, and Resize Image. The second phase is the extraction of features from images using Principal Component Analysis (PCA). In the third phase, the features that are extracted are used as input to machine learning (ML) classifiers for object recognition. ML algorithms used in this proposed system are Random Forest (RF), Random Tree (RT), and Naive Bayes (NB). The results show that the RF algorithm has the highest classification precision rate, 75%, compared to the NB algorithm 63% and the RT algorithm 53%. © 2021 IEEE.

关键词： computer vision

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IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G THz Mobile Wireless Networks

IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G ...

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2023 IEEE International Conference on Communications, ICC 2023

作者： Wang, Fei Zhang, Xi Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&m University College StationTX77843 United States

ISBN: (纸本)9781538674628

Intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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