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arXiv 2024年

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta53851 Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

Context: Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. Objective: This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum software Project Success Prediction Model (AQSSPM). Methodology: Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Results: Utilizing GA with NBC, project success probability improved from 53.17% to 99.68%, with cost reductions from 0.463% to 0.403%. Similarly, GA with LR increased success rates from 55.52% to 98.99%, and costs decreased from 0.496% to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs=0.955) in causes ranking, with no significant difference between them (t=1.195, p=0.240>0.05). Conclusion: The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project. © 2024, CC BY.

关键词： Genetic algorithms

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Emergency Vehicle Navigation in Connected Autonomous systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年第12期12卷 20670-20677页

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Girivel, Niveditha Subramanian Sundaram, Varsha Mani Shanmuga Khowaja, Sunder Ali Dev, Kapal Anna University NGNLab Department of Computer Technology MIT Campus Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Citicorp Services Private Ltd Software Development Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Glasnevin Campus DublinD09 V209 Ireland Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Johannesburg2006 South Africa Chitkara University Institute of Engineering and Technology Centre for Research Impact Outcome Chitkara University Punjab Rajpura140401 India

Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite Emergency Vehicle (EV) travel in the context of Connected AVs (CAVs) and ensured secured communication employing Public Key Infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV's signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and Road Side Units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV's lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an Adaptive Smart Leader Selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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Global solution to sensor network localization: A non-convex potential game approach and its distributed implementation

arXiv

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Consider a sensor network consisting of both anchor and non-anchor nodes. We address the following sensor network localization (SNL) problem: given the physical locations of anchor nodes and relative measurements among all nodes, determine the locations of all non-anchor nodes. The solution to the SNL problem is challenging due to its inherent non-convexity. In this paper, the problem takes on the form of a multi-player nonconvex potential game in which canonical duality theory is used to define a complementary dual potential function. After showing the Nash equilibrium (NE) correspondent to the SNL solution, we provide a necessary and sufficient condition for a stationary point to coincide with the NE. An algorithm is proposed_ to reach the NE and shown to have convergence rate O(1/√k). With the aim of reducing the information exchange within a network, a distributed algorithm for NE seeking is implemented and its global convergence analysis is provided. Extensive simulations show the validity and effectiveness of the proposed approach to solve the SNL problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Energy-Aware Edge-Cloud Computation Offloading for Smart Connected Health 8

Energy-Aware Edge-Cloud Computation Offloading for Smart Con...

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8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Materwala, Huned Ismail, Leila United Arab Emirates University Intelligent Distributed Computing and Systems Research Laboratory College of Information Technology Department of Computer Science and Software Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

ISBN: (纸本)9781665425742

Smart connected healthcare is an emerging technology in the context of smart cities. The connected network aims to provide efficient and effective remote patient care. In such scenarios, edge and clouds come into the picture to offload computation from sensors and mobile devices, monitoring patient's health conditions, having limited processing capabilities to the edge and/or the cloud. The problem of optimizing the energy consumption of the edge and cloud servers in this offloading scenario is crucial. However, existing research efforts focus on sensors or cloud energy optimization. They do not consider the edge and cloud as part of the offloading strategy. In this paper, we address this void by proposing a novel energy-aware offloading algorithm for smart connected healthcare, which optimizes the energy of the edge-cloud computing platform for compute-intensive applications. The experimental results show that our proposed algorithm is a promising approach to energy savings. © 2021 IEEE.

关键词： Energy efficiency

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Secure and Privacy-Preserving Lightweight Blockchain for Energy Trading 8

Secure and Privacy-Preserving Lightweight Blockchain for Ene...

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8th International Conference on Future Internet of Things and Cloud, FiCloud 2021

作者： Materwala, Huned Ismail, Leila College of Information Technology United Arab Emirates University Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering Abu Dhabi Al Ain15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi Al Ain United Arab Emirates

ISBN: (纸本)9781665425742

Renewable energy is gaining wide attention to address the issues, such as depleting resources and environmental degradation, linked with the current non-renewable energy. However, storing renewable energy is challenging. Consequently, energy trading is required to transfer the surplus amount of renewable energy from the prosumers to the consumers. The current centralized energy trading system is less secure and involves large transmission losses and high overheads. The decentralization, replication, immutability, and transparency characteristics of blockchain show a promising potential to address the inherent issues of centralized energy trading. However, the existing blockchain-based energy trading suffers from the drawbacks such as limited scalability, high energy loss, and low transaction throughput. In this paper, we propose a secure and privacy-preserving lightweight blockchain for energy trading that reduces the computational and communication overhead compared to the bitcoin network. This is by clustering the prosumers and perform the energy trading through a cluster head. Our experimental results show the potential of our architecture. © 2021 IEEE.

关键词： Scalability

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers 6

Performance and energy-aware bi-objective tasks scheduling f...

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6th Information systems International Conference, ISICO 2021

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021 The Authors. Published by Elsevier B.V.

关键词： Quality of service

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

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Procedia computer science 2022年 197卷 238-246页

作者： Huned Materwala Leila Ismail Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users’ tasks. The pervasive adoption of cloud computing in smart cities’ services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in the cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to trade off the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm.

关键词： Autonomous agents cloud computing energy-efficiency evolutionary algorithm genetic algorithm quality of service multi-objective optimization performance

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Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO systems with Low-Density Pilots

arXiv

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arXiv 2023年

作者： Zhou, Binggui Yang, Xi Ma, Shaodan Gao, Feifei Yang, Guanghua The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Department of Automation Tsinghua University Beijing100084 China The School of Intelligent Systems Science and Engineering The GBA and B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China

To reap the promising benefits of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) is required through channel estimation. However, due to the complicated wireless propagation environment and large-scale antenna arrays, precise channel estimation for massive MIMO systems is significantly challenging and costs an enormous training overhead. Considerable time-frequency resources are consumed to acquire sufficient accuracy of CSI, which thus severely degrades systems’ spectral and energy efficiencies. In this paper, we propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods under various pilot-density settings and signal-to-noise ratios. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Channel estimation

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A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO systems

arXiv

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arXiv 2023年

作者： Zhou, Binggui Yang, Xi Wang, Jintao Ma, Shaodan Gao, Feifei Yang, Guanghua The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The School of Communication and Electronic Engineering East China Normal University Shanghai200241 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Automation Tsinghua University Beijing100084 China The School of Intelligent Systems Science and Engineering The Guangdong International Cooperation Base of Science and Technology for GBA Smart Logistics Jinan University Zhuhai519070 China

Accurate channel state information (CSI) is essential for downlink precoding in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems with orthogonal frequency-division multiplexing (OFDM). However, obtaining CSI through feedback from the user equipment (UE) becomes challenging with the increasing scale of antennas and subcarriers and leads to extremely high CSI feedback overhead. Deep learning-based methods have emerged for compressing CSI but these methods generally require substantial collected samples and thus pose practical challenges. Moreover, existing deep learning methods also suffer from dramatically growing feedback overhead owing to their focus on full-dimensional CSI feedback. To address these issues, we propose a low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems. An incorporation-extrapolation scheme for eigenvector-based CSI feedback is proposed to reduce the feedback overhead. Then, to alleviate the necessity of extensive collected samples and enable few-shot CSI feedback, we further propose a knowledge-driven data augmentation (KDDA) method and an artificial intelligence-generated content (AIGC) -based data augmentation method by exploiting the domain knowledge of wireless channels and by exploiting a novel generative model, respectively. Experimental results based on the DeepMIMO dataset demonstrate that the proposed IEFSF significantly reduces CSI feedback overhead by 64 times compared with existing methods while maintaining higher feedback accuracy using only several hundred collected samples. Copyright © 2023, The Authors. All rights reserved.

关键词： Extrapolation

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MetaUE: Model-based Meta-learning for Underwater Image Enhancement

arXiv

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arXiv 2023年

作者： Zhang, Zhenwei Yan, Haorui Tang, Ke Duan, Yuping Center for Applied Mathematics Tianjin University Tianjin300072 China The Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

The challenges in recovering underwater images are the presence of diverse degradation factors and the lack of ground truth images. Although synthetic underwater image pairs can be used to overcome the problem of inadequately observing data, it may result in over-fitting and enhancement degradation. This paper proposes a model-based deep learning method for restoring clean images under various underwater scenarios, which exhibits good interpretability and generalization ability. More specifically, we build up a multi-variable convolutional neural network model to estimate the clean image, background light and transmission map, respectively. An efficient loss function is also designed to closely integrate the variables based on the underwater image model. The meta-learning strategy is used to obtain a pre-trained model on the synthetic underwater dataset, which contains different types of degradation to cover the various underwater environments. The pre-trained model is then fine-tuned on real underwater datasets to obtain a reliable underwater image enhancement model, called MetaUE. Numerical experiments demonstrate that the pre-trained model has good generalization ability, allowing it to remove the color degradation for various underwater attenuation images such as blue, green and yellow, etc. The fine-tuning makes the model able to adapt to different underwater datasets, the enhancement results of which outperform the state-of-the-art underwater image restoration methods. All our codes and data are available at https://***/Duanlab123/MetaUE. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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