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Strong Co-Location Pattern Mining Incorporating Multi-Path and Distance Decay Effects 7

Strong Co-Location Pattern Mining Incorporating Multi-Path a...

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7th international conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： He, Yonggui Yang, Peizhong Wang, Lizhen Chen, Hongmei Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Kunming China

ISBN: (纸本)9798331529314

Spatial co-location pattern mining (SCPM) is a sub-field of data mining, which aims to discover the subset of spatial features whose instances are frequently located in proximate areas. SCPM has broad prospects in many applications, such as ecology, public health, smart cities, etc. In recent years, improving and applying SCPM technology with the constraints of road networks has emerged as a prominent research focus. However, existing studies solely focus on the shortest path between instances when assessing the proximity relationships, neglecting other proximity paths, and thus may overlook co-location patterns with strong associations. To address this issue, this paper introduces a novel metric called Strong Proximity Score, which integrates both multi-path proximity and distance decay effects to measure the strength of proximity relationships. Additionally, the Minimum First Search (MFS) algorithm is presented, which utilizes the strategy of minimum instance pair search to accelerate the calculation of Strong Proximity Score. Extensive experiments conducted on real datasets of points of interest demonstrate the superiority of the method based on Strong Proximity Score over traditional SCPM methods and confirm the efficiency of MFS algorithm. © 2024 ieee.

关键词： Smart city

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A Coherent 3-D Model for Satellite-Based VHF Communications

A Coherent 3-D Model for Satellite-Based VHF Communications

引用

2024 ieee conference on Antenna Measurements and applications, CAMA 2024

作者： Tran, Van-Su Nguyen, Huy Nguyen, Minh N. T. School of Electrical Engineering International University Vietnam National University HCMC Ho Chi Minh City Viet Nam John Von Neumann Institute University of Science Vietnam National University HCMC Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350386929

Non-terrestrial communications such as satellite communications will be affected to build a wide-ranging world and enable novel applications in a cost-effective system. The radiocommunication inclusion is likely to expand from only 'population' 2D on the ground to the 'global and space' 3D coverage. The integration of non-terrestrial and terrestrial communications systems will attain 3D coverage of the Earth. They will not only deliver communications with broadband and wide-range services around the world, but also provide new roles such as precision enhanced positioning and navigation as well as real-time earth observation. Certain innovative usage circumstances and applications need to be discovered and a few practical encounters need to be lectured. In this paper, a coherent 3-D model of full-wave scattering by a complex area is used to produce the data essential to construct SAR images of the given field. © 2024 ieee.

关键词： Satellite communication systems

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Machine Learning to Predict Risk Management applications Performance 5

Machine Learning to Predict Risk Management Applications Per...

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5th international conference on Autonomic computing and Self-Organizing systems

作者： De Giorgi, Laura Ardagna, Danilo Politecn Milan Dept Elect Informat & Bioengn Milan Italy Tinvent Srl Venice Italy

ISBN: (纸本)9798350389777;9798350389760

Machine Learning is increasingly crucial for predicting application performance, offering a black-box approach that does not require a deep understanding of the application internal workings. This method enables accurate predictions without delving into complex system models. Our study utilized ML to forecast the execution time of an industrial application dealing with risk measures as part of the Solvency II regulations for insurance companies. By conducting a comparative analysis of multiple models, XGBoost was identified as the most effective, achieving a Mean Absolute Percentage Error of 18%. The results demonstrated robust accuracy for intermediate durations, though limitations were observed for shorter and significantly longer times due to data scarcity. Overall, this study highlights the significant potential of ML in improving prediction accuracy for complex industrial applications, offering valuable insights for resource allocation and performance management.

关键词： Performance Machine Learning XGBoost

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Constructing Future Vehicle Digital Twins with Insights from Cloud-Edge Collaboration 21

Constructing Future Vehicle Digital Twins with Insights from...

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21st ieee international Symposium on Parallel and Distributed Processing with applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Shi, Xin Zhao, Liang Lin, Na Xu, Lexi Hawbani, Ammar Bi, Yuanguo Shenyang Aerospace University Shenyang China China Unicom Beijing China Northeastern University Shenyang China

ISBN: (纸本)9798350329223

Intelligent Transportation System (ITS) is embarking on a new development road, in which Digital Twins (DT) provides crucial support for improving the smartness standard of vehicles and realizing a high-level of traffic management in this process. However, the existing solutions face challenges such as limited computing resources, data scarcity and data distribution, which hinder the application prospects of DT for ITS. First, we propose the concept of future-oriented DT aim to address these issues. Specifically, lightweight local DTs are deployed at the different edge servers to provide services for vehicles, and a strong performance global DT is deployed in the cloud server to manage and optimize the local DTs. Then, we introduce a collaborative learning method between two models to construct the DT network architecture and realize the vision of the future-oriented. This method makes use of rich global knowledge and local data, so that the model can dexterously integrate personalization and generalization capabilities. Finally, We use an Incremental Learning algorithm to improve the ability of the model that adapt to new scenarios and changing input data. All experiments are based on real traffic data, and trajectory prediction task as application carrier. Experimental results demonstrate the effectiveness of the proposed method, and the stability and performance of DTs have achieved excellent performance in the construction process. © 2023 ieee.

关键词： Learning systems

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Machine Learning and Artificial Intelligence Algorithms that Minimize Power Consumption in Internet of Things Gadgets

Machine Learning and Artificial Intelligence Algorithms that...

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2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Berin Shalu, S. Vergin Raja Sarobin, M. VIT Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319200

The exponential growth of IoT gadgets has enabled previously unimaginable levels of connectedness and convenience, but it has also introduced a significant new difficulty: battery life. In this study, we investigate how artificial intelligence (AI) and machine learning may be used to cut down on power usage in Internet of Things (IoT) devices. With an emphasis on data-driven insights and fictitious data situations, we provide a systematic technique for data collecting, algorithm creation, and rigorous testing. Our results show that by combining Q-learning with supervised learning and statistical analysis, considerable gains in energy efficiency may be achieved. There is strong statistical evidence that data transmission operations may reduce their energy footprint by 25%. In addition to the technical details, we explore the practical ramifications of our study, such as increased battery life, reduced environmental impact, and reduced expenses. Insights into the revolutionary potential of machine learning and AI in transforming the IoT landscape towards a more sustainable and user-centric future are provided in this research paper, which may be used as a practical guide by industry experts, academics, and policymakers. We also provide an overview of promising future avenues for improving IoT power management and scalability, such as the use of sophisticated reinforcement learning models, the incorporation of energy harvesting, and real-time adaption. © 2023 ieee.

关键词： Artificial Intelligence (AI) Battery Life Extension Internet of Things (IoT) Machine Learning Power Consumption Q-learning real-world applications Reinforcement Learning Models Scalability Statistical Analysis Supervised Learning

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A Comprehensive Analysis of optimization of task scheduling algorithms for IOT applications in a Combined Environment of Cloud, Fog and Edge 3

A Comprehensive Analysis of optimization of task scheduling ...

引用

3rd international conference on Smart Technologies and systems for Next Generation computing, ICSTSN 2024

作者： Sarumathi, S. Vijayalakshmi, K. Ramco Institute of Technology Department of CSE Rajapalayam India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

Optimising the use of resources in data centres requires effective task scheduling on the cloud. As 5G and IoT applications proliferated and produced a vast amount of jobs with strict latency requirements, it became even more difficult. The mid-layer(fog), a layer that complements cloud computing, is born out of this. Because network processing is done closer to end devices and users in fog computing, task latency can be decreased. However, because there aren't enough resources available, not all the jobs can be scheduled in the fog. Better quality of service can only be attained by using specifically created and fine-tuned scheduling algorithms, as conventional scheduling algorithms frequently fall short in utilising the diverse resources. This research delves into a wide range of aspects about the most recent work scheduling algorithms used in cloud and fog environments. In this study, we are going to analyse various scheduling algothims based on optimization methods Constraints such as energy efficiency, time efficiency and cost efficiency based scheduling. In contrast to a packages of jobs and resources, a larger percentage of tasks are autonomous, and the majority of scheduling algorithms have been found to be dynamic and non- preemptive in nature. From this study various scheduling objectives are derived based on Quality metrics such as makespan, resource utilization, delay, load balancing, and energy consumption. © 2024 ieee.

关键词： Scheduling algorithms

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time-Triggered Inference on FPGAs 29

Time-Triggered Inference on FPGAs

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29th ieee international conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Bebawy, Yosab Schmidt, Michael-Christian Ahmadian, Hamidreza Ezekiel, Aniebiet Micheal Obermaisser, Roman University of Siegen Embedded Systems Faculty Iv Germany Eskitec GmbH Germany

ISBN: (纸本)9798350361230

Safety-critical AI systems demand the utmost reliability to prevent catastrophic outcomes. However, conventional AI hardware accelerators, such as the Versatile Tensor Accelerator (VTA), suffer from limitations in predictability and reliability due to inherent variability in event-driven task execution and the lack of precise timing control. These limitations make them unsuitable for safety-critical applications where erroneous or untimely operations could lead to catastrophic consequences. This paper proposes a novel time-triggered VTA (TT-VTA) architecture specifically designed to address the shortcomings of conventional VTAs and enhance safety and reliability in safety-critical AI systems. The TT-VTA architecture utilizes pattern-based timing schedules which are generated by a software simulator that integrates DRAMSim2 to simulate memory-related instructions as well as by a cycle-accurate simulator to provide accurate cycle counts for non-memory-related instructions. A comparative evaluation using a ResNet18 classification model demonstrates that the TT-VTA achieves identical classification accuracy while maintaining deterministic resource utilization and enabling precise timing control. Our proposed TT-VTA architecture demonstrates significant promise for enhancing the safety and reliability of AI systems in safety-critical applications. © 2024 ieee.

关键词： Tensors

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Clustering-based Cooperative Computation Offloading Game for dependent tasks in Industrial Internet of Things systems

Clustering-based Cooperative Computation Offloading Game for...

引用

ieee international conference on Communications (ieee ICC)

作者： Chouikhi, Samira Esseghir, Moez Merghem-Boulahia, Leila Univ Technol Troyes Comp Sci & Digital Soc LIST3N Lab Troyes France

ISBN: (纸本)9781538674628

With the expansion of connected devices used for industrial purposes, the Industrial Internet of Things (IIoT) has emerged as a specific branch of the Internet of Things (IoT) for Industry 4.0. Its applications in industrial domains include monitoring, smart manufacturing, and virtual and augmented reality. However, the huge amount of data generated by IIoT devices with limited computing resources makes it challenging to guarantee the different required quality of service (QoS) of the applications while minimizing the computation cost in terms of energy consumption. especially in terms of latency. In this paper, we opt for the offloading of dependent computation-intensive tasks to edge and cloud servers with more powerful computation capacities. Our proposed model aims to minimize the energy consumption of each IIoT device while respecting the maximal tolerant deadline of task completion. We propose to cluster the IIoT devices that have dependent tasks together to better handle this dependency. Moreover, we propose a distributed cooperative game that allows each device to decide whether it is beneficial for it and for its cluster, in terms of task completion and energy consumption, to offload its task or execute it locally. We prove that the Nash Equilibrium exists by proving that our game is a weighted potential game. Finally, we propose a practical distributed offloading algorithm to implement the cooperative game. The performance evaluation results show that the proposal optimizes energy consumption whilst increasing the number of tasks completed on time.

关键词： Computation offloading edge computing industrial Internet of Things game theory dependent computation tasks cooperative offloading game

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Polynesia: Enabling High-Performance and Energy-Efficient Hybrid Transactional/Analytical Databases with Hardware/Software Co-Design 38

Polynesia: Enabling High-Performance and Energy-Efficient Hy...

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38th ieee international conference on Data Engineering (ICDE)

作者： Boroumand, Amirali Ghose, Saugata Oliveira, Geraldo F. Mutlu, Onur Google Mountain View CA 94043 USA Univ Illinois Urbana IL USA Swiss Fed Inst Technol Zurich Switzerland

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

A growth in data volume, combined with increasing demand for real-time analysis (using the most recent data), has resulted in the emergence of database systems that concurrently support transactions and data analytics. These hybrid transactional and analytical processing (HTAP) database systems can support real-time data analysis without the high costs of synchronizing across separate single-purpose databases. Unfortunately, for many applications that perform a high rate of data updates, state-of-the-art HTAP systems incur significant losses in transactional (up to 74.6%) and/or analytical (up to 49.8%) throughput compared to performing only transactional or only analytical queries in isolation, due to (1) data movement between the CPU and memory, (2) data update propagation from transactional to analytical workloads, and (3) the cost to maintain a consistent view of data across the system. We propose Polynesia, a hardware-software co-designed system for in-memory HTAP databases that avoids the large throughput losses of traditional HTAP systems. Polynesia (1) divides the HTAP system into transactional and analytical processing islands, (2) implements new custom hardware that unlocks software optimizations to reduce the costs of update propagation and consistency, and (3) exploits processing-in-memory for the analytical islands to alleviate data movement overheads. Our evaluation shows that Polynesia outperforms three state-of-the-art HTAP systems, with average transactional/analytical throughput improvements of 1.7x/3.7x, and reduces energy consumption by 48% over the prior lowest-energy HTAP system.

关键词： Costs Data analysis conferences Throughput Propagation losses real-time systems Database systems

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real-time Anomaly Detection Using Distributed Tracing in Microservice Cloud applications 12

Real-Time Anomaly Detection Using Distributed Tracing in Mic...

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12th international conference on Cloud Networking (CloudNet)

作者： Raeiszadeh, Mahsa Ebrahimzadeh, Amin Saleem, Ahsan Glitho, Roch H. Eker, Johan Mini, Raquel A. F. Concordia Univ CIISE Montreal PQ Canada Ericsson Res Lund Sweden Univ Western Cape Comp Sci Programme Cape Town South Africa

ISBN: (纸本)9798350313062;9798350313079

Distributed tracing plays a vital role in microservice infrastructure, and learning-based trace analysis has been utilized to detect anomalies within such systems. However, existing approaches for learning-based trace-based anomaly detection face certain limitations. Some assume that trace patterns can be learned solely from normal executions, while others depend on anomaly injection to generate labeled traces categorized as normal or anomalous. However, in practical scenarios, anomalies may also happen during the normal execution. Moreover, a wide variety of anomalies may occur in practice, which cannot be captured solely through anomaly injection. To address these issues, we propose a Trace-Driven Anomaly Detection (TDAD) approach based on a Span Causal Graph (SCG) representation, which trains a model using a Graph Neural Network (GNN) and Positive and Unlabeled (PU) learning. This technique allows the model parameters to be optimized by estimating the underlying data distribution. As a result, TDAD can be effectively trained using a small number of labeled anomalous traces along with a relatively large number of unlabeled traces. Our evaluation reveals that TDAD outperforms not only the existing unsupervised trace-based anomaly detection methods by 11.9% in terms of F-1-score but also a supervised learning-based benchmark by 12x in terms of detection time.

关键词： Anomaly Detection Distributed Tracing Microservice Positive and Unlabeled Learning

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