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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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Optimal Multi-Constrained Workflow Scheduling for Cyber-Physical systems in the Edge-Cloud Continuum 48

Optimal Multi-Constrained Workflow Scheduling for Cyber-Phys...

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48th Annual ieee international Computers, Software, and applications conference (COMPSAC) - Digital Development for a Better Future

作者： Kouloumpris, Andreas Stavrinides, Georgios L. Michael, Maria K. Theocharides, Theocharis Univ Cyprus Dept Elect & Comp Engn KIOS Res & Innovat Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9798350376975;9798350376968

The emerging edge-hub-cloud paradigm has enabled the development of innovative latency-critical cyber-physical applications in the edge-cloud continuum. However, this paradigm poses multiple challenges due to the heterogeneity of the devices at the edge of the network, their limited computational, communication, and energy capacities, as well as their different sensing and actuating capabilities. To address these issues, we propose an optimal scheduling approach to minimize the overall latency of a workflow application in an edge-hub-cloud cyber-physical system. We consider multiple edge devices cooperating with a hub device and a cloud server. All devices feature heterogeneous multicore processors and various sensing, actuating, or other specialized capabilities. We present a comprehensive formulation based on continuous-time mixed integer linear programming, encapsulating multiple constraints often overlooked by existing approaches. We conduct a comparative experimental evaluation between our method and a well-established and effective scheduling heuristic, which we enhanced to consider the constraints of the specific problem. The results reveal that our technique outperforms the heuristic, achieving an average latency improvement of 13.54% in a relevant real-world use case, under varied system configurations. In addition, the results demonstrate the scalability of our method under synthetic workflows of varying sizes, attaining a 33.03% average latency decrease compared to the heuristic.

关键词： scheduling workflow cyber-physical system edge-cloud mixed integer linear programming optimization

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Towards a Talking Tiny Cognitive Architecture for the Study of Spoken Language Evolution 27

Towards a Talking Tiny Cognitive Architecture for the Study ...

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27th international conference on System Theory, Control and computing, ICSTCC 2023

作者： Kaje, Smitha Saviour, George Doboli, Alex Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY United States

ISBN: (纸本)9798350337983

Understanding spoken language evolution can offer important insight that is needed to devise intelligent systems with speech capabilities. However, existing work mostly focuses on mathematical and software models that are insensitive to naturalistic conditions, even though such conditions are essential in the evolution of real languages. This paper discusses the implementation of Talking Tiny Cognitive Architecture proposed to study language evolution in more naturalistic conditions. The architecture can generate new words that are verbally communicated to describe environment conditions. It also attempts to understand and learn words verbally communicated by another architecture. Experiments describe the performance of the implementation as an embedded system. © 2023 ieee.

关键词： embedded systems

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Performance optimization of Learning Trees modules in the ECL repository 3

Performance optimization of Learning Trees modules in the EC...

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3rd international conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Shashank, B. Sudershan, K.S. Shobha, G. Manonmani, S. Dev, Roger Rv College of Engineering Dept. of Computer Science Bangalore India LexisNexis Risk Solutions United States

ISBN: (纸本)9798331528201

Multi-Node computation, also known as distributed computing, is a paradigm that allows for the efficient utilization of multiple interconnected nodes or machines to perform complex computational tasks. By dividing the workload and distributing it across multiple nodes, multi-node computation enables parallel processing, scalability, and improved performance for a wide range of applications. HPCC systems® from LexisNexis Risk Solutions is a proven, open source solution for big data insights that can be implemented by businesses of all sizes. HPCC systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. ECL is the Enterprise Control Language designed specifically for huge data projects using the HPCC systems platform. It is extremely scalable and a powerful declarative data-processing language with native multi-processing capabilities and used for HPCC systems. Some core ECL functions, like the ones handling Learning Tree algorithms of Machine Learning Bundle are recursive in nature and hence high computational time is needed. This paper thus presents a solution to achieve optimized processing of ML learning tree algorithms in ECL through embedding python libraries. The proposed approach was tested on standard datasets and a significant decrease in computation time. For real Estate Valuation Regression dataset the computation time was reduced from 11.371 seconds to 0.886 seconds using the proposed method showing 1324% improvement with no impact on the accuracy. Random Forests are also implemented using the proposed Decision Tree method allowing the users to specify the number of decision trees to be used in the forest. © 2024 ieee.

关键词： Parallel processing systems

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Energy Efficient Workflow Scheduling Algorithm for Latency-Sensitive applications using Cloud-Fog Collaboration 17

Energy Efficient Workflow Scheduling Algorithm for Latency-S...

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17th ieee international conference on Advanced Networks and Telecommunications systems, ANTS 2023

作者： Shukla, Prashant Pandey, Sudhakar National Institute of Technology Department of Information Technology Chhattisgarh Raipur India

ISBN: (纸本)9798350307672

The objectives of most studies are aimed at employing fog computing (FC) as an effective support to cloud computing (CC) in order to monitor sensor data and their associated necessities via the rapid advancements in Internet of Things (IoT). FC is completely dedicated to IoT sensor systems and delivers CC based computing, networking, and storage services. The cloud-Fog Collaboration (CFC) Environment will be more suitable for real-time IoT data sensing and workflow applications where fast and reliable internet connectivity is available. Workflow Scheduling means efficiently assigning the interdependent tasks to available computing resources. The workflow scheduling techniques play a significant role in minimizing the latency in Heterogeneous computing systems (HCS). In this paper, we present a novel Hybrid-metaheuristics Multi-objective based Workflow Scheduling Algorithm (HMWSA) by combining Harmony Search (HS) and Genetic Algorithm (GA) for optimizing workflow scheduling in heterogeneous CFC. The proposed minimization objective function takes into account goals like makespan, energy usage, and cost. To preserve the precedence relationship among tasks, a topologically sorted execution sequence is constructed. Extensive testing results show that our proposed approach yields substantially better cost-makespan trade-offs while significantly reducing energy consumption than other existing algorithms. © 2023 ieee.

关键词： Multiobjective optimization

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A Defense Mechanism Against LOKI Attacks in Federated Learning for Enhancing Big Data Privacy in Mobile systems

A Defense Mechanism Against LOKI Attacks in Federated Learni...

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2024 ieee international conference on Big Data, BigData 2024

作者： Nawshin, Faria Unal, Devrim Suganthan, Ponnuthurai N. College of Engineering Qatar University Kindi Computing Research Center Doha Qatar

ISBN: (纸本)9798350362480

With the exponential growth of mobile applications, Android systems have become a significant source of big data which provides both vast opportunities and substantial privacy challenges. This makes it essential to adopt secure learning approaches like Federated Learning (FL). FL is a decentralized approach that trains models across distributed data without centralizing sensitive information. However, FL still faces security threats in the scope of big data, where the volume and variety of data increase the risks of sophisticated attacks such as the LOKI attacks. This attack exploits shared model updates in FL to infer and leak sensitive data, even in a decentralized setup. In this paper, we simulate the LOKI attacks within an FL environment using a real-world Android malware detection dataset characterized by dynamic analysis features. We propose a defense mechanism that combines differential privacy and anomaly detection to reduce the impact of LOKI attacks. While this mechanism is designed for mobile systems, where the large volume of data generated by numerous applications mirrors the complexities of big data environments, this approach is adaptable and can be applied to other big data contexts. Through extensive experiments, we demonstrate the effectiveness of the proposed mechanism in enhancing data privacy and securing FL for applications where big data privacy is foremost. © 2024 ieee.

关键词： Federated learning

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Fitness EDA using Advanced PySpark 4

Fitness EDA using Advanced PySpark

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4th ieee international conference on Data Engineering and Communication systems, ICDECS 2024

作者： Kiran, C. Pushpa, T.S. Shilpa, S. Shailaja, K.P. Bms College of Engineering Department of Computer Applications Bengaluru India

ISBN: (纸本)9798350393354

Exploratory Data Analysis (EDA) plays a pivotal role in comprehending intricate datasets and deriving meaningful insights. This research focuses on harnessing advanced PySpark and SQL techniques for conducting EDA on FitRec (Fitness Record) data, an extensive dataset obtained from the well-known fitness tracking application, Endomondo. Leveraging PySpark's distributed computing capabilities and Spark SQL's querying prowess, this study delves into data preprocessing, cleaning, and visualization. Also, this study enables a comprehensive understanding of users' fitness patterns and performance. Through interactive analysis and real-world case studies, the study showcases the effectiveness of employing PySpark and SQL for in-depth EDA on FitRec data, delivering valuable knowledge to researchers and data analysts in the fitness domain. © 2024 ieee.

关键词： Data visualization

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ARCTO: AIoT System for Reducing Carbon Emissions using Traffic Optimization 18

ARCTO: AIoT System for Reducing Carbon Emissions using Traff...

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18th ieee/ASME international conference on Mechatronic and embedded systems and applications (MESA)

作者： Kim, Ryan H. Min, Hyung-Gi Choate Rosemary Hall Wallingford CT 06492 USA Hanyang Univ Ctr Educ Innovat Seoul South Korea

ISBN: (纸本)9781665455701

In the status quo, traffic control systems operate on predetermined patterns and instructions devised from past data. While this method functions effectively for traffic under normal conditions, it becomes heavily congested and inefficient during instances of high traffic, which leads to a multitude of temporal, economic, health, and environmental harms. However, by combining traditional traffic controllers with modern technologies such as Internet of Things devices and computer vision, these issues can be effectively addressed. This research presents a novel, affordable Artificial Intelligence of Things traffic control system that enables accurate real-time vehicle detection and signal control. This work is split into two sections: (1) an AIoT physical system that can scan traffic conditions in real-time and (2) a realistic traffic simulator with a custom optimization algorithm. Combined, this research provides up to 35% greater throughput, 50% reduced waiting time, and 50% reduction in greenhouse gas emission reductions in comparison to non-optimized algorithms used in the status quo. The implementation of this work leads to various temporal, economic, environmental, and health benefits;in addition to providing comparable emission reduction as the complete replacement of all internal combustion engine vehicles with battery electric vehicles, while significantly reducing vehicle travel time, systems installation time, and cost.

关键词： artificial intelligence of things vehicle emissions adaptive traffic signal control intelligent transport systems deep learning

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AtmoCell: Advanced Home Air-Quality Monitoring Using Low-Power embedded systems 32

AtmoCell: Advanced Home Air-Quality Monitoring Using Low-Pow...

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32nd National conference with international Participation, TELECOM 2024

作者： Gorbounov, Yassen New Bulgarian University Department of Informatics Sofia Bulgaria

ISBN: (纸本)9798350355000

The indoor air quality significantly impacts human health, and monitoring it in real time is essential for ensuring safe living environments. This paper presents AtmoCell, an advanced, low-power air-quality monitoring node designed for home use. The system integrates a CCS811 gas sensor for detecting volatile organic compounds and CO2, a BME280 sensor for monitoring temperature, humidity, and pressure, and a ferroelectric random access memory based microcontroller for energy-efficient data logging. The device utilizes an ESP32 for wireless connectivity and real-time data transmission. With modularity in mind, and a focus on low-power operation and compact design, AtmoCell offers an affordable, scalable solution for continuous air quality monitoring in residential settings, aligning with current health and environmental standards. © 2024 ieee.

关键词： Ferroelectric RAM

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Detailed Implementation of Hardware-In-the-Loop Validation of an Advanced Energy Management Controller for Power-Split HEVs 3

Detailed Implementation of Hardware-In-the-Loop Validation o...

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3rd ieee international conference on computing, Communication, and Intelligent systems, ICCCIS 2022

作者： Biswas, Atriya Dhale, Sumedh Khzym, Samuel Emadi, Ali Hamilton Canada

ISBN: (纸本)9781665462006

This brief will describe the entire process of setting up a hardware-in-the-loop (HIL) simulation platform required to validate the performances of any hybrid supervisory control strategy for hybrid electric vehicle (HEV)s. Performances of any new control strategy' developed for an electrified powertrain's energy management system (EMS) must be validated in the HIL platform to confirm its real-time applicability in an actual microcontroller. Setting up the HIL platform is not as trivial as developing a model-in-the-loop (MIL) platform on SIMULINK®, and it involves a handful of intricate technical steps. The HIL platform presented in this paper is curated with two primary elements, i.e., the vehicle plant model simulated in ***® and the EMS embedded on dSPACE® MicroAutoBox II. This utilitarian paper streamlines the entire journey from a MIL to a HIL platform. Results corroborate the real-time implementation of a well-accredited supervisory control strategy, i.e., equivalent consumption minimization strategy (ECMS), for a commercially available HEV. © 2022 ieee.

关键词： Energy management systems

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The Context for Contextualizing - Design Implications for Adaptive Teacher Support systems for More Relevant Instruction 23

The Context for Contextualizing - Design Implications for Ad...

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23rd ieee international conference on Advanced Learning Technologies (ICALT)

作者： Rao, Nanjie Li, Qing Murphy, Shaina Chu, Sharon Lynn Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA

ISBN: (纸本)9798350300543

Contextualization in education connects learning material to students' experiences and background knowledge to make it relevant and meaningful. It involves placing content in a real-world context to show students the practical applications and relevance of the concepts they are learning. Contextualizing is an important pedagogical strategy. In order to support teachers in this effort, it is necessary to understand what contexts to provide for teachers to make relevant instruction in certain situations. In this paper, we aim to explore: In what context do teachers need students' everyday life experiences to make the class more relevant to students? We conducted focus groups with teachers who shared their contextualizing experiences. The context for contextualizing refers to the information used to characterize the situation in the classroom and the experiences of the students. The results of this research include the three categories of context information, time, activity, and location, that are relevant to the lesson and the students' prior experiences related to these factors. Design implications are discussed in this paper to illustrate how adaptive teacher support systems can provide relevant and meaningful information to the teachers by considering context information and students' prior experiences.

关键词： relevant instruction adaptive teacher support system qualitative study context ChatGPT

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