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共 2746 条记录，以下是131-140 订阅

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Multi-objective Optimization based Service Pre-deployment in Edge computing 15

Multi-objective Optimization based Service Pre-deployment in...

引用

15th ieee international conference on Software Engineering and Service Science (ICSESS)

作者： Liu, Junju Zhang, Qianqian Hubei Univ Zhixing Coll Wuhan Peoples R China

ISBN: (纸本)9798350389166;9798350389173

Edge computing enhances service quality by deploying application services closer to the data source at the network edge, providing rapid responses to the increasing service demands. However, edge servers, dispersed across the network, have limited computational and storage capabilities, necessitating efficient service deployment strategies to collaboratively process service request chains. Existing deployment methods focus solely on the response time of user requests, neglecting edge server resource utilization, leading to suboptimal deployments. This paper proposes a pre-deployment method for microservice compositions, aiming to minimize the response time of user requests and the proportion of services processed by cloud servers. By combining NSGA-II with the simulated annealing algorithm, our approach performs multi-objective optimization. Experimental results on a real-world dataset demonstrate that our method effectively improves edge service quality compared to baselines.

关键词： edge computing service deployment microservice composition multi-objective optimization

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A Survey on the Integration and Optimization of Large Language Models in Edge computing Environments 16

A Survey on the Integration and Optimization of Large Langua...

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16th international conference on Computer and Automation Engineering (ICCAE)

作者： Bhardwaj, Sarthak Singh, Pardeep Pandit, Mohammad Khalid Natl Inst Technol Dept Comp Sci Hamirpur India

ISBN: (纸本)9798350370058;9798350370164

In this survey, we delve into the integration and optimization of Large Language Models (LLMs) within edge computing environments, marking a significant shift in the artificial intelligence (AI) landscape. The paper investigates the development and application of LLMs in conjunction with edge computing, highlighting the advantages of localized data processing such as reduced latency, enhanced privacy, and improved efficiency. Key challenges discussed include the deployment of LLMs on resource-limited edge devices, focusing on computational demands, energy efficiency, and model scalability. This comprehensive analysis underscores the transformative potential and future implications of combining LLMs with edge computing, paving the way for advanced AI applications across various sectors.

关键词： Large Language Models Edge computing Model Optimization real-time Data Processing Edge AI Model Adaptability Pruning Model Quantization

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HMUR: A Two-Stage Heuristic for UAV Scheduling in Mobile Edge computing with time Window Constraints

HMUR: A Two-Stage Heuristic for UAV Scheduling in Mobile Edg...

引用

ieee international conference on Web Services (ieee ICWS)

作者： Liu, Guangrui Zhu, Xia Chen, Long Li, Xin Southeast Univ Sch Comp Sci & Engn Key Lab New Generat Artificial Intelligence Tech Minist Educ Nanjing Peoples R China Southeast Univ Nanjing Peoples R China

ISBN: (纸本)9798350368567;9798350368550

In Mobile Edge computing (MEC), efficiently scheduling limited resources to fulfill the service requirements of massive Internet of Things (IoT) ground devices (GDs) presents a significant challenge. Unmanned Aerial Vehicles (UAVs), as airborne mobile devices, can be deployed to GD Task Areas (TAs) to provide communication relay or computational support. In this paper, a new multiple UAVs scheduling problem with UAV heterogeneity and time windows constraints is considered and modeled as a Multi-Traveling Salesman Problem (MTSP) with soft time constraints. A two-stage heuristic Heterogeneous Multiple UAVs Routing (HMUR) was proposed for the problem. The approach first identifies TAs and the optimal hovering positions for UAVs, and defines an effective fitness measurement to match between UAVs and different routing path under certain energy constraints. A score function is also defined to determine the final path under the real-time constraints of tasks, thereby enhancing the Quality of Service (QoS). Simulation results demonstrate that our proposed HMUR method surpasses existing baseline algorithms across multiple metrics, validating its effectiveness in optimizing resource scheduling in MEC environments.

关键词： mobile edge computing heterogeneous multiple UAVs IoT network resource scheduling path planning

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ServiceCoder: A Service computing Code Generation Model

ServiceCoder: A Service Computing Code Generation Model

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ieee international conference on Web Services (ieee ICWS)

作者： Chen, Yue Yu, Lei Zhou, Xiang Su, Huafeng Liu, Junhua Wang, Yuhang Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China

ISBN: (纸本)9798350368567;9798350368550

This paper reports on work in progress on the application of a Parameter-Efficient Fine-Tuning (PEFT[1]) that combines a baseline code generation model (CGM) with Quantization and Low-rank Adapter (QLoRA[2]). Thereby improving the progress of work on baseline models in dealing with the quality of service code. Current research has focused on applying CGM to code generation tasks for common programming projects. At the same time, fewer attempts have been made to fine-tune the models for service computing, resulting in poor performance of open-source CGMs for code generation tasks in this domain. By acquiring a dataset of code snippets related to service computing tasks, we develop a CGM, ServiceCoder. Experimental results show that ServiceCoder is better than pre-trained CGMs in programming in the service computing domain. Finally, the RESTful[3] API integration ensures the security and convenience of the model in real applications, offering a novel approach to investigating the intersection of code generation and Service computing;thus, this work makes an exploratory contribution.

关键词： code generation parameter-efficient fine-tuning large language model service computing

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Development of Wave Electronics in the Focus of international conference WECONF

Development of Wave Electronics in the Focus of Internationa...

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2024 Wave Electronics and its Application in Information and Telecommunication Systems, WECONF 2024

作者： Dymkova, Svetlana S. Varlamov, Oleg V. Vienna Austria Moscow Technical University Of Communications And Informatics Moscow Russia

ISBN: (纸本)9798350364989

Wave electronics and acoustoelectronic devices are the basic elements of all systems of mobile and cellular communications, navigation, radar, monitoring, robotics, sensors, which make it possible to process complex signals in real time with high sensitivity, wide dynamic range and high noise immunity. The materials of the international scientific conference WECONF reflect the current state of wave electronics and its application in information and telecommunication systems. The article discusses historical aspects of wave electronics development, classification of radio-electronic equipment and systems. Statistical data on the results of conference materials publication in international databases is presented. © 2024 ieee.

关键词： real time systems

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TinyML enabled smart parking dynamic slots computing and license plate recognition 20

TinyML enabled smart parking dynamic slots computing and lic...

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20th ieee international Wireless Communications and Mobile computing (ieee IWCMC) - Green and Intelligent Communications

作者： Henaien, Amira Ben Elhadj, Hadda Fourati, Lamia Chaari SM RTS Digital Res Ctr CNRS Elons Sfax Tunisia

ISBN: (纸本)9798350361261;9798350361278

The increasing global automotive fleet brings different adverse effects like pollution, congestion, and inefficient land use. In response, smart parking emerges as a multifaceted solution, that significantly enhances urban life by increasing time efficiency, boosting revenue generation, and providing a framework for better urban planning. Our system harnesses the capabilities of Tiny Machine Learning (TinyML) and IoT cameras to effectively tackle urban parking challenges. Key features encompass accurate license plate identification, a reservation mobile app, and real-time slot calculation. Our TinyML integration ensures a lightweight and energy-efficient implementation, promoting efficiency in resource-constrained environments. Results showcase high accuracy in slot computation and license plate recognition, signaling a significant stride towards efficient urban living with global applicability.

关键词： Smart parking IoT cameras TinyML

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LEASE: Leveraging Energy-Awareness in Serverless Edge for Latency-Sensitive IoT Services

LEASE: Leveraging Energy-Awareness in Serverless Edge for La...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Verma, Aastik Satpathy, Anurag Das, Sajal. K. Addya, Sourav Kanti Natl Inst Technol Karnataka Cloud & Smart Syst Serv Lab Dept Comp Sci & Engn Surathkal India Missouri Univ Sci & Technol Rolla MO USA

ISBN: (纸本)9798350304367;9798350304374

Resource scheduling catering to real-time IoT services in a serverless-enabled edge network is particularly challenging owing to the workload variability, strict constraints on tolerable latency, and unpredictability in the energy sources powering the edge devices. This paper proposes a framework LEASE that dynamically schedules resources in serverless functions catering to different microservices and adhering to their deadline constraint. To assist the scheduler in making effective scheduling decisions, we introduce a priority-based approach that offloads functions from over-provisioned edge nodes to under-provisioned peer nodes, considering the expended energy in the process without compromising the completion time of microservices. For real-world implementations, we consider a testbed comprising a Raspberry Pi cluster serving as edge nodes, equipped with container orchestrator tools such as Kubernetes and powered by OpenFaaS, an open-source serverless platform. Experimental results demonstrate that compared to the bench-marking algorithm, LEASE achieves a 23.34% reduction in the overall completion time, with 97.64% of microservices meeting their deadline. LEASE also attains a 30.10% reduction in failure rates.

关键词： Serverless Edge computing IoT Microservices Completion time Energy Awareness Deadline

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Automated Biofloc Technology for Fish Farming with GSM based Monitoring System 3

Automated Biofloc Technology for Fish Farming with GSM based...

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3rd ieee international conference on robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hassain, Md Mehedi Gani, Muhammad Osman Rahman, Abdur Hasnat, S.M. Rahbar Abdullah International Islamic University Chittagong Dept. of Electrical & Electronics Engineering Chittagong Bangladesh East Delta University Dept. of Electrical & Electronics Engineering Chittagong Bangladesh

ISBN: (纸本)9798331534400

Biofloc technology mitigates aquaculture's environmental impact by using microbial flocs to convert organic waste into nutrient rich feed. To enhance water quality management, our research focuses on a GSM based monitoring system for real time data collection and analysis. The system integrates pH, temperature, and ultrasonic sensors with an Arduino Uno microcontroller, GSM module, servo, and pump motors to monitor and regulate water quality. The system measures timely and crucial data on water quality, including parameters such as temperature, pH levels, and the state of the feeding box. The pH and temperature sensors provide analog readings, while the ultrasonic sensor monitors feeding box levels. Food management is controlled by a servo motor that operates the feeding box, and a pump motor maintains water quality. The system uses temperature and pH sensors for rapid hazard detection, minimizing ammonia and nitrite buildup risks. The GSM module enables real time data connectivity, allowing users to track and control system operations remotely. This study empowers fish farmers and hobbyists with real-time water quality data, promoting informed decision-making to enhance aquatic health and productivity. It offers valuable insights to the broader aquaculture community. © 2024 ieee.

关键词： Decision making

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Design of Mixed-Signal LSI with Analog Spiking Neural Network and Digital Inference Circuits for Reservoir computing

Design of Mixed-Signal LSI with Analog Spiking Neural Networ...

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international Joint conference on Neural Networks (IJCNN)

作者： Moriya, Satoshi Yamamoto, Hideaki Ishikawa, Masaya Yuminaka, Yasushi Horio, Yoshihiko Madrenas, Jordi Sato, Shigeo Tohoku Univ Elect Commun Res Inst Sendai Miyagi Japan Gunma Univ Grad Sch Sci & Technol Kiryu Gumma Japan Univ Politecn Cataluna Dept Elect Engn Barcelona Spain

ISBN: (纸本)9798350359329;9798350359312

Edge computing requires low-power, real-time processing of complex information. Spiking neural networks are highly expected to be applied to edge computing due to their efficient computing properties. Here, we design a mixed-signal LSI consisting of an analog spiking neural network and digital inference circuits for edge device. The two-variable spiking neuron circuits operate in an analog manner using the physical properties of transistors and are connected through synaptic circuits to form a network. The neural network successfully operates and exhibits complex nonlinear behavior in response to external inputs. The power consumption of the spike generation is as low as tens of femtojoules per spike because the transistors in the circuits operate in the subthreshold region, which is enough to be used as edge computing devices. In addition, a digital circuit was designed to perform real-time inference using the spiking sequences from the analog spiking neural network. The result showed that the mixed-signal LSI consisting of the analog spiking neural network and the digital inference circuits can be applied to the spoken digit classification task in real time. The proposed system has the potential to be used as ultra-low-power neuromorphic hardware in practical applications.

关键词： spiking neural network mixed-signal LSI reservoir computing edge computing

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Classification and Featurization of Motherboard Components Using YOLO Through Machine Learning

Classification and Featurization of Motherboard Components U...

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2024 ieee international conference on computing, Applications and Systems, COMPAS 2024

作者： Rahman Fahim, Md. Imadur Mahazabin Senthia, Sabrina Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

The role of technology is vital and can be observed through the 5th industrial revolution. As a matter of fact, the impact is so severe, it can be felt almost everywhere. As technology advances, one of the most promising frontiers lies in the realm of Artificial Intelligence (AI), with machine learning at its forefront. Object detection is a crucial such part with applications in robotics and image analysis. This paper presents a comprehensive study on vision AI, focusing on the detection of motherboard components utilizing the YOLOv8 object detection framework. The results demonstrate the effectiveness and real time effectiveness of YOLOv8 in terms of motherboard object detection. Simultaneously, this work validates the feasibility of employing YOLOv8 for in-depth analysis and object detection on various circuit boards. This will allow many automated devices to repair circuit boards with the help of artificial intelligence. Consequently, allowing robots to detect flaws and repair integrated hardware components in situations considered difficult for humans. © 2024 ieee.

关键词： Robot vision

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