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Optimization of Indoor sensor Position Under FLUENT 2

Optimization of Indoor Sensor Position Under FLUENT

引用

2nd international conference on distributed computing and Optimization Techniques, ICDCOT 2024

作者： Cheng, Xinliang Lu, Jifu School of Mechanical and Power Engineering Zhengzhou University Henan Zhengzhou China

ISBN: (纸本)9798350382952

In order to achieve efficient monitoring of temperature information in uneven spaces, it is necessary to arrange multiple sensors reasonably to reduce structural redundancy and improve the richness of temperature information, while meeting the conditions of economy and management convenience. This project intends to take the layout scheme of indoor temperature sensors as the research object, and conduct three-dimensional simulation of the air flow in commonly used working environments and standard air chambers using Fluent software. It simulates the temperature inside the room under different conditions through numerical simulation methods. Research has shown that the arrangement of sensors has a significant impact on the accuracy of measuring room temperature. (The measurement accuracy of the experimental V3 position sensors is higher than that of the sensors in other positions, while the horizontal sensors are only lower than the V3 position sensors. Their root-mean-square deviation and inhomogeneity coefficients are lower than those of the sensors in other positions). © 2024 ieee.

关键词： Numerical methods

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Enhancing distributed Energy Efficiency through Optimized Cluster Head Re-Election in WSNs 3

Enhancing Distributed Energy Efficiency through Optimized Cl...

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Kumar, R Vinod Kannan, M Ajay Arunkumar, S. Saveetha Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350375190

The optimal cluster head re-election method proposed in this study offers a unique way to improve distributed energy efficiency in Wireless sensor Networks (WSNs). Smart energy management plays a crucial role in extending the lifespan of WSNs due to their limited resources. Cluster-based architecture groups nodes into clusters and assigns a cluster head to handle data aggregation and communication with the base station. This architecture is widely used to minimize energy usage. Ineffective cluster head election and selection, however, may cause early energy depletion and network deterioration. Here, the proposal is to choose cluster heads by considering energy levels, communication distance, and data forwarding capabilities in order to tackle this difficulty. By using simulation studies, shows that our suggested strategy performs better than current methods in terms of network lifespan extension and energy efficiency. The results of the study demonstrate how optimal cluster head re-election may enhance WSN performance and promote the adoption of energy-efficient Internet of Things devices. © 2024 ieee.

关键词： Wireless sensor networks

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A Spatial Alignment Framework Using Geolocation Cues for Roadside Multi-View Multi-sensor Fusion 26

A Spatial Alignment Framework Using Geolocation Cues for Roa...

引用

ieee 26th international conference on Intelligent Transportation systems (ITSC)

作者： Zhao, Zhiguo Li, Yong Chen, Yunli Zhang, Xiaoting Tian, Rui Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Fac Sci Technol Beijing 100124 Peoples R China

ISBN: (纸本)9798350399462

Multi-modal sensor fusion plays a vital role in achieving high-quality roadside perception for intelligent traffic monitoring. Unlike on-board sensors in autonomous driving, roadside sensors present heightened calibration complexity, posing a challenge to spatial alignment for data fusion. And existing spatial alignment methods typically focus on one-toone alignment between cameras and radar sensors and require precise calibration. However, when applied to large-scale roadside monitoring networks, these methods can be difficult to implement and may be vulnerable to environmental influences. In this paper, we present a spatial alignment framework that utilizes geolocation cues to enable multi-view alignment across distributed multi-sensor systems. In this framework, a deep learning-based camera calibration model combined with angle and distance estimation is used for monocular geolocation estimation. A camera parameter approaching method is then used to search for pseudo camera parameters that can tolerate inevitable calibration errors in practice. Finally, the geolocation information is then used for data association between Light Detection and Ranging (LiDAR) and cameras. The framework has been conducted and tested at several intersections in Hangzhou. Experimental results show that the framework can achieve geolocation estimation errors of less than 1.1 m for vehicles traversing the monitored zone, demonstrating the framework's ability to accomplish spatial alignment with a singular execution, and apply it in extensive large-scale roadside sensor fusion scenarios.

关键词： Roadsides

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Higher Reconstruction Performance with Dual Deep Neural Networks for Wireless sensor Networks Compressed Temporal Signals 12

Higher Reconstruction Performance with Dual Deep Neural Netw...

引用

12th ieee international conference on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications, IDAACS 2023

作者： Rousseau, Sebastien Negrier, Romain Courreges, Fabien Engoti, Frank Itoua Quere, Raymond University of Limoges Xlim Institute Brive-la-Gaillarde19100 France NAIoBEE Company Brive-la-Gaillarde19100 France

ISBN: (纸本)9798350358056

In many applications using wireless sensor networks, the reliability of monitored data is crucial to analyze situations and take decisions. Compressed sensing methods are effective to ensure durability of a wireless sensor network installation and to overcome communication failures by drastically lowering the amount of data to transmit. The method proposed in this paper aims to perform an abstraction of the signals sparsity model required by compressed sensing in order to use a deep learning-based approach. This method is designed to be effective for all kinds of transient or temporally dynamic signals sufficiently regular to be predictable, that is to say signals that can be implicitly modelled by a sparse representation. Through the collaboration of two recurrent neural networks and signal processing algorithms, our temporal method outperforms, in terms of accuracy, state-of-art methods, including distributed compressed sensing, time series imputation and forecasting deep learning methods. Thus, high compression rates up to 95% can be reached while leading to an error of less than 0.5°C in reconstructing temperature signals. © 2023 ieee.

关键词： compressed sensing deep learning recurrent neural networks signal processing algorithms wireless sensor networks

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DR-DRL: distributed Redundant Task Offloading based on Reinforcement Learning 9

DR-DRL: Distributed Redundant Task Offloading based on Reinf...

引用

9th international conference on Information Science, Computer Technology and Transportation, ISCTT 2024

作者： Lan, Shizhan Lin, Hongcai Liang, Huaqian Chen, Shan Wang, Zhenyu School of Software Engineering South China University of Technology Guangzhou China China Mobile Group Guangxi branch Co. Ltd Nanning China

ISBN: (纸本)9798350388435

Edge computing enhances task reliability by employing redundant task executions across edge nodes. Conventional decentralized task offloading strategies, based on heuristics and game theory, either focus on optimization or are based on unrealistic assumptions. Moreover, existing Deep Reinforcement Learning (DRL) task offloading approaches underperform due to discrepancies between simulated environments and real systems, and overlook task redundancy, thus failing to meet reliability requirements. This paper proposes a DRL-based approach to offload distributed redundant tasks, namely DR-DRL (Decentralized and Redundant DRL), to solve the redundancy problem of distributed task offloading under edge computing. Experimental results show that DR-DRL has about 8% higher task success rate than other benchmark methods. ©2024 ieee.

关键词： Deep reinforcement learning

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Secure distributed Outsourcing of Large-scale Linear systems 42

Secure Distributed Outsourcing of Large-scale Linear Systems

引用

42nd ieee international conference on distributed computing systems (ICDCS)

作者： Feng, Da Zhou, Fucai He, Debiao Guo, Mengna Wu, Qiyu Northeastern Univ Software Coll Shenyang Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China China Mobile Grp Liaoning Co Ltd Ctr Informat Technol Shenyang Peoples R China

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Solving the system of linear algebraic equations (LAE) is the most well known and probably the most important of all numerical computations involving real numbers. Researchers have been committed to developing distributed algorithms to solve such systems for a long time. However, traditional distributed algorithms have serious security risks when the coefficients and the solution are of great value. To address the privacy issue, we propose a new secure distributed outsourcing protocol for solving large-scale LAE systems. Specifically, we give an algorithm for generating a generic repeatedly jointly strongly connected sequence, for the first time as far as we know. Then we embed the matrix masking technique in our distributed system requiring multiple rounds of iteration while keeping the correctness of convergence. In addition, for the first time, we give a method for discriminating by the agents under masking whether the plaintext approximate solution corresponding to the current masked solution satisfies a predetermined constraint. Finally, we give the experimental result to show the practicality of our new protocol.

关键词： Privacy-preserving Linear algebraic equation distributed outsourcing Verification

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A Comprehensive Modeling and Scheduling Approach for Allocating distributed Multi-Robot Software to the Edge/Cloud

A Comprehensive Modeling and Scheduling Approach for Allocat...

引用

2024 international conference on Intelligent Robots and systems

作者： Zhang, Yongzhou Mirus, Florian Pasch, Frederik Scholl, Kay-Ulrich Wurll, Christian Hein, Bjoern Karlsruhe Univ Appl Sci D-76133 Karlsruhe Germany Intel Labs D-76131 Karlsruhe Germany Karlsruhe Inst Technol D-76131 Karlsruhe Germany

ISBN: (纸本)9798350377712;9798350377705

Offloading software modules to the edge/cloud can enhance a robot's capabilities by leveraging massive computing power. However, determining which software module should be offloaded and scheduled to which robot/edge/cloud node is a challenging task, particularly for robot fleets with diverse tasks. In this paper, we tackle the software scheduling problem and introduce a taxonomy to categorize software modules and classify their applicability and requirements for offloading. Additionally, by using prior measurements, we model the compute cluster and formalize software scheduling as a multi-objective optimization problem which we tackle with a genetic algorithm. To evaluate our approach with a challenging setup, we build a mobile manipulation task using open-source frameworks and libraries in the Robot Operating System (ROS2) community in simulation as well as a mildly simplified real-world variant. Our evaluation shows significant improvements compared to the built-in scheduler of Kubernetes (K8s) regarding robotic specific metrics such as the rate of missed cycle time in both simulated and real-world experiments.

关键词： Microrobots

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Energy-Efficient Scheduling of Moldable Streaming Computations for the Edge-Cloud Continuum 9

Energy-Efficient Scheduling of Moldable Streaming Computatio...

引用

9th ieee international conference on Fog and Mobile Edge computing (FMEC)

作者： Khosravi, Sajad Kessler, Christoph Litzinger, Sebastian Keller, Joerg Linkoping Univ Linkoping Sweden Fernuniv Hagen Germany

ISBN: (纸本)9798350366495;9798350366488

We consider the problem of cost-effectively mapping a swarm of soft real-time stream processing applications with moldable-parallel tasks to multicore resources in the device-edge-cloud continuum, consisting of mobile devices, edge resources and cloud resources. We leverage flexibility from different parallelization degrees and frequency levels (DVFS) for the tasks, keeping application throughput constraints and communication bandwidth limitations while minimizing overall cost (including device/edge resource energy and cloud resource renting). We present two offline algorithmic solutions with a global view of the environment: an integer linear program (ILP) extending the crown scheduling approach for multi-layer distributed systems and a greedy heuristic algorithm. Our experimental evaluation for several real-world and synthetic scenarios shows that the time required for solving the scheduling problem to cost-optimality by the ILP is feasible for nontrivial scenarios. The heuristic achieves about 12% worse cost efficiency on average, yet operates much faster (by 1-2 orders of magnitude), allowing to scale up the problem size more than the ILP approach.

关键词： distributed stream processing Mapping Scheduling Moldable tasks Edge-Cloud continuum Energy efficiency DVFS

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distributed Service Placement in Ultra-Dense Edge computing: A Game-Theoretical Approach 23

Distributed Service Placement in Ultra-Dense Edge Computing:...

引用

23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： He, Junyi Zhang, Di Li, Xishuo Zhou, Yuezhi Zhang, Yaoxue School of Software Engineering Beijing Jiaotong University China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China School of Computer Science and Engineering Beihang University China Tsinghua University Department of Computer Science and Technology China

ISBN: (纸本)9781665494571

Ultra-dense mobile edge computing (MEC) is expected as an effective approach to provide services with ultra-low latency and massive connectivity, which is extremely important for delay-sensitive applications. In this paper, we study service instance (SI) placement for delay-sensitive applications in ultra-dense MEC, which decides where to place applications and the required data (programming codes and datasets). In general, the SI placement problem is a combinatorial optimization and usually does not have low-complexity optimal solutions. Existing centralized policies are difficult to implement due to the high complexity, especially when edge servers are densely deployed in the ultra-dense network (UDN). To design an approach that can make SI placement decisions quickly and efficiently, this paper investigates a distributed strategy for multi-user multi-server edge systems in UDN. We first formulate the SI placement problem as a non-linear combinatorial optimization, which is proven to be NP-hard to compute a centralized optimal solution. Then, we propose an uncooperative game to design a low-complexity distributed algorithm. We further analyze the properties of the game and prove the existence of pure strategy Nash Equilibrium. A polynomial-complexity distributed algorithm is proposed to get the Nash Equilibrium. Theoretical analysis shows the algorithm's convergence and the bound of the price of anarchy (PoA), which serves as a performance guarantee in the worst-case scenarios compared to the global optimum. Numerical results validate the convergence and near-optimal performance of the proposed distributed algorithm. © 2021 ieee.

关键词： Performance evaluation Games Reinforcement learning Nash equilibrium Servers distributed algorithms Ultra-dense networks

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Verifying Randomized Consensus Protocols with Common Coins 54

Verifying Randomized Consensus Protocols with Common Coins

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54th Annual ieee/IFIP international conference on Dependable systems and Networks (DSN)

作者： Gao, Song Zhan, Bohua Wu, Zhilin Zhang, Lijun Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China Inst Intelligent Software Guangzhou Peoples R China

ISBN: (纸本)9798350341065;9798350341058

Randomized fault-tolerant consensus protocols with common coins are widely used in cloud computing and blockchain platforms. Due to their fundamental role, it is vital to guarantee their correctness. Threshold automata is a formal model designed for the verification of fault-tolerant consensus protocols. It has recently been extended to probabilistic threshold automata (PTAs) to verify randomized fault-tolerant consensus protocols. Nevertheless, PTA can only model randomized consensus protocols with local coins. In this work, we extend PTA to verify randomized fault-tolerant consensus protocols with common coins. Our main idea is to add a process to simulate the common coin (the so-called common-coin process). Although the addition of the common-coin process destroys the symmetry and poses technical challenges, we show how PTA can be adapted to overcome the challenges. We apply our approach to verify the agreement, validity and almost-sure termination properties of 8 randomized consensus protocols with common coins.

关键词： Randomized consensus Threshold automata distributed protocols Common coin

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