Situation-Aware Orchestration of Resource Allocation and Task Scheduling for Collaborative Rendering in IoT Visualization

Chen, Ruitao; Wang, Xianbin

doi:10.1109/tsusc.2022.3165016

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…At the same time, when collecting and storing large-scale grid edge cloud data for cross-region networking, data transmission needs to be performed in a networked environment, while data storage can be performed in an offline environment. By storing and calling the data in an offline way, we can not only increase the storage capacity of the database but also improve the efficiency of data analysis [13]. Therefore, to provide secure and stable storage space for large-scale unstructured data in the grid edge cloud, it is necessary to design an access interface to connect the temporal database to different databases with different functions and features, in addition to the basic temporal database, to build a multi-target database.…”

Section: Multi-objective Database Designmentioning

confidence: 99%

Research on unstructured data storage in large-scale grid edge cloud for cross-region networking

Huang,

Lin,

Chen

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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The current conventional unstructured data storage methods for grid edge cloud mainly use distributed storage to realize the data storage, which leads to poor storage efficiency due to ignoring the fitness between data attributes and databases. In this regard, a large-scale grid edge cloud unstructured data storage method for cross-region networking is proposed. The features of the edge cloud unstructured data are extracted utilizing data fusion. Access interfaces are designed to connect the temporal database with other types of databases to build a multi-target database with higher adaptability, and the data storage network topology is designed. In the experiments, the storage performance of the designed storage method is tested. The final results can prove that the average response time of the system is shorter when the proposed method is used to store the edge cloud data, and it has a more desirable data storage performance.

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Section: Multi-objective Database Designmentioning

confidence: 99%

Research on unstructured data storage in large-scale grid edge cloud for cross-region networking

Huang,

Lin,

Chen

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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“…SA-enabled smart systems emphasize three ascending levels, namely perception, comprehension, and projection, of the environment where they are deployed [45]. Many comprehensive modeling works for SA emerged to consider objective synthesized information such as weather, location, system synthesis logs, etc., for understanding the context of the deployed environment [46][47][48][49][50][51][52]. Nevertheless, among all the factors, human intentions play a key role in the evolvability and interactivity of smart systems.…”

Section: Situation Awareness and Situ Frameworkmentioning

confidence: 99%

Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems

Bian,

Zhang,

Chang

2024

Blockchains

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The decentralized nature of blockchain enables data traceability, transparency, and immutability as complementary security features to the existing Internet of Things (IoT) systems. These Blockchain-based IoT (BIoT) systems aim to mitigate security risks such as malicious control, data leakage, and dishonesty often found in traditional cloud-based, vendor-specific IoT networks. As we steadily advance into the era of situation-aware IoT, the use of machine learning (ML) techniques has become essential for synthesizing situations based on sensory contexts. However, the challenge to integrate learning-based situation awareness with BIoT systems restricts the full potential of such integration. This is primarily due to the conflicts between the deterministic nature of smart contracts and the non-deterministic nature of machine learning, as well as the high costs of conducting machine learning on blockchain. To address the challenge, we propose a framework named Situ-Oracle. With the framework, a computation oracle of the blockchain ecosystem is leveraged to provide situation analysis as a service, based on Recurrent Neural Network (RNN)-based learning models tailored for the Situ model, and specifically designed smart contracts are deployed as intermediary communication channels between the IoT devices and the computation oracle. We used smart homes as a case study to demonstrate the framework design. Subsequently, system-wide evaluations were conducted over a physically constructed BIoT system. The results indicate that the proposed framework achieves better situation analysis accuracy (above 95%) and improves gas consumption as well as network throughput and latency when compared to baseline systems (on-chain learning or off-chain model verification). Overall, the paper presents a promising approach for improving situation analysis for BIoT systems, with potential applications in various domains such as smart homes, healthcare, and industrial automation.

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A Novel Outlier-Tolerable and Predictive Approach to Web Service Composition

Sun

Chen

Dai

et al. 2022

Lecture Notes in Computer Science

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Situation-Aware Orchestration of Resource Allocation and Task Scheduling for Collaborative Rendering in IoT Visualization

Cited by 4 publications

References 40 publications

Research on unstructured data storage in large-scale grid edge cloud for cross-region networking

Research on unstructured data storage in large-scale grid edge cloud for cross-region networking

Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems

A Novel Outlier-Tolerable and Predictive Approach to Web Service Composition

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