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UGV-awareness task placement in edge-cloud based urban intelligent video systems

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Abstract

With the development of Mobile Edge Computing, driverless, 5 G, and related techniques, Edge-Cloud based Urban Intelligent Video Systems are extremely promising to support public safety through powerful analysis and timely response. Furtherly, flexible Unmanned Ground Vehicles(UGVs), which are equipped with edge devices, can enhance these edge systems to withstand these abnormalities: natural disasters, abnormal crowd flows, and other emergencies. In this regard, as a critical issue in edge systems, task placement in these systems needs to consider these “mobile” edge nodes: ICVs(UGVs). Therefore, a novel and effective framework named Optimized Centroids K-means based Task Placement framework is proposed: we firstly involve the clustering approach to optimize initial centroids as the positions of ICVs in terms of Edge Nodes, various typical optimization methods can be utilized to place related edge tasks effectively. The experimental results demonstrate that our novel framework has a great improvement over several existing typical strategies and supports multiple optimization methods well in this paper.

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Funding

This work is supported by the National Key Research and Development Plan of China (No. 2022YFC3900800), the National Natural Science Foundation of China (Grant Nos. 61972128, 62102126), and the Fundamental Research Funds for the Central Universities, China (PA2021KCPY0050, JZ2022HGQA0163).

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Authors and Affiliations

  1. School of Software, Hefei University of Technology, 485 Danxia Rd, Hefei, 230601, Anhui, China

    Gaofeng Zhang & Benzhu Xu

  2. School of Computer Science and Information Engineering, Hefei University of Technology, 485 Danxia Rd, Hefei, 230601, Anhui, China

    Xiang Li, Liqiang Xu, Liping Zheng & Wenming Wu

  3. Intelligent Interconnected Systems Laboratory, Hefei University of Technology, Hefei, Anhui Province, China

    Gaofeng Zhang & Liping Zheng

  4. School of Public Security and Emergency Management, Anhui University of Science and Technology, Hefei, 231131, China

    Ensheng Liu

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  1. Gaofeng Zhang
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  2. Xiang Li
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  3. Liqiang Xu
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  4. Ensheng Liu
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  5. Liping Zheng
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  6. Wenming Wu
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  7. Benzhu Xu
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Contributions

GZ and XL wrote the main manuscript text. GZ, LZ, and BX designed the main framework in this paper. LX, WW, and EL prepared figures and data processing. All authors reviewed the manuscript.

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Correspondence to Benzhu Xu.

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Zhang, G., Li, X., Xu, L. et al. UGV-awareness task placement in edge-cloud based urban intelligent video systems. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04305-w

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