Resource Allocation for Power IoT Edge Devices Based on Edge Node Collaboration

The current power IoT edge device resource allocation mechanism is mostly set as a basic form, and it is difficult to deal with the allocation according to the preset target, which leads to the low optimization ratio of resource allocation efficiency. In this regard, the design and practical implementation of the resource allocation method for power IoT edge devices based on the collaboration of edge nodes is proposed. According to the current demand for resource allocation processing, mobile edge computing is carried out first, and a multi-level approach is adopted to reduce the actual difficulty of target allocation processing and formulate a multi-level power IoT device resource allocation mechanism. Based on this, the resource allocation model of edge node collaborative power IoT edge device is constructed, and the resource allocation processing is realized by using compression processing and adaptive scheduling. The test results show that compared with the traditional distribution network optimization and maintenance resource allocation method and the digital dual-grid mobile edge network resource allocation model, the designed edge node collaborative power IoT edge device resource allocation method finally achieves a relatively high optimization ratio of resource allocation efficiency, which indicates that the resource allocation method for power IoT edge devices designed based on the principle of edge node collaboration is more efficient, more specific, more targeted, more balanced and more stable in allocation, and has practical application value.

Authors

Institutions

Publication Details

Published
2024-11-01
DOI
https://doi.org/10.1145/3705677.3705698
Primary Topic
Energy Harvesting in Wireless Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Resource Allocation for Power IoT Edge Devices Based on Edge Node Collaboration

Zilong Liu, Wenqin Chen, Hua Li, Shaohui Du et al.
Energy Harvesting in Wireless Networks
article

Resource Allocation for Power IoT Edge Devices Based on Edge Node Collaboration

Zilong Liu, Wenqin Chen, Hua Li, Shaohui Du, Zhaonan Wu
article en

Abstract

The current power IoT edge device resource allocation mechanism is mostly set as a basic form, and it is difficult to deal with the allocation according to the preset target, which leads to the low optimization ratio of resource allocation efficiency. In this regard, the design and practical implementation of the resource allocation method for power IoT edge devices based on the collaboration of edge nodes is proposed. According to the current demand for resource allocation processing, mobile edge computing is carried out first, and a multi-level approach is adopted to reduce the actual difficulty of target allocation processing and formulate a multi-level power IoT device resource allocation mechanism. Based on this, the resource allocation model of edge node collaborative power IoT edge device is constructed, and the resource allocation processing is realized by using compression processing and adaptive scheduling. The test results show that compared with the traditional distribution network optimization and maintenance resource allocation method and the digital dual-grid mobile edge network resource allocation model, the designed edge node collaborative power IoT edge device resource allocation method finally achieves a relatively high optimization ratio of resource allocation efficiency, which indicates that the resource allocation method for power IoT edge devices designed based on the principle of edge node collaboration is more efficient, more specific, more targeted, more balanced and more stable in allocation, and has practical application value.

China Southern Power Grid (China) (CN)
Openalex Percentile: Top 77%
Energy Harvesting in Wireless Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Resource Allocation for Power IoT Edge Devices Based on Edge Node Collaboration — Zilong Liu, Wenqin Chen, et al. · (2024) | TGRS Research Map | TGRS