.png){kind=icon}
.png){kind=icon}
Return
New algorithm of multi-strategy channel allocation for edge computing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.aeue.2020.153372.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Wireless mesh networks (WMNs) are a kind of wireless network technology that can transmit multi-hop information, and have been regarded as one of the key technologies for configuring wireless machines. In WMNs, wireless routers can provide multi-hop wireless connections between nodes in the network and access the Internet through gateway devices. Multicast is a communication technology that uses best effort to send information from a source node to multiple destinations. In this paper, we study the problems of channel interference and time slot multi-user collisions caused by radio during WMNs information transmission. Through innovative use of edge computing technology to construct a node data cache model and step-by-step calculation of node channel separation, a multi-strategy channel allocation algorithm for edge computing is proposed. The mechanism of this algorithm is to use edge computing technology to pre-store the data in the nodes in the multicast tree and calculate the channel separation between the nodes, and then select the transmission channel number of the node with the least interference to avoid mutual interference between node information transmissions. Through experimental tests and comparisons, the MSCA (Multi-Strategy Channel Allocation algorithm for edge computing) algorithm presented in this paper can minimize channel interference and overall network energy consumption while satisfying throughput and end-to-end delay. (C) 2020 Elsevier GmbH. All rights reserved.
Keywords:
Edge computing
Multi-hop
Wireless mesh network
Channel allocation
Multi-strategy
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
A
IF:
3.2
Papers:
5.6K
Citations:
8.3K