Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Network Flow Queuing Delay Prediction for City Public Services Based on Long Short-term Memory

Long Zhang, Yu Chen, Xinyi Huang, Cheng-Fu Yang, and Peng Xue

(Received October 21, 2020; Accepted January 28, 2021)

Keywords: load prediction, Spring Cloud, microservice architecture, network flow queuing delay, long short-term memory (LSTM)

It is very important to accurately predict the network flow queuing delay to improve the network performance of city public services. City public services are the offspring of the paradigm “smart + connected communities” and aim to overcome the problems of isolated and fragile data collection because of administrative divisions. Quality of service is one of the important evaluation indexes in service-level agreements, in which low delay is a basic requirement for measurement. In order to improve city public services and predict the network flow queuing delay of city public services in advance, we propose a framework based on long short-term memory (LSTM) that will allow the government to enhance service efficiencies and offer better service experiences for every citizen. The results obtained by using the investigated framework show that the proposed algorithm has superior aggregated prediction accuracy and real-time performance to other methods.

Corresponding author: Yu Chen, Cheng-Fu Yang




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