Match Word with Deed: Maintaining Consistency for IoT Systems with Behavior Models

Ensuring the reliability and consistency of Internet of Things (IoT) systems is critical. Traditional approaches to maintaining consistency often rely on retry and rollback mechanisms, which can be inadequate and lead to further complications. These methods struggle with the complexity and heterogeneity of IoT systems, failing to provide robust and general solutions for real-time consistency assurance.

Authors

Institutions

Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1145/3691620.3695319
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Match Word with Deed: Maintaining Consistency for IoT Systems with Behavior Models

Jun Wei, Tao Wang, Huang Tao, Wei Chen et al.
Semantic Web and Ontologies
article

Match Word with Deed: Maintaining Consistency for IoT Systems with Behavior Models

Jun Wei, Tao Wang, Huang Tao, Wei Chen, Liwei Liu, Guoquan Wu
article en

Abstract

Ensuring the reliability and consistency of Internet of Things (IoT) systems is critical. Traditional approaches to maintaining consistency often rely on retry and rollback mechanisms, which can be inadequate and lead to further complications. These methods struggle with the complexity and heterogeneity of IoT systems, failing to provide robust and general solutions for real-time consistency assurance.

Chinese Academy of Sciences (CN), Institute of Software (CN)
Openalex Percentile: Top 84%
Semantic Web and Ontologies
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.