.png){kind=icon}
Return
Collaborative Data Aggregation Algorithm Considering User Preferences for Opportunistic Edge Computing
.png){kind=icon}
PRE
AI
DOI：10.1002/cpe.70302.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In dynamic and unpredictable environments, opportunistic edge computing has emerged as a novel paradigm. It aims to leverage temporarily available computational resources to enable efficient data processing. To tackle the challenges of mobile group collaborative data aggregation in such contexts, this paper proposes a Multi-Preference Redundant Data Collaborative Aggregation algorithm (MPRDCA), which jointly optimizes channel state and data preference weighting, and pioneers three key innovations: (a) a novel binary decision factor integrating dynamic user preferences with channel states, (b) a time-varying utility function incorporating preference weights, and (c) Lyapunov-based energy constraint transformation. Performance was rigorously evaluated via parameterized stochastic simulations incorporating probabilistic channel states and hierarchical preference coefficients, modeling a dynamic edge group. Key results demonstrate that under stringent energy constraints, MPRDCA achieves higher overall transmission utility while enhancing effective transmission volume of the most urgently required data by up to 41.99% compared to benchmarks. Theoretical and empirical results validate MPRDCA as a highly adaptive solution for resource-constrained edge scenarios, achieving optimal utility-efficiency trade-offs.
Keywords:
collaborative group
data aggregation
edge computing
online distributed optimization
Journal
C
IF:
1.5
Papers:
473
Citations:
0
Organization
Cited Papers
Reliable cluster based data aggregation scheme for IoT network using hybrid deep learning techniques
OAAI
.png){kind=icon}
OAAIShareSave
ShareSave
View more