An Efficient Framework for Peer Selection in Dynamic P2P Network Using Q Learning with Fuzzy Linear Programming

An Efficient Framework for Peer Selection in Dynamic P2P Network Using Q Learning with Fuzzy Linear Programming

This paper proposes a new approach to integrating Q learning into the fuzzy linear programming (FLP) paradigm to improve peer selection in P2P networks. Using Q learning, the proposed method employs real-time feedback to adjust and update peer selection policies. The FLP framework enriches this proc...

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Bibliographic Details
Main Authors: Mahalingam Anandaraj, Tahani Albalawi, Mohammad Alkhatib
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Journal of Sensor and Actuator Networks
Subjects:
Erdos–Renyi model
fuzzy linear programming
Q learning
P2P network
Q table
reinforcement learning
Online Access:https://www.mdpi.com/2224-2708/14/2/38
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