Which theoretical models or frameworks are most widely applied in this field?
- Reinforcement Learning (Q-Learning/DRL): Applied for routing but does not scale efficiently
Q & A Forum
Literature Review & Gap Analyses
Q: Which theoretical models or frameworks are most widely applied in this field?
- Reinforcement Learning (Q-Learning/DRL): Applied for routing but does not scale efficiently
- Trust Models: Heuristically designed trust update rules; lacks formal theory
- Game Theory: The application to model adversarial behaviour and cooperation but lacks integration in learning
- Graph Theory/Graph neural networks: seldom used especially considering their potential in modelling dynamic topology
- Federated Trust Models: emerging but conceptually underdeveloped
Such reviews align with academic literature review standards and can benefit from research gap analysis service inputs.
Gap Insight: The existing theoretical instruments are highly obsolete or too narrowly applied and, thus, there is a strong case for creating new frameworks through developing hybrids or formalized new frameworks.