In this discussion, the effect of the node mobility is taken into account at the fusion center decision updating.
In the second model, nodes take the effect of the node mobility into account for node-level decision updating.
Each person (social or regular) executes the attention and currency update mechanisms followed by mechanisms for making decision, updating memory, resetting density and granting reward.
Decision update Researchers are exploring the use of online or incremental learning approaches to improve the decision boundary of the classifiers even in deployment phase[32, 33].
Specifically, we develop two decision-fusion architectures for detection where in the first one, impact of node mobility is taken into account for decisions updating at the fusion center, while in the second model the impact of node mobility is taken at the node level decision updating.
Specifically, we develop two decision fusion models to make the final decision where in the first model, the impact of the node mobility is taken into account to update the decision at the fusion center, while in the second model, the impact of node mobility is taken at the node-level decision updating.
Specifically, we propose two decision fusion architectures where in the first one, the effect of the node mobility is taken into account for the decision updating at the fusion center, and nodes make binary decisions based on the observations during one step movement.
Since the final sampling frequency was 100 Hz, each decision update should be given in less than 10 ms to provide a real-time feedback.
The modular aspect facilitates access to evidence and decisions, updates, and database development.
Then, this strategy is applied to the case where the subject's decision system updates the information for making a decision.
