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It is often possible to greatly reduce the central communication burden by allowing agents to communicate with each other in a non-hierarchical manner.
The consensus protocol included in the synchronization component of the controller addresses the communication burden by transmitting output signals to its communicating systems instead of entire states and further, adapts the synchronization weights in proportion to the pairwise state disagreement, as a means to minimize the control effort.
For multi-sensor network communications, a multiple event-triggered mechanism is employed firstly, which can reduce communication burden of each channel.
The optimality, however, comes with a high cost, both in communication burden and computation complexity.
For the coarse-grained parallel implementation the communication burden is not prohibitive.
But information exchange and fusion would inevitably bring about large computational complexity and communication burden.
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It can significantly alleviate the communication burdens and therefore can be beneficial to the real time applications.
Therefore, there is a need to develop optimized deep learning strategies to achieve resource efficiency and reduce communication burdens inside the MPIs.
For both communication networks event-triggers are utilized to reduce the communicational burden.
Thus, the performance of simple rules such as the CR will suffer degradation and sophisticated rules such as the CVR will burden the network with large communication load.
Furthermore, the relative angular velocity information between neighbouring rigid bodies is not required, such that the communication network burden can be further reduced.
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