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In large scale cloud deployments individual VMs operate under a range of computation and communication constraints.
Such an assumption ignores the spatiotemporal relationships between the traffic flow dynamics and V2V communication constraints.
Such side-effects include communication constraints, variable sampling, time-varying transmission delays, packet dropouts, and quantizations.
This paper is concerned with the distributed synchronization control of complex networks with communication constraints.
Major advantages of such an approach are the relaxation of communication constraints and increased fuel efficiency.
The method is simple to implement under realistic communication constraints and requires minimal assumptions on the field.
In practice, the synchrophasor system as a physical network involves communication constraints, such as data quality and data security.
Hence, in this contribution, we will concentrate on estimating the source distribution f using sensor networks with underlying communication constraints.
An efficient scheme in such cases requires performance optimization that jointly considers both the sensing and communication constraints.
We consider the stochastic perturbation, exogenous disturbance and the network-induced communication constraints in a whole framework of SMC design.
This paper is concerned with the optimal linear estimation problem for discrete time-varying networked systems with communication constraints.
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