An Improved Model of Greedy Tasks Assignment in Distributed Robotic Systems

Abstract

In the current paper the improved model for computational tasks distribution efficiency estimation is presented and discussed. Taking into account the tight connection between distributed robotic systems and IoT concepts, including fog and edge, the problem of computational resource spending is considered. As the failure rate of the node depends on the workload, we consider the strategy, when each node chooses to transmit or to process data. The model developed allows to implement the greedy strategy of tasks distribution, in which every node choses the best individual state. Also some selected experimental results are presented, pros and cons of such greedy approach are discussed.