It is shown that the TPWL model provides accurate results when the controls (bottom hole pressures of the production wells in this case) applied in test simulations are within the general range of the controls applied in the training runs, even though the well pressure schedules for the test runs can differ significantly from those of the training runs.
The pressure schedule, the overall forming time and the final thickness distribution in the formed component were calculated.
However, there is a lack of fundamental understanding on the interactions between the incentive mechanisms and the underlying protocols (e.g., shortest-path routing, ETX routing or back-pressure scheduling), and whether integration of these protocols will lead to a robust network, i.e., networks can sustain a given traffic workload.
We prove that VBR can maintain back-pressure scheduling's throughput optimality.
To ease the understanding, in here, we briefly introduce how back-pressure scheduling works as follows.
The back-pressure scheduling algorithm was proposed in [1], and it works as follows.
The second mechanism is backward-pressure scheduling which restricts the forwarding node from sending more packets to its downstream node.
Further, only λ that is within the network capacity region is considered in the context of back-pressure scheduling.
Further, in [15], Ying et al. combined back-pressure scheduling and shortest path routing with an expectation to shorten the path length under back-pressure-based routing.
In this section, we detail our framework of distributed back-pressure scheduling with opportunistic routing for a CRN with multiple simultaneous flows.
In this paper, we design a new virtual queue-based back-pressure scheduling algorithm (VBR) for achieving significant delay reduction in wireless sensor networks (WSN).
