Statistical Maximum Flow Guarantee for Evolving Wireless Backscatter Networks

With the merit of self-sustainability, ambient backscatter aided wireless networks (AmBWNs) have attracted considerable attention for the potential application in Internet of Everything. The ambient backscatter transmission significantly reduces the power consumption by reflecting or absorbing ambient RF signals to transmit at the cost of fragile performance guarantee. The dual-mode node which can transmit in active mode (ATM) or backscatter mode (BTM) has been proposed to improve both energy efficiency and performance reliability. In the AmBWN composed of dual-mode nodes, information flows and energy flows coexist and are transferable. Existing maximum flow algorithms designed for static networks absence of frequent node and link state transitions are not suitable for AmBWNs. Therefore, we investigate the statistical guarantee of the maximum flow in evolving AmBWNs with energy constraint, flow conservation and Markov inequality constraint. The simulation result shows that the proposed statistical maximum flow algorithm can improve the energy efficiency by 2.5 times under poor channel condition.