Maximizing Sampling Data Upload in Ambient Backscatter-Assisted Wireless-Powered Networks

This article studies a novel problem that aims to maximize the number of uploaded samples by devices in wireless-powered Internet of Things (IoT) networks. To do so, it takes advantage of ambient backscatter communications (AmBC) to help sensor devices conserve energy, and thus leaving them with more energy to collect samples. We outline a mixed-integer linear program (MILP) that aims to determine the operation mode of each device in each time slot in order to maximize the total amount of uploaded samples. We also present a heuristic approach to set the operation mode of devices based on their residual energy and data. Our results show that as compared to the case without AmBC, the total data uploaded by devices increases by 48% and 45% for the MILP and heuristic, respectively-both of which exploit AmBC.

Automated summary

This article studies a novel problem in wireless-powered IoT networks, aiming to maximize the number of uploaded samples by utilizing ambient backscatter communications (AmBC) to conserve energy. The mixed-integer linear program (MILP) and heuristic approach are proposed to set the operation mode of devices based on their residual energy and data, resulting in increased total data uploaded by devices compared to the case without AmBC.