"Maximizing Sampling Data Upload in Ambient Backscatter Assi

"Maximizing Sampling Data Upload in Ambient Backscatter Assisted Wirele" by Ying Liu, Kwan Wu Chin et al.

This paper studies a novel problem that aims to maximize the number of uploaded samples by devices in wireless powered Internet of Things (IoTs) 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.

Related Keywords

, Integer Linear Program , Mixed Integer Linear Program , Ambient Backscatter Communications , Backscatter , Data Communication , Nternet Of Things , Rink Schedule , Ptimization , Radio Frequency , Delays , Sampling , Wireless Communication , Ireless Powered Networks , Wireless Sensor Networks , முழு நேரியல் ப்ரோக்ர்யாம் , கலப்பு முழு நேரியல் ப்ரோக்ர்யாம் ,

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