Optimizing Information Freshness in RF-Powered Multi-Hop Wireless Networks

Abstract

Many applications operating in the Internet of Things (IoT) require timely and fair data collection from devices. This has motivated research into a new metric called Age of Information (AoI). This paper contributes to this effort by proposing to minimize the maximum average AoI (min-max AoI) in a multi-hop IoT network comprising of solar-powered Power Beacons (PBs). It outlines a Mixed Integer Linear Program (MILP) that jointly optimizes: (i) the beamforming vector used by PBs to charge devices, and (ii) routing, which determines how samples from devices are forwarded to a sink node, and (iii) the sampling time of sources. It also presents two protocols: Centralized Linear Relaxation (CLR) and Distributed Path Selection (DPS), respectively. CLR is run by the sink to determine the transmit power of PBs and the path of each source using two Linear Programs (LPs). On the other hand, DPS is a distributed approach whereby PBs and sources make their own decisions using local information. Our simulation results show that min-max AoI increases with the number of sources, but reduces with increasing number of PBs. The number of paths available to a source, the number of frames, and solar panel size have limited impact on performance. The min-max AoI of CLR and DPS is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.60\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.95\times $ </tex-math></inline-formula> higher than that of MILP.