Timely Updates in Energy Harvesting Two-Hop Networks: Offline and Online Policies

Abstract

A two-hop energy harvesting communication network is considered, in which measurement updates are transmitted by a source to a destination through an intermediate relay. Updates are to be sent in a timely fashion that minimizes the age of information, defined as the time elapsed since the most recent update at the destination was generated at the source. The source and the relay communicate using energy harvested from nature, which is stored in infinite-sized batteries. Both nodes use fixed transmission rates, and hence updates incur fixed delays (service times). Two problems are formulated:an offline problem, in which the energy arrival information is known a priori, and an online problem, in which such information is revealed causally over time. In both problems, it is shown that it is optimal to transmit updates from the source just in time as the relay is ready to forward them to the destination, making the source and the relay act as one combined node. A recurring theme in the optimal policy is that updates should be as uniformly spread out over time as possible, subject to energy causality and service time constraints. This is perfectly achieved in the offline setting, and is achieved almost surely in the online setting by a best effort policy. I. INTRODUCTIONDeveloping low-latency communication policies is a crucial requirement for next generation communication networks, especially in applications pertaining to real-time status monitoring where information needs to be kept as fresh as possible at interested destinations. The age of information (AoI) metric has been recently introduced in the literature as a suitable performance

Timely Status Updating Over Erasure Channels Using an Energy Harvesting Sensor: Single and Multiple Sources

“…One can show that ρ noFB q,1 is given by λ * that solves p noFB (λ * ) = 0, and that such λ * is unique since p noFB (λ) is decreasing in λ [48]. The next theorem characterizes the solution of problem (7). The proof is in Appendix B.…”

AoI Minimization in Status Update Control With Energy Harvesting Sensors

“…We consider a common assumption (see e.g., [20], [22], [23], [28], [37]) that transmitting a status update from each sensor to the edge node consumes one unit of energy.…”

“…(i) In order to prove that v * k (s) is non-decreasing with respect to the AoI, we define two states s = {b, ∆} and s = {b, ∆}, where ∆ ≥ ∆, and show that v * k (s) ≥ v * k (s). According to (22), it suffices to prove that v * k (s) (n) ≥ v * k (s) (n) , ∀n. We prove this by mathematical induction.…”

“…In [21], the authors explored the benefits of erasure status feedback for online timely updating for an EH sensor with a unit-sized battery. Age-optimal transmission policies for EH two-hop networks were investigated in [22].…”

A Reinforcement Learning Framework for Optimizing Age of Information in RF-Powered Communication Systems

“…Specifically, the infinite battery capacity case was studied in [34]- [37], [44] whereas [38]- [43], [45], [46] considered the case of finite battery capacity. Different from [36]- [41] where it was assumed that each update packet could be transmitted to the destination instantly subject to the energy causality constraint, [34], [43], [44] considered stochastic transmission time and [35], [45], [46] studied the non-zero fixed transmission time case. While [34]- [36], [38]- [42], [45] considered error-free channel models, i.e., every update packet transmission is successfully received at the destination, a noisy channel model was considered in [37], [43], [44], [46].…”