Control of Status Updates for Energy Harvesting Devices That Monitor Processes with Alarms

Abstract

In this work, we derive optimal transmission policies in an energy harvesting status update system. The system monitors a stochastic process which can be either in a normal or in an alarm state of operation. We capture the freshness of status updates for each state of the stochastic process by introducing two Age of Information (AoI) variables and extend the definition of AoI to account for the state changes of the stochastic process. We formulate the problem at hand as a Markov Decision Process which, under the assumption that the demand for status updates is higher when the stochastic process is in the alarm state, utilizes a transition cost function that applies linear and nonlinear penalties based on AoI and the state of the stochastic process. Finally, we evaluate numerically the derived policies and illustrate their effectiveness for reserving energy in anticipation of future alarm states.

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

“…The objective of this line of research was to investigate ageoptimal offline/online policies for update packet transmissions subject to the energy causality constraint at the source under various assumptions regarding the battery size, transmission time of update packets and channel modeling. 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.…”

“…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].…”

“…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]. A common model of the energy harvesting process in [34]- [45] is an external point process (e.g., Poisson process) independent from all the system design parameters.…”

Age of Information Aware Communication Systems : Modeling and Performance Analysis

“…In [49,50] effective age is loosely defined as an age-related metric that captures both the information structure of the signal and the sampling pattern that is used and is minimized when the error is minimized. In [51] the authors capture the freshness of status updates for different states of the observed stochastic process by introducing different AoI variables to account for the state changes. In [52] the term relative age of information (rAoI) is used to represent the AoI observed at the receiver relative to the AoI at the transmitter, in a point-to-point communication system.…”

The Cost of Delay in Status Updates and Their Value: Non-Linear Ageing

“…A wireless network with heterogeneous traffic is considered in [28]- [30]. Another control policy is to assume that the source is monitoring the network servers' idle/busy state and is able to generate status updates at any time, as in [31]- [34]. Aging control policies in hybrid networks are studied in [35], [36].…”