TY - JOUR
T1 - Age-Minimal Transmission for Energy Harvesting Sensors with Finite Batteries
T2 - Online Policies
AU - Arafa, Ahmed
AU - Yang, Jing
AU - Ulukus, Sennur
AU - Poor, H. Vincent
N1 - Funding Information:
Manuscript received June 17, 2018; revised May 9, 2019; accepted August 11, 2019. Date of publication September 2, 2019; date of current version December 23, 2019. This work was supported by the National Science Foundation under Grant CCF-0939370, Grant CCF-1513915, Grant ECCS-1650299, Grant CNS-1526608, and Grant ECCS-1807348. This article was presented in part at the 2018 Information Theory and Applications Workshop [1] and in part at the 2018 International Conference on Communications [2].
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2020/1
Y1 - 2020/1
N2 - An energy-harvesting sensor node that is sending status updates to a destination is considered. The sensor is equipped with a battery of finite size to save its incoming energy, and consumes one unit of energy per status update transmission, which is delivered to the destination instantly over an error-free channel. The setting is online in which the harvested energy is revealed to the sensor causally over time after it arrives, and the goal is to design status update transmission times (policy) such that the long term average age of information (AoI) is minimized. The AoI is defined as the time elapsed since the latest update has reached at the destination. Two energy arrival models are considered: a random battery recharge (RBR) model, and an incremental battery recharge (IBR) model. In both models, energy arrives according to a Poisson process with unit rate, with values that completely fill up the battery in the RBR model, and with values that fill up the battery incrementally in a unit-by-unit fashion in the IBR model. The key approach to characterizing the optimal status update policy for both models is showing the optimality of renewal policies, in which the inter-update times follow a renewal process in a certain manner that depends on the energy arrival model and the battery size. It is then shown that the optimal renewal policy has an energy-dependent threshold structure, in which the sensor sends a status update only if the AoI grows above a certain threshold that depends on the energy available in its battery. For both the random and the incremental battery recharge models, the optimal energy-dependent thresholds are characterized explicitly, i.e., in closed-form, in terms of the optimal long term average AoI. It is also shown that the optimal thresholds are monotonically decreasing in the energy available in the battery, and that the smallest threshold, which comes in effect when the battery is full, is equal to the optimal long term average AoI.
AB - An energy-harvesting sensor node that is sending status updates to a destination is considered. The sensor is equipped with a battery of finite size to save its incoming energy, and consumes one unit of energy per status update transmission, which is delivered to the destination instantly over an error-free channel. The setting is online in which the harvested energy is revealed to the sensor causally over time after it arrives, and the goal is to design status update transmission times (policy) such that the long term average age of information (AoI) is minimized. The AoI is defined as the time elapsed since the latest update has reached at the destination. Two energy arrival models are considered: a random battery recharge (RBR) model, and an incremental battery recharge (IBR) model. In both models, energy arrives according to a Poisson process with unit rate, with values that completely fill up the battery in the RBR model, and with values that fill up the battery incrementally in a unit-by-unit fashion in the IBR model. The key approach to characterizing the optimal status update policy for both models is showing the optimality of renewal policies, in which the inter-update times follow a renewal process in a certain manner that depends on the energy arrival model and the battery size. It is then shown that the optimal renewal policy has an energy-dependent threshold structure, in which the sensor sends a status update only if the AoI grows above a certain threshold that depends on the energy available in its battery. For both the random and the incremental battery recharge models, the optimal energy-dependent thresholds are characterized explicitly, i.e., in closed-form, in terms of the optimal long term average AoI. It is also shown that the optimal thresholds are monotonically decreasing in the energy available in the battery, and that the smallest threshold, which comes in effect when the battery is full, is equal to the optimal long term average AoI.
KW - Age-of-information (AoI)
KW - energy harvesting
KW - finite battery
KW - online scheduling
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U2 - 10.1109/TIT.2019.2938969
DO - 10.1109/TIT.2019.2938969
M3 - Article
AN - SCOPUS:85077231378
SN - 0018-9448
VL - 66
SP - 534
EP - 556
JO - IEEE Transactions on Information Theory
JF - IEEE Transactions on Information Theory
IS - 1
M1 - 8822722
ER -