On-Demand AoI Minimization in Resource-Constrained Cache-Enabled IoT Networks With Energy Harvesting Sensors

Abstract

We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cacheenabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor. The edge node serves users' requests by deciding whether to command the corresponding sensor to send a fresh status update or retrieve the most recently received measurement from the cache. Our objective is to find the best actions of the edge node to minimize the average age of information (AoI) of the received measurements upon request, i.e., average on-demand AoI, subject to per-slot transmission and energy constraints. First, we derive a Markov decision process model and propose an iterative algorithm that obtains an optimal policy. Then, we develop an asymptotically optimal low-complexity algorithm -termed relaxthen-truncate -and prove that it is optimal as the number of sensors goes to infinity. Simulation results illustrate that the proposed relax-then-truncate approach significantly reduces the average on-demand AoI compared to a request-aware greedy policy and a weighted AoI policy, and also depict that it performs close to the optimal solution even for moderate numbers of sensors.Index Terms-Age of information (AoI), energy harvesting (EH), constrained Markov decision process (CMDP).

Status Updating with an Energy Harvesting Sensor under Partial Battery Knowledge

“…Differently, [3], [20]- [27] consider that the source nodes are powered by energy harvested from the environment; thus, AoI-aware scheduling is carried out under the energy causality constraint at the source nodes. Also, while the above works (implicitly) assume that time-sensitive information is needed at the destination at all time moments, [4]- [6], [28]- [31] study information freshness of the source(s) driven by users' requests.…”

“…, B}. We assume that measuring and transmitting a status update from the sensor to the edge node consumes one unit of energy (see, e.g., [4], [6], [15], [20], [21]). Thus, if the sensor is commanded to send an update (i.e., a(t) = 1), it can only do so if its battery is not empty (i.e., b(t) ≥ 1).…”

“…As the inevitable consequence of this status updating procedure, the edge node has only partial knowledge about the battery level at each slot, i.e., outdated knowledge based on the sensor's last update. It is worth emphasizing that considering this realistic setting is in stark contrast to the previous works on AoI-aware network designs (see e.g., [5], [6], [26], [28]) which all assume that perfect battery knowledge is available at the decision-maker (herein, the edge node) at each slot.…”

“…This introduces an inherent trade-off between the age of information (AoI) at the users and the energy consumption of the sensors. As the main novelty of our work compared to the related AoI-aware network designs [3]- [6], we consider a practical scenario where the edge node is informed of the sensors' battery levels only via the received status updates, leading to partial battery knowledge at the edge node. Particularly, our objective is to find the best actions of the edge node to minimize the average AoI of the served measurements, i.e., average on-demand AoI.…”

Version Age-Optimal Cached Status Updates in a Gossiping Network with Energy Harvesting Sensor

“…The monitoring node aims to choose an optimal action (requesting or remaining idle) at each time slot in order to minimize the average AoI by formulating an MDP problem. The authors in [15] consider the minimization of a request-based AoI (on-demand AoI) problem subject to the energy causality and transmission constraints. This problem is formulated as an MDP for a multi-user multisensor IoT EH sensing network.…”