Status Updating Under Partial Battery Knowledge in Energy Harvesting IoT Networks

Abstract

Abstract

We study status updating under inexact knowledge about the battery levels of the energy harvesting sensors in an IoT network, where users make on-demand requests to a cache-enabled edge node to send updates about various random processes monitored by the sensors. To serve the request(s), the edge node either commands the corresponding sensor to send an update or uses the aged data from the cache. We find a control policy that minimizes the average on-demand AoI subject to per-slot energy harvesting constraints under partial battery knowledge at the edge node. Namely, the edge node is informed about sensors’ battery levels only via received status updates, leading to uncertainty about the battery levels for the decision-making. We model the problem as a POMDP which is then reformulated as an equivalent belief-MDP. The belief-MDP in its original form is difficult to solve due to the infinite belief space. However, by exploiting a specific pattern in the evolution of beliefs, we truncate the belief space and develop a dynamic programming algorithm to obtain an optimal policy. Moreover, we address a multi-sensor setup under a transmission limitation for which we develop an asymptotically optimal algorithm. Simulation results assess the performance of the proposed methods.