Minimizing the Age of Information From Sensors With Common Observations

Abstract

We study the average Age of Information (AoI) in a system where physical sources produce independent discretetime updates that are each observed by several sensors. We devise a model that is simple, but still capable to capture the main tradeoffs. Two sensor scheduling policies are proposed to minimize the AoI of the sources; one in which the system parameters are assumed known, and one in which they are learned. Both policies are able to exploit the common sensor information to reduce the AoI, resulting in large reductions in AoI compared to common schedules.

Energy-Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space

“…We were the first to propose taking advantage of fresher information from correlated sources to conserve the energy of battery-powered sources by prolonging the times between sources' consecutive updates [4]. Subsequently, the authors in [8] and [9] analyzed from different perspectives a system with correlated sources. In [8], the authors consider a system in which multiple sources may observe the same information captured by sensors, i.e., multiple sources can obtain the same status update.…”

“…Subsequently, the authors in [8] and [9] analyzed from different perspectives a system with correlated sources. In [8], the authors consider a system in which multiple sources may observe the same information captured by sensors, i.e., multiple sources can obtain the same status update. In [9], the authors considered a one-dimensional static random field from which multiple sources transmitted observations.…”

“…In [9], the authors considered a one-dimensional static random field from which multiple sources transmitted observations. The works in [4], [8], [9] considered only the impact of correlation on the timeliness of information to establish the desired frequency of updates. This paper presents a solution that considers both the timeliness of the information and the energy available to the sources to arrive at a scheduling of information updates that prolongs the lifetime of battery-powered sensors.…”

On the Age of Information in Internet of Things Systems with Correlated Devices

“…Recently, the works in [21] and [22] considered remote estimation systems with spatially correlated processes and proposed updating strategies to improve the estimation error. For monitoring systems with common observations, the work in [23] proposed scheduling policies to minimize the average AoI and the work in [24] analyzed the average AoI from a queueing theoretic perspective. However, the authors in [23] and [24] assume that the AoI at the destination changes if a fresh status update from only one device is delivered.…”

“…For monitoring systems with common observations, the work in [23] proposed scheduling policies to minimize the average AoI and the work in [24] analyzed the average AoI from a queueing theoretic perspective. However, the authors in [23] and [24] assume that the AoI at the destination changes if a fresh status update from only one device is delivered. In [25], the authors study joint node assignment and scheduling to minimize the maximum peak AoI for wireless camera networks with multi-view processing.…”