Minimizing the Age of Incorrect Information for Real-time Tracking of Markov Remote Sources

Kriouile, Saad; Assaad, Mohamad

doi:10.1109/isit45174.2021.9518209

Cited by 28 publications

(36 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A class is indexable if the set of states in which the passive action is the optimal action with respect to the optimal solution of Problem (10), increases with the Lagrangian parameter W . One can see [14] for a rigorous Definition of Indexability property as well as Whittle's index. We note that in the sequel, we precise the indices of users to differentiate between them.…”

Section: Indexability and Whittle's Index Expressionsmentioning

confidence: 99%

“…Having said that, instead of computing the exact value of the Age of incorrect information at each time, we need to estimate it by averaging over the different possible values of AoII as the monitor is unaware of the state of the remote sources as long as it doesn't receive the information from the sensors. The first work that studies this framework, specifically, the Partially Observable Markov Decision Process framework in the context of AoI, is [14]. In this work, the authors consider a multiple-transmitter-one-receiver scenario and aim to minimize the expected total average AoII by deriving the low complex and well-performing policy called Whittle's index policy which is optimal in the many-users regime.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the scheduler does not know the instantaneous state of the remote sources until it receives their updates. To that extent, as done in [14], we consider the mean of AoII at each time slot rather than the exact value of AoII since the monitor is unaware of current states of the remote sources before receiving their updates.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

When to pull data from sensors for minimum Distance-based Age of incorrect Information metric

Kriouile¹,

Assaad²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

The age of Information (AoI) has been introduced to capture the notion of freshness in real-time monitoring applications. However, this metric falls short in many scenarios, especially when quantifying the mismatch between the current and the estimated states. To circumvent this issue, in this paper, we adopt the age of incorrect of information metric (AoII) that considers the quantified mismatch between the source and the knowledge at the destination. We consider for that a problem where a central entity pulls the information from remote sources that evolve according to a Markovian Process. It selects at each time slot which sources should send their updates. As the scheduler does not know the real state of the remote sources, it estimates at each time the value of AoII based on the Markovian sources' parameters. Its goal is to keep the time average of AoII function as small as possible. We develop a scheduling scheme based on Whittle's index policy for that purpose. To that extent, we proceed by using the Lagrangian Relaxation Approach and establish that the dual problem has an optimal threshold policy. Building on that, we compute the expressions of Whittle's indices. Finally, we provide some numerical results to highlight the performance of our derived policy compared to the classical AoI metric.

show abstract

Section: Indexability and Whittle's Index Expressionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

When to pull data from sensors for minimum Distance-based Age of incorrect Information metric

Kriouile¹,

Assaad²

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [6], the authors analyze Whittle index-based heuristics for minimizing AoI, achieving asymptotic optimality when the number of users is large. Whittle index policies can also be used for AoII, as in [7], which considers a multi-sensor scenario with an infinite time horizon. Many recent works also involve the use of threshold based policies.…”

Section: Introductionmentioning

confidence: 99%

A Decentralized Policy for Minimization of Age of Incorrect Information in Slotted ALOHA Systems

Nayak¹,

Kalør²,

Chiariotti³

et al. 2023

Preprint

View full text Add to dashboard Cite

The Age of Incorrect Information (AoII) is a metric that can combine the freshness of the information available to a gateway in an Internet of Things (IoT) network with the accuracy of that information. As such, minimizing the AoII can allow the operators of IoT systems to have a more precise and up-to-date picture of the environment in which the sensors are deployed. However, most IoT systems do not allow for centralized scheduling or explicit coordination, as sensors need to be extremely simple and consume as little power as possible. Finding a decentralized policy to minimize the AoII can be extremely challenging in this setting. This paper presents a heuristic to optimize AoII for a slotted ALOHA system, starting from a threshold-based policy and using dual methods to converge to a better solution. This method can significantly outperform state-independent policies, finding an efficient balance between frequent updates and a low number of packet collisions.

show abstract

“…In [7], the authors study the problem of minimizing AoII when channel state information is available and the time penalty function is general. The authors of [8] consider a similar system, but the base station has no way of knowing the true state of the event before deciding to transmit. In the above papers, the update transmission time is constant, usually one time slot.…”

Section: Introductionmentioning

confidence: 99%

Preempting to Minimize Age of Incorrect Information under Transmission Delay

Ephremides¹

2022

Preprint

View full text Add to dashboard Cite

We consider the problem of optimizing the decision of a preemptive transmitter to minimize the Age of Incorrect Information (AoII) when the channel has a random delay. In the system, a transmitter observes a dynamic source and makes decisions based on the system status. When the channel is busy, the transmitter can choose whether to preempt to transmit a new update. When the channel is idle, the transmitter can choose whether or not to transmit a new update. We assume that the channel has a random delay and that this delay is independent and identically distributed for any update. At the other end of the channel is a receiver that estimates the state of the dynamic source based on the updates it receives. We adopt AoII to measure the performance of the system. Therefore, this paper aims to optimize the transmitter's action in each time slot to minimize AoII. We first use the Markov decision process to formulate the optimization problem and give the corresponding value iterative algorithm to obtain the optimal policy. However, the value iteration algorithm is computationally demanding, and some approximations are made to realize the algorithm. Hence, we theoretically analyze some canonical delay distributions and obtain the corresponding optimal policies by leveraging the policy improvement theorem.

show abstract

Minimizing the Age of Incorrect Information for Real-time Tracking of Markov Remote Sources

Cited by 28 publications

References 15 publications

When to pull data from sensors for minimum Distance-based Age of incorrect Information metric

When to pull data from sensors for minimum Distance-based Age of incorrect Information metric

A Decentralized Policy for Minimization of Age of Incorrect Information in Slotted ALOHA Systems

Preempting to Minimize Age of Incorrect Information under Transmission Delay

Contact Info

Product

Resources

About