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

Abstract

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.

Semantic-aware Sampling and Transmission in Real-time Tracking Systems: A POMDP Approach

“…The work [33] provided an AoII-optimal transmission policy in a system with a multi-state symmetric Markov source and a monitor subject to a hybrid automatic repeat request protocol and a resource constraint; they proposed a constrained MDP approach to find an optimal policy. In contrast to AoII studies in centralized settings (e.g., [23]), the authors of [14] optimized AoII in a decentralized setting where multiple sensors, each monitoring a Markov source, send their state to a monitor through a shared slotted ALOHA random access channel. Particularly, they provided a heuristic policy for which a non-convex optimization problem was formulated and approximately solved using a gradient-based algorithm.…”

Query-Age-Optimal Scheduling under Sampling and Transmission Constraints

“…The work [33] provided an AoII-optimal transmission policy in a system with a multi-state symmetric Markov source and a monitor subject to a hybrid automatic repeat request protocol and a resource constraint; they proposed a constrained MDP approach to find the optimal policy. In contrast to AoII studies in centralized settings (e.g., [23]), the authors of [18] optimized AoII in a decentralized setting where multiple sensors, each monitors a Markov source, send their state to a monitor through a shared slotted ALOHA random access channel. Particularly, they provided a heuristic policy for which a non-convex optimization problem was formulated and approximately solved using a gradient-based algorithm.…”

“…From (22), we can observe that for sufficiently large values of the AoI upper bound N , the belief approaches 0.5 exponentially fast as θ(t) increases. Thus, for an appropriate choice of N , the belief MDP problem can be reformulated by a finite-state MDP by replacing the belief b(t) from the state definition (18) with θ(t) ∈ {1, 2, . .…”

Query-Age-Optimal Scheduling Under Sampling and Transmission Constraints

“…This could be practically limited due to continuous sampling costs or even impossible due to, e.g., insufficient energy to make sampling at each time, as is often the case in energy harvesting systems. Even though [12] and its extensions [13], [17] do not base the AoII optimization on a fully observable source, there is no sampling cost involved, and it is assumed that the process of interest can be sampled at any given time upon request (from the monitor); thus, each sensor (that senses the underlying source process) needs to always remain active to listen for requests, which can quickly deplete its batteries [14].…”