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We study the real-time remote tracking of a two-state Markov process by an energy harvesting source. The source decides whether to transmit over an unreliable channel based on the state. We formulate this scenario as a Markov decision process (MDP) to determine the optimal transmission policy that minimizes the average Version Innovation Age (VIA) as a performance metric. We demonstrate that the optimal transmission policy is threshold-based, determined by the battery level, source state, and VIA value. We numerically verify the analytical structure of the optimal policy and compare the performance of our proposed policy against two baseline policies across various system parameters, establishing the superior performance of our approach.

We consider the problem of real-time remote monitoring of a two-state Markov process, where a sensor observes the state of the source and makes a decision on whether to transmit the status updates over an unreliable channel or not. We introduce a modified randomized stationary sampling and transmission policy where the decision to perform sampling occurs probabilistically depending on the current state of the source and whether the system was in a sync state during the previous time slot or not. We then propose two new performance metrics, coined the Version Innovation Age (VIA) and the Age of Incorrect Version (AoIV) and analyze their performance under the modified randomized stationary and other state-of-the-art sampling and transmission policies. Specifically, we derive closed-form expressions for the distribution and the average of VIA, AoIV, and Age of Incorrect Information (AoII) under these policies. Furthermore, we formulate and solve three constrained optimization problems. The first optimization problem aims to minimize the average VIA subject to constraints on the time-averaged sampling cost and time-averaged reconstruction error. In the second and third problems, the objective is to minimize the average AoIV and AoII, respectively, while considering a constraint on the time-averaged sampling cost. Finally, we compare the performance of various sampling and transmission policies and identify the conditions under which each policy outperforms the others in optimizing the proposed metrics., Comment: arXiv admin note: substantial text overlap with arXiv:2401.17691

In this paper, we propose two new performance metrics, coined the Version Innovation Age (VIA) and the Age of Incorrect Version (AoIV) for real-time monitoring of a two-state Markov process over an unreliable channel. We analyze their performance under the change-aware, semantics-aware, and randomized stationary sampling and transmission policies. We derive closed-form expressions for the distribution and the average of VIA, AoIV, and AoII for these policies. We then formulate and solve an optimization problem to minimize the average VIA, subject to constraints on the time-averaged sampling cost and time-averaged reconstruction error. Finally, we compare the performance of various sampling and transmission policies and identify the conditions under which each policy outperforms the others in optimizing the proposed metrics.

The problem of real-time remote tracking and reconstruction of a two-state Markov process is considered here. A transmitter sends samples from an observed information source to a remote monitor over an unreliable wireless channel. The receiver, in turn, performs an action according to the state of the reconstructed source. We propose a state-aware randomized stationary sampling and transmission policy which accounts for the importance of different states of the information source, and their impact on the goal of the communication process. We then analyze the performance of the proposed policy, and compare it with existing goal-oriented joint sampling and transmission policies, with respect to a set of performance metrics. Specifically, we study the real-time reconstruction error, the cost of actuation error, the consecutive error, and a new metric, coined importance-aware consecutive error. In addition, we formulate and solve a constrained optimization problem that aims to obtain the optimal sampling probabilities that minimize the average cost of actuation error. Our results show that in the scenario of constrained sampling generation, the optimal state-aware randomized stationary policy outperforms all other sampling policies for fast evolving sources, and, under certain conditions, for slowly varying sources. Otherwise, a semantics-aware policy performs better only when the source is slowly varying., Comment: arXiv admin note: text overlap with arXiv:2302.13927

In this work, we study the real-time tracking and reconstruction of an information source with the purpose of actuation. A device monitors the state of the information source and transmits status updates to a receiver over a wireless erasure channel. We consider two models for the source, namely an $N$-state Markov chain and an $N$-state Birth-Death Markov process. We investigate several joint sampling and transmission policies, including a semantics-aware one, and we study their performance with respect to a set of metrics. Specifically, we investigate the real-time reconstruction error and its variance, the cost of actuation error, the consecutive error, and the cost of memory error. These metrics capture different characteristics of the system performance, such as the impact of erroneous actions and the timing of errors. In addition, we propose a randomized stationary sampling and transmission policy and we derive closed-form expressions for the aforementioned metrics. We then formulate two optimization problems. The first optimization problem aims to minimize the time-averaged reconstruction error subject to time-averaged sampling cost constraint. Then, we compare the optimal randomized stationary policy with uniform, change-aware, and semantics-aware sampling policies. Our results show that in the scenario of constrained sampling generation, the optimal randomized stationary policy outperforms all other sampling policies when the source is rapidly evolving. Otherwise, the semantics-aware policy performs the best. The objective of the second optimization problem is to obtain an optimal sampling policy that minimizes the average consecutive error with a constraint on the time-averaged sampling cost. Based on this, we propose a \emph{wait-then-generate} sampling policy which is simple to implement., Comment: Submitted for possible journal publication. arXiv admin note: substantial text overlap with arXiv:2302.01132

In this work, we study the problem of real-time tracking and reconstruction of an information source with the purpose of actuation. A device monitors an $N$-state Markov process and transmits status updates to a receiver over a wireless erasure channel. We consider a set of joint sampling and transmission policies, including a semantics-aware one, and we study their performance with respect to relevant metrics. Specifically, we investigate the real-time reconstruction error and its variance, the consecutive error, the cost of memory error, and the cost of actuation error. Furthermore, we propose a randomized stationary sampling and transmission policy and derive closed-form expressions for all aforementioned metrics. We then formulate an optimization problem for minimizing the real-time reconstruction error subject to a sampling cost constraint. Our results show that in the scenario of constrained sampling generation, the optimal randomized stationary policy outperforms all other sampling policies when the source is rapidly evolving. Otherwise, the semantics-aware policy performs the best., Comment: IEEE ICC 2023 Workshop on Semantic Communications