Impact of Data Freshness on Remote Inference Performance

Główne pojęcia

The freshness of data, measured by the Age of Information (AoI), can have a non-monotonic impact on the performance of remote inference systems. While fresher data is not always better, the inference error is a function of the AoI, and this function can be non-monotonic.

Streszczenie

The paper analyzes the impact of data freshness, measured by the Age of Information (AoI), on the performance of remote inference systems. Remote inference systems use a pre-trained neural network to infer time-varying targets (e.g., vehicle and pedestrian locations) based on features (e.g., video frames) observed at a sensing node.
The key insights are:

Experimental results show that the inference error does not always degrade monotonically as the data becomes stale. In some scenarios, even the fresh data with AoI = 0 may generate a larger inference error than stale data with AoI > 0.

The authors develop an information-theoretic analysis to interpret these counter-intuitive experimental results. They show that the assumption "fresh data is better than stale data" is true when the time-sequence data used for remote inference can be closely approximated as a Markov chain, but it is not true when the data sequence is far from Markovian. Hence, the inference error is a function of the AoI, where the function could be non-monotonic.

The authors propose a new "selection-from-buffer" model for sending the features, which is more general than the "generate-at-will" model used in earlier studies. They design low-complexity scheduling policies to improve inference performance, which can minimize general AoI functions (monotonic or non-monotonic).

For single-source and single-channel systems, an optimal scheduling policy is devised. For multi-source and multi-channel systems, the authors propose a new asymptotically optimal scheduling policy that integrates Whittle index-based source selection and duality-based feature selection-from-buffer algorithms.

The results hold for random delay channels and are applicable to signal-agnostic remote estimation when the training and inference data have the same probabilistic distribution.

Statystyki

The paper does not provide specific numerical data, but it presents the following key figures:

Fig. 1: Training and inference errors for video prediction, showing a monotonic increase with AoI.
Fig. 2: Training and inference errors for robot state prediction, showing a non-monotonic trend with AoI.
Fig. 3: Training and inference errors for actuator state prediction under mechanical response delay, showing a non-monotonic trend with AoI.
Fig. 4: Training and inference errors for temperature prediction, showing a non-monotonic trend with AoI.
Fig. 5: Training and inference errors for wireless channel state information (CSI) prediction, showing a non-monotonic trend with AoI.

Cytaty

"One might expect that the performance of a remote inference system degrades monotonically as the feature becomes stale. Using an information-theoretic analysis, we show that this is true if the feature and target data sequence can be closely approximated as a Markov chain, whereas it is not true if the data sequence is far from Markovian."
"Hence, the inference error is a function of Age of Information (AoI), where the function could be non-monotonic."

Kluczowe wnioski z

Timely Communications for Remote Inference

by Md Kamran Ch... o arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16281.pdf

Głębsze pytania

What are the potential applications of the proposed scheduling policies beyond remote inference, such as in other real-time systems that rely on timely information

The proposed scheduling policies for minimizing inference error in remote inference systems have the potential for applications beyond just remote inference. These scheduling policies can be applied to various real-time systems that rely on timely information, such as autonomous driving, factory automation, UAV navigation, and extended reality. In autonomous driving, for example, the scheduling policies can be used to optimize the transmission of sensor data for real-time decision-making by autonomous vehicles. In factory automation, the policies can help in optimizing the communication of data between different machines and systems to ensure efficient and timely operations. Similarly, in UAV navigation, the scheduling policies can be utilized to enhance the transmission of critical data for safe and accurate navigation. Overall, the scheduling policies can be adapted to a wide range of real-time systems where timely information processing is crucial for optimal performance.

How can the proposed information-theoretic analysis be extended to capture the impact of other factors, such as communication errors or network dynamics, on the performance of remote inference systems

The proposed information-theoretic analysis can be extended to capture the impact of various other factors on the performance of remote inference systems. One such factor is communication errors, which can significantly affect the quality of data transmission in remote inference systems. By incorporating error models into the analysis, the impact of communication errors on the freshness of data and the inference error can be quantified. Additionally, network dynamics, such as varying network conditions and congestion levels, can also be considered in the analysis. By modeling how network dynamics affect the transmission of data and the inference process, the information-theoretic analysis can provide insights into optimizing system performance under dynamic network conditions. By integrating these factors into the analysis, a more comprehensive understanding of the performance of remote inference systems in real-world scenarios can be achieved.

Can the insights from this work be leveraged to design more efficient feature extraction and representation techniques for remote inference tasks, especially when the target and feature data sequences exhibit non-Markovian characteristics

The insights from this work can be leveraged to design more efficient feature extraction and representation techniques for remote inference tasks, especially when the target and feature data sequences exhibit non-Markovian characteristics. One approach could be to develop adaptive feature extraction algorithms that can dynamically adjust the feature selection process based on the non-Markovian nature of the data sequences. By incorporating the insights from the information-theoretic analysis, feature extraction techniques can be optimized to capture the relevant information from the data sequences, taking into account the non-monotonic relationships between data freshness and inference error. Additionally, the analysis can guide the development of more sophisticated feature representation methods that can effectively encode the complex dependencies present in non-Markovian data sequences. By tailoring feature extraction and representation techniques to the specific characteristics of the data, the overall performance of remote inference systems can be improved.