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Joint Status Sampling and Updating for Minimizing Age of Information in the Internet of Things (1807.04356v2)

Published 11 Jul 2018 in cs.IT, cs.NI, and math.IT

Abstract: The effective operation of time-critical Internet of things (IoT) applications requires real-time reporting of fresh status information of underlying physical processes. In this paper, a real-time IoT monitoring system is considered, in which the IoT devices sample a physical process with a sampling cost and send the status packet to a given destination with an updating cost. This joint status sampling and updating process is designed to minimize the average age of information (AoI) at the destination node under an average energy cost constraint at each device. This is formulated as an infinite horizon average cost constrained Markov decision process (CMDP) and transformed into an unconstrained MDP using a Lagrangian method. For the single IoT device case, the optimal policy for the CMDP is shown to be a randomized mixture of two deterministic policies for the unconstrained MDP, which is of threshold type. Then, a structure-aware optimal algorithm to obtain the optimal policy of the CMDP is proposed and the impact of the wireless channel dynamics is studied while demonstrating that channels having a larger mean channel gain and less scattering can achieve better AoI performance. For the case of multiple IoT devices, a low-complexity distributed suboptimal policy is proposed with the updating control at the destination and the sampling control at each device. Then, an online learning algorithm is developed to obtain this policy, which can be implemented at each IoT device and requires only the local knowledge and small signaling from the destination. The proposed learning algorithm is shown to converge almost surely to the suboptimal policy. Simulation results show the structural properties of the optimal policy for the single IoT device case; and show that the proposed policy for multiple IoT devices outperforms a zero-wait baseline policy, with average AoI reductions reaching up to 33%.

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Authors (2)
  1. Bo Zhou (244 papers)
  2. Walid Saad (378 papers)
Citations (176)
View on Semantic Scholar

Summary

Joint Status Sampling and Updating for Minimizing Age of Information in the Internet of Things

The paper "Joint Status Sampling and Updating for Minimizing Age of Information in the Internet of Things", authored by Bo Zhou and Walid Saad, addresses a critical issue in the field of IoT networks—maintaining up-to-date status information at a destination node to ensure efficient operation of IoT systems. The authors propose a framework to optimize the Age of Information (AoI) metric, which measures the freshness of data received from IoT devices. This metric is crucial for time-sensitive applications such as sensor networks and smart transportation.

Summary of Contributions

The paper primarily focuses on optimizing the trade-offs between AoI and energy consumption during the processes of status sampling and updating. To achieve this, the authors formulate the problem as a constrained Markov Decision Process (CMDP) and further transform it into an unconstrained MDP using the Lagrangian method. The paper delineates optimal policies for both single and multiple IoT devices under different costs associated with sampling and updating.

  1. Single IoT Device: For single devices, the optimal sampling and updating policy is proven to be threshold-based. This threshold policy reveals how devices should decide when to sample and update, given their current AoI state and energy costs. The authors derive structural properties of the value function and propose a structure-aware optimal algorithm to compute the best policy efficiently. The paper discloses that channels with larger mean channel gain and less scattering perform better in maintaining lower AoI.
  2. Multiple IoT Devices: The problem becomes more complex when multiple devices are involved, due to potential collisions in data transmission. The authors employ a CMDP formulation and propose a low-complexity semi-distributed suboptimal solution based on linear approximation of Q-factors. This suboptimal policy is determined using an innovative online learning algorithm, which allows devices to acquire their per-device Q-factors locally without requiring extensive knowledge exchange.

Numerical Results

The simulation results showcase the effectiveness of the proposed methods. In particular, they demonstrate up to a 33% reduction in average AoI compared to baseline policies, highlighting the efficiency of the threshold-based structure and the semi-distributed suboptimal policy for multiple devices.

Implications and Future Directions

This research contributes significantly to understanding how IoT systems can intelligently manage sampling and updating processes amidst constraints. The threshold policy provides a clear guideline for practical implementation, aiming for energy-efficient data freshness maintenance. The demonstrated efficiency of the proposed algorithm in reducing AoI prompts further exploration of similar methodologies in broader scopes of multi-agent IoT networks.

The paper suggests several avenues for future exploration, such as enriching the linear approximation analysis of Q-factors in CMDPs and investigating grant-free transmission protocols. Additionally, as IoT infrastructures grow and diversify, the scalability of these proposed solutions under varied network topologies remains a promising area for further research.

In summary, Zhou and Saad's work offers concrete strategies to improve IoT system performance through a disciplined approach to monitoring AoI, balancing computational demands and freshness of data with stringent energy constraints.