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On the Role of Age of Information in the Internet of Things

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arxiv 1812.08286 v3 pith:GPSVX5MU submitted 2018-12-19 cs.IT cs.NImath.IT

classification cs.ITcs.NImath.IT
keywords devicesinformationpolicydesigndestinationenergyexplorefreshness-aware
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In this article, we provide an accessible introduction to the emerging idea of Age of Information (AoI) that quantifies freshness of information and explore its possible role in the efficient design of freshness-aware Internet of Things (IoT). We start by summarizing the concept of AoI and its variants with emphasis on the differences between AoI and other well-known performance metrics in the literature, such as throughput and delay. Building on this, we explore freshness-aware IoT design for a network in which IoT devices sense potentially different physical processes and are supposed to frequently update the status of these processes at a destination node (such as a cellular base station). Inspired by the recent interest, we also assume that these IoT devices are powered by wireless energy transfer by the destination node. For this setting, we investigate the optimal sampling policy that jointly optimizes wireless energy transfer and scheduling of update packet transmissions from IoT devices with the goal of minimizing long-term weighted sum-AoI. Using this, we characterize the achievable AoI region. We also compare this AoI-optimal policy with the one that maximizes average throughput (throughput-optimal policy), and demonstrate the impact of system state on their structures. Several promising directions for future research are also presented.

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  1. A Reinforcement Learning Framework for Optimizing Age-of-Information in RF-powered Communication Systems

    cs.IT 2019-08 conditional novelty 5.0 of 10

    For RF-powered multi-source monitoring systems, the age-optimal sampling policy is threshold-based in each process's age, and a deep Q-network can learn it efficiently.

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