Minimizing Age of Information With Power Constraints: Multi-User Opportunistic Scheduling in Multi-State Time-Varying Channels

Abstract

This work is motivated by the need of collecting fresh data from power-constrained sensors in the industrial Internet of Things (IIoT) network. A recently proposed metric, the Age of Information (AoI) is adopted to measure data freshness from the perspective of the central controller in the IIoT network. We wonder what is the minimum average AoI the network can achieve and how to design scheduling algorithms to approach it. To answer these questions when the channel states of the network are Markov time-varying and scheduling decisions are restricted to bandwidth constraint, we first decouple the multi-sensor scheduling problem into a single-sensor constrained Markov decision process (CMDP) by relaxing the hard bandwidth constraint. Next we exploit the threshold structure of the optimal policy for the decoupled single sensor CMDP and obtain the optimum solution through linear programming (LP). Finally, an asymptotically optimal truncated policy that can satisfy the hard bandwidth constraint is built upon the optimal solution to each of the decoupled single-sensor. Our investigation shows that to obtain a small AoI performance: (1) The scheduler exploits good channels to schedule sensors supported by limited power; (2) Sensors equipped with enough transmission power are updated in a timely manner such that the bandwidth constraint can be satisfied.

A Reinforcement Learning Framework for Optimizing Age of Information in RF-Powered Communication Systems

“…Afterwards, a series of works [5]- [12] aimed at characterizing the average AoI and its variations (e.g., Peak Age-of-Information (PAoI) [8]- [10] and Value of Information of Update (VoIU) [11]) for adaptations of the queueing model studied in [4]. Another direction of research [13]- [33] focused on employing AoI as a performance metric for different communication systems that deal with time critical information while having limited resources, e.g., multi-server information-update systems [14], broadcast networks [15]- [17], multi-hop networks [18], cognitive networks [19], unmanned aerial vehicle (UAV)assisted communication systems [20]- [22], IoT networks [2], [23], [24], ultra-reliable low-latency vehicular networks [25], multicast networks [26], decentralized random access schemes [32], and multi-state time-varying networks [33]. Particularly, the objective of this research direction was to characterize optimal policies that minimize average AoI, referred to as ageoptimal polices, by applying different tools from optimization theory.…”

“…Particularly, the objective of this research direction was to characterize optimal policies that minimize average AoI, referred to as ageoptimal polices, by applying different tools from optimization theory. Note that [13]- [33] did not consider energy harvesting as a powering source for the source nodes.…”

“…Different from [13]- [33], another line of research [34]- [48] focused on the class of problems in which the source node is powered by energy harvesting under various system settings. The objective of this line of research was to investigate ageoptimal offline/online policies for update packet transmissions subject to the energy causality constraint at the source under various assumptions regarding the battery size, transmission time of update packets and channel modeling.…”

Optimizing Age Penalty in Time-Varying Networks with Markovian and Error-Prone Channel State

“…To fill this gap, we study a scenario where the base station (BS) collects time-sensitive data from multiple sensors through time-varying channels. We generalize our previous work [ 22 ] by considering a more realistic time-varying channel with packet loss and a more general age penalty measurement to model different application scenarios. The main contributions of the paper are listed as follows.…”

Semi-Online Computational Offloading by Dueling Deep-Q Network for User Behavior Prediction

“…There are variety of computation offloading methods such as [5]- [13] are proposed to offload task from edge users to servers in edge computing environments. However, most of the current computation offloading approaches assume that the edge users share the same settings.…”