Background

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Since affordable drones arrived, the number of people using them has kept climbing, mostly because they’re highly mobile and can do jobs in the air that are awkward to do any other way. With 5G just around the corner, researchers are looking for better ways to place drones, both in the air and on the ground between flights. Get the placement right and cellular connectivity improves, and the average latency between aerial user equipment and equipment on the ground comes down.

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems

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Types of UAVs

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Paper [1] sorts UAVs into a tree that branches out into sub-classes.

Beyond 5G with UAVs: Foundations of a 3D Wireless Cellular Network

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Introduction

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• Drones in large numbers will have a big effect on wireless networks.
• Applications include thermal imaging, package delivery and aerial base stations.
• The paper looks at low-altitude drone user equipment in a 3D space, served by access points on high-altitude platforms, which are UAVs such as blimps.

Background

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• Earlier work ignored drones that are themselves user equipment (UE) in flight.
• Earlier work on user association in drone networks didn’t consider aerial base stations.

System Model

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• Two kinds of drone: high-altitude drones act as access points for low-altitude drone UEs.

Transport Theory

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My Contribution

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Facility Location Problem Approach for Distributed Drones

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The paper starts from two gaps: governments and aviation agencies are bound to restrict where drones can take off and land, and most drone research assumes where a drone starts without justifying it. We proposed drone ports, fixed places for drones to take off, land and charge, and an algorithm derived from the facility location problem that picks them from a set of candidate sites to minimise the average distance drones travel to the tasks in an area. It gives a near-optimal placement that reduces the drones’ overall energy use. We then applied several travelling salesman algorithms to find the shortest route for a drone to visit all its tasks.

The figure above shows the idea: put drone ports where demand is high. The larger coloured ports are the ones chosen as optimal, and the smaller greyed-out ones are candidate locations that didn’t make the cut.

Facility Location Problem

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Framing it as a facility location problem turns drone port placement into an optimisation problem: given a budget and a set of candidate locations, find the best places to put the ports.

References

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[1] Mozaffari, Mohammad, et al. “A tutorial on UAVs for wireless networks: Applications, challenges, and open problems.” arXiv preprint arXiv:1803.00680 (2018).

[2] Mozaffari, Mohammad, et al. “Beyond 5G with UAVs: Foundations of a 3D wireless cellular network.” IEEE Transactions on Wireless Communications 18.1 (2019): 357-372.

[3] Lynskey, Jared, et al. “Facility Location Problem Approach for Distributed Drones.” Symmetry 11.1 (2019): 118.

That makes it different from latency, which is the delay of each individual packet, and from throughput, which counts how many packets get through. You can do badly on age in two opposite ways: send updates too rarely and the receiver is left holding old information; send them so often that queues build up, and every update is already stale by the time it arrives.

How old is your data?

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Data timeliness is one of the six criteria I identified while working at Methanex NZ, and the idea behind it is simple: new information is worth much more than old. Recently, researchers have been proposing algorithms that schedule data collection from sensors and IoT devices so as to minimise the Age of Information in next-generation (5G) wireless networks.

Data quality

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I researched data quality during my time at Methanex NZ, and these were the key points I identified for judging the quality of data:

• Timeliness: how much value the data still has at the moment it’s used.
• Completeness: how much of the data is in the system, with no missing fields.
• Consistency: how well the data is kept consistent.
• Confidence: how far you can trust the source of the data.
• Accuracy: how well the data represents reality.
• Integrity: the number of sources, and how consistent the data is across them.

Welcome to the age of data.

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Age of Information

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Age of Information is about the freshness of data [1]. Interest in it has grown with the move from 4G to 5G, which puts far more weight on wireless service for Internet of Things (IoT) devices and machine-type communication (MTC) [2].

The key ideas: the sawtooth, average age and peak age

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Plot the age over time and you get a sawtooth. Between updates it climbs steadily, one second of age per second. When a new update arrives the age drops, but not to zero: it drops to the time that update spent in transit, because it was already that old when it landed.

Two numbers summarise the curve. Average AoI is the area under the sawtooth divided by the time observed, so it’s the age you’d see at a random moment. Peak AoI is the average height of the teeth, the age just before each update lands, which is easier to analyse and tells you how stale things get at worst in a typical cycle.

The classic early result comes from Kaul, Yates and Gruteser’s 2012 paper, “Real-time status: How often should one update?” [4]. They modelled updates passing through a single first-come-first-served queue (M/M/1), with service rate μ and utilisation ρ (how busy the link is), and showed that the average age is

Δ = (1/μ) × (1 + 1/ρ + ρ² / (1 − ρ))

The 1/ρ term punishes sending too rarely, and the ρ²/(1 − ρ) term punishes sending so much that a queue builds up. Age is lowest at ρ ≈ 0.53, with the link idle almost half the time. Maximising throughput would push ρ towards 1, and minimising the delay of each packet would push it towards 0; freshness wants neither. That is the whole case for the metric in one line: the best update rate for freshness is one that neither throughput nor delay would choose.

Two papers on scheduling for AoI

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Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks [1]

The authors consider a factory where a number of sensors constantly transmit data about their current state. The sensors have no on-device storage, so a sensor’s present state is only recorded if the remote scheduler selects its signal; the scheduler’s access point ignores the rest. That leaves the scheduler with a dilemma: how to choose among sensors with different thresholds and packet sizes so that none of their timing requirements are violated. “The URLLC term minimization is represented by ensuring that the probability the AoI of each sensor exceeds a predefined threshold should be at its minimum.” To solve this formulation, they train a reinforcement learning scheduler with an actor-critic algorithm.

Minimizing the Age of Information from Sensors with Correlated Observations [3]

This paper studies “the average AoI in a generic setting with correlated sensor information.” Each update from a physical source can be picked up by several sensors, so their observations overlap, and there’s no point in the access point collecting the same update twice. The authors propose two scheduling policies: one that needs the system’s parameters to be known in advance, and one based on contextual bandits that learns them as it goes. Both exploit the overlap between sensors to cut the age by a wide margin compared with random or round-robin schedules.

References

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[1] Elgabli, A., Khan, H., Krouka, M., & Bennis, M. (2018). Reinforcement learning based scheduling algorithm for optimizing age of information in ultra reliable low latency networks. arXiv preprint arXiv:1811.06776.

[2] Boccardi, Federico, et al. “Five disruptive technology directions for 5G.” IEEE Communications Magazine 52.2 (2014): 74-80.

[3] Kalør, Anders E., and Petar Popovski. “Minimizing the Age of Information from Sensors with Correlated Observations.” arXiv preprint arXiv:1811.06453 (2018). Later versions are titled “…with Common Observations”.

[4] Kaul, S., Yates, R., & Gruteser, M. (2012). Real-time status: How often should one update? Proceedings of IEEE INFOCOM 2012, 2731-2735.

Further reading: the best place to start is the survey by Yates, Sun, Brown, Kaul, Modiano and Ulukus, “Age of Information: An Introduction and Survey” (IEEE Journal on Selected Areas in Communications, 39(5), 2021). For a whole book on the subject, there’s Age of Information: Foundations and Applications, edited by Pappas, Abd-Elmagid, Zhou, Saad and Dhillon (Cambridge University Press, 2023).