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title: GNU Radio Implementation of Multi-Armed bandits Learning for Internet-of-things Networks subtitle: IEEE WCNC 2019 author: Lilian Besson institute: SCEE Team, IETR, CentraleSupélec, Rennes date: Wednesday 17th of April, 2019

lang: english

IEEE WCNC 2019: "GNU Radio Implementation of Multi-Armed bandits Learning for Internet-of-things Networks"

• Date :date: : $17$th of April $2019$

• Who: Lilian Besson :wave: , PhD Student in France, co-advised by

Christophe Moy @ IETR, Rennes	Emilie Kaufmann @ CNRS & Inria, Lille

See our paper at HAL.Inria.fr/hal-02006825

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Introduction

• We implemented a demonstration of a simple IoT network
• Using open-source software (GNU Radio) and USRP boards from Ettus Research / National Instrument
• In a wireless ALOHA-based protocol, IoT objects are able to improve their network access efficiency by using embedded decentralized low-cost machine learning algorithms
• The Multi-Armed Bandit model fits well for this problem
• Our demonstration shows that using the simple UCB algorithm can lead to great empirical improvement in terms of successful transmission rate for the IoT devices

Joint work by R. Bonnefoi, L. Besson and C. Moy.

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:timer_clock: Outline

1. Motivations

2. System Model

3. Multi-Armed Bandit (MAB) Model and Algorithms

4. GNU Radio Implementation

5. Results

Please :pray:

Ask questions at the end if you want!

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1. Motivations

• IoT networks are interesting and will be more and more present,
• More and more IoT objects
• $\Longrightarrow$ networks will be more and more occupied

But...

• Heterogeneous spectrum occupancy in most IoT networks standards
• Maybe IoT objects can improve their communication by learning to access the network more efficiently (e.g., by using the less occupied spectrum channel)
• Simple but efficient learning algorithm can give great improvements in terms of successful communication rates
• $\Longrightarrow$ can fit more objects in the existing IoT networks :tada: !

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2. System Model

Wireless network - In ISM band, centered at $433.5$ MHz (in Europe) - $K=4$ (or more) orthogonal channels

Gateway - One gateway, handling different objects - Communications with ALOHA protocol (without retransmission) - Objects send data for $1$s in one channel, wait for an acknowledgement for $1$s in same channel, use Ack as feedback: success / failure - Each object: communicate from time to time (e.g., every $10$ s) - Goal: max successful communications $\Longleftrightarrow$ max nb of received Ack

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2. System Model

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Hypotheses

1. We focus on one gateway

2. Different IoT objects using the same standard are able to run a low-cost learning algorithm on their embedded CPU

3. The spectrum occupancy generated by the rest of the environment is assumed to be stationary

4. And non uniform traffic: some channels are more occupied than others.

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3. Multi-Armed Bandits (MAB)

3.1. Model

3.2. Algorithms

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3.1. Multi-Armed Bandits Model

• $K \geq 2$ resources (e.g., channels), called arms
• Each time slot $t=1,\ldots,T$, you must choose one arm, denoted $A(t)\in{1,\ldots,K}$
• You receive some reward $r(t) \sim \nu_k$ when playing $k = A(t)$
• Goal: maximize your sum reward $\sum\limits{t=1}^{T} r(t)$, or expected $\sum\limits{t=1}^{T} \mathbb{E}[r(t)]$
• Hypothesis: rewards are stochastic, of mean $\mu_k$. Example: Bernoulli distributions.

Why is it famous?

Simple but good model for exploration/exploitation dilemma.

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3.2. Multi-Armed Bandits Algorithms

Often "index based"

• Keep index $I_k(t) \in \mathbb{R}$ for each arm $k=1,\ldots,K$
• Always play $A(t) = \arg\max I_k(t)$
• $I_k(t)$ should represent our belief of the quality of arm $k$ at time $t$

Example: "Follow the Leader"

• $Xk(t) := \sum\limits{s < t} r(s) \bold{1}(A(s)=k)$ sum reward from arm $k$
• $Nk(t) := \sum\limits{s < t} \bold{1}(A(s)=k)$ number of samples of arm $k$
• And use $I_k(t) = \hat{\mu}_k(t) := \frac{X_k(t)}{N_k(t)}$.

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Upper Confidence Bounds algorithm (UCB)

• Instead of using $I_k(t) = \frac{X_k(t)}{N_k(t)}$, add an exploration term $$ I_k(t) = \frac{X_k(t)}{N_k(t)} + \sqrt{\frac{\alpha \log(t)}{2 N_k(t)}} $$

Parameter $\alpha$: tradeoff exploration vs exploitation

• Small $\alpha$: focus more on exploitation,
• Large $\alpha$: focus more on exploration,
• Typically $\alpha=1$ works fine empirically and theoretically.

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4. GNU Radio Implementation

4.1. Physical layer and protocol

4.2. Equipment

4.3. Implementation

4.4. User interface

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4.1. Physical layer and protocol

Very simple ALOHA-based protocol

An uplink message $\,\nearrow\,$ is made of... - a preamble (for phase synchronization) - an ID of the IoT object, made of QPSK symbols $1\pm1j \in \mathbb{C}$ - then arbitrary data, made of QPSK symbols $1\pm1j \in \mathbb{C}$

A downlink (Ack) message $\,\swarrow\,$ is then... - same preamble - the same ID (so a device knows if the Ack was sent for itself or not)

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4.2. Equipment

$\geq3$ USRP boards

1: gateway 2: traffic generator 3: IoT dynamic objects (as much as we want)

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4.3. Implementation

• Using GNU Radio and GNU Radio Companion
• Each USRP board is controlled by one flowchart
• Blocks are implemented in C++
• MAB algorithms are simple to code

(examples...)

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Flowchart of the random traffic generator

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Flowchart of the IoT gateway

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Flowchart of the IoT dynamic object

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4.4. User interface of our demonstration

→ See video of the demo: YouTu.be/HospLNQhcMk

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5. Example of simulation and results

On an example of a small IoT network: - with $K=4$ channels, - and non uniform "background" traffic (other networks), with a repartition of $15\%$, $10\%$, $2\%$, $1\%$

1. $\Longrightarrow$ the uniform access strategy obtains a successful communication rate of about $40\%$.

2. About $400$ communication slots are enough for the learning IoT objects to reach a successful communication rate close to $80\%$, using UCB algorithm or another one (Thompson Sampling).

Note: similar gains of performance were obtained in other scenarios.

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Illustration

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6. Summary

We showed

1. The system model and PHY/MAC layers of our demo
2. The Multi-Armed Bandits model and algorithms
3. Our demonstration written in C++ and GNU Radio Companion
4. Empirical results: the proposed approach works fine, is simple to set-up in existing networks, and give impressive results!

Take home message

Dynamically reconfigurable IoT objects can learn on their own to favor certain channels, if the environment traffic is not uniform between the $K$ channels, and greatly improve their succesful communication rates!

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6. Future works

• Study a real IoT LPWAN protocol (e.g., LoRa)
• Implement our proposed approach in a large scale realistic

We are exploring these directions

• Extending the model for ALOHA-like retransmissions (→ HAL.Inria.fr/hal-02049824 at MoTION Workshop @ WCNC)
• Experiments in a real LoRa network with dozens of nodes (→ IoTlligent project @ Rennes, France)

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6. Conclusion

→ See our paper: HAL.Inria.fr/hal-02006825

→ See video of the demo: YouTu.be/HospLNQhcMk

→ See the code of our demo:

Under GPL open-source license, for GNU Radio: bitbucket.org/scee_ietr/malin-multi-arm-bandit-learning-for-iot-networks-with-grc

Thanks for listening !