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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](https://GitHub.com/Naereen/slides/) :wave: , PhD Student in France, co-advised by | *Christophe Moy* <br> @ IETR, Rennes | *Emilie Kaufmann* <br> @ CNRS & Inria, Lille | |:---:|:---:| | ![8%](../common/LogoCS.png) ![14%](../common/LogoIETR.png) | ![12%](../common/LogoInria.jpg) ![16%](../common/LogoCNRS.jpg) | > See our paper at [`HAL.Inria.fr/hal-02006825`](https://hal.inria.fr/hal-02006825) --- # 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. --- # :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! --- # 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: ! --- # 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 --- # 2. System Model ![80%](plots/system_model.png) --- # 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. --- # 3. Multi-Armed Bandits (MAB) ## 3.1. Model ## 3.2. Algorithms --- # 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. --- # 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" - $X_k(t) := \sum\limits_{s < t} r(s) \bold{1}(A(s)=k)$ sum reward from arm $k$ - $N_k(t) := \sum\limits_{s < t} \bold{1}(A(s)=k)$ number of samples of arm $k$ - And use $I_k(t) = \hat{\mu}_k(t) := \frac{X_k(t)}{N_k(t)}$. --- ## *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. --- # 4. GNU Radio Implementation ## 4.1. Physical layer and protocol ## 4.2. Equipment ## 4.3. Implementation ## 4.4. User interface --- # 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) --- # 4.2. Equipment $\geq3$ USRP boards 1: gateway 2: traffic generator 3: IoT dynamic objects (as much as we want) ![55%](plots/our-demo.png) --- # 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...) --- # Flowchart of the random traffic generator ![95%](plots/USRP_TX_PU__v1__simple_grc.png) --- # Flowchart of the IoT gateway ![95%](plots/USRP_RX_BTS__v1__simple_grc.png) --- # Flowchart of the IoT dynamic object ![95%](plots/USRP_TX_SU__v1__simple_grc.png) --- # 4.4. User interface of our demonstration → See video of the demo: [`YouTu.be/HospLNQhcMk`](https://youtu.be/HospLNQhcMk) ![30%](plots/UI.png) --- # 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. --- # Illustration ![70%](plots/plot_datafile_append_Uniform_vs_UCB_vs_TS.png) --- # 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!** --- # 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`](https://hal.inria.fr/hal-02049824) at MoTION Workshop @ WCNC) - Experiments in a real LoRa network with dozens of nodes (→ IoTlligent project @ Rennes, France) --- # 6. Conclusion ### → See our paper: [`HAL.Inria.fr/hal-02006825`](https://hal.inria.fr/hal-02006825) ### → See video of the demo: [`YouTu.be/HospLNQhcMk`](https://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](https://bitbucket.org/scee_ietr/malin-multi-arm-bandit-learning-for-iot-networks-with-grc/) > Thanks for listening !