Decentralized spectrum learning for radio collision mitigation in ultra-dense IoT networks: LoRaWAN case study and experiments Christophe Moy, Lilian Besson, Guillaume Delbarre, Laurent Toutain To cite this version: Christophe Moy, Lilian Besson, Guillaume Delbarre, Laurent Toutain. Decentralized spectrum learn- ing for radio collision mitigation in ultra-dense IoT networks: LoRaWAN case study and experiments. Annals of Telecommunications - annales des télécommunications, Springer, 2020, 75 (11-12), pp.711- 727. 10.1007/s12243-020-00795-y. hal-02956350 HAL Id: hal-02956350 https://hal.archives-ouvertes.fr/hal-02956350 Submitted on 2 Oct 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution| 4.0 International License Annals of Telecommunications https://doi.org/10.1007/s12243-020-00795-y Decentralized spectrum learning for radio collision mitigation in ultra-dense IoT networks: LoRaWAN case study and experiments Christophe Moy1 & Lilian Besson2 & Guillaume Delbarre1 & Laurent Toutain 3 Received: 10 July 2019 /Accepted: 10 August 2020 # The Author(s) 2020 Abstract This paper describes the theoretical principles and experimental results of reinforcement learning algorithms embedded into IoT devices (Internet of Things), in order to tackle the problem of radio collision mitigation in ISM unlicensed bands. Multi-armed bandit (MAB) learning algorithms are used here to improve both the IoT network capability to support the expected massive number of objects and the energetic autonomy of the IoT devices. We first illustrate the efficiency of the proposed approach in a proof-of-concept, based on USRP software radio platforms operating on real radio signals. It shows how collisions with other RF signals are diminished for IoT devices that use MAB learning. Then we describe the first implementation of such algorithms on LoRa devices operating in a real LoRaWAN network at 868 MHz. We named this solution IoTligent. IoTligent does not add neither processing overhead, so it can be run into the IoT devices, nor network overhead, so that it requires no change to LoRaWAN protocol. Real-life experiments done in a real LoRa network show that IoTligent devices’ battery life can be extended by a factor of 2, in the scenarios we faced during our experiment. Finally we submit IoTligent devices to very constrained conditions that are expected in the future with the growing number of IoT devices, by generating an artificial IoT massive radio traffic in anechoic chamber. We show that IoTligent devices can cope with spectrum scarcity that will occur at that time in unlicensed bands. Keywords Internet of Things (IoT) . Machine learning . MAB . Bandit . UCB . Radio spectrum . Collision mitigation . Interference . LoRa . Artificial intelligence . LoRaWAN . Cognitive radio . Spectrum scarcity . ISM band 1 Introduction expected to experience a tremendous expansion in the very next few years, through IoT networks. Wireless Internet of Things (IoT) is based on low-power wide- We can consider two categories of IoT networks. First are area networks (LPWAN) able to interconnect low-cost and the cellular IoT networks, deployed by mobile phone opera- mostly battery-powered devices over long ranges to an access tors, running 3GPP standards such as EC-GSM IoT, LTE- point to the Internet. This is made possible by the use of low Cat0, LTE-Cat M1, NB-IoT, or expected 5G IoT. These stan- bit rates, low-bandwidth machine-to-machine (M2M) types dards will be supported in licensed frequency bands operated communications. After the expansion of human-to-human for cellular telephony. The second category of IoT wireless mobile communications in the 1990s and then human to the networks uses unlicensed bands for wireless links, also called Internet communications in the 2000s, now has come the era ISM bands, which are open to the use for industrial, scientific, of M2M and especially Machine to the Internet (M2I). M2I is and medical applications. The most commonly used ISM bands are 434 MHz and 868 MHz in Europe and Africa and 915 MHz in America, with worldwide bands at 2.4 GHz and 5.8 GHz. Due to the constraints in terms of range and band- * Christophe Moy width, the 868 MHz and 915 MHz bands are mostly preferred [email protected] for IoT networks. They communicate through protocols based on very different radio physical layer and medium access con- 1 Univ Rennes, CNRS, IETR - UMR 6164, F-35000 Rennes, France trol specifications. For instance, the current two most well- 2 CentraleSupélec, CNRS, IETR - UMR 6164, known IoT standards are LoRaWAN [1], based on a chirp F-35576 Cesson-Sévigné, France spread-spectrum solution, and Sigfox [2], based on an ultra- 3 IMT Atlantique, IRISA, F-35700 Rennes, France narrow band technology. Ann. Telecommun. In cellular licensed IoT networks, only one transmission failure of the IoT service, either because IoT devices cannot may occur in a given place, at a given time, and in a given succeed in sending any data to the network or because multi- frequency band, between any operated device and the radio ple repetitions could make them consume all their energy access point, scheduled by the cellular network. However, in much faster than expected. unlicensed bands, IoT networks face very different and spe- cific conditions. Many IoT networks can be deployed in the 2.2 Analysis of collisions same area and overlap geographically, regardless if they are using the same protocol or not. Even if there exist rules to be Radio collisions will be the weak point of LPWAN IoT net- followed in unlicensed bands, such as transmit power mask works operating in the unlicensed bands. Different kinds of and duty cycle limits in the 434, 868, and 915 MHz band, for collisions exist, as collision may occur with: instance, many radio transmissions may collide at the same place, time, and frequency, as no global coordination is & Other IoT devices of the same network, as several net- achieved. works covering the same area are not coordinated. This The goal of this paper is to present the original IoTligent can occur between IoT devices uplink (UL) transmissions approach that embeds very low-cost machine learning algo- and between IoT UL and gateway downlink (DL) trans- rithms inside IoT devices in order to make them smart. We missions towards IoT devices. named these IoT “intelligent devices”: IoTligent [3]. & Other IoT devices of surrounding IoT networks using the Intelligence here is used in order to mitigate radio collisions same IoT standard. This can occur both in UL and DL, as and other jamming effects (propagation, malicious attacks, surrounding IoT gateways of different networks are not etc.) in the ISM bands. Low cost here is to be considered in coordinated. They could use the same channels or partly terms of processing power, processing resources, memory same and partly different channels. footprint, protocol overhead, and frequency resources usage. & Other IoT radio signals using other IoT radio standards After exposing the issues we target in this work and the with different channels, bandwidth, users’ repartition, etc. corresponding hypothesis in Section 2, Section 3 reminds the & Other radio signals present in the ISM bands that are not foundation of the learning algorithms used in IoTligent. Then, IoT signals. By definition, they use completely different we show how we validated our approach through several rules than IoT. They can be considered “jammers” from gradual stages of experimentations. Measurements 1 of the IoT network point of view. Section 4 give results of a proof-of-concept made in laborato- ry conditions using SDR (software-defined radio) platforms in It is also important to note that, as each IoT standard uses its order to validate the learning approach. Then, Section 5 gives own rules for channeling and bandwidth, all this leads to an the experimental architecture and hardware configuration erratic spectrum usage, which cannot be planned, and has to used for measurement 2 campaign presented in Section 6. be learnt in vivo. However, unlicensed band does not mean Experiments have been realized on LoRa IoT devices operat- un-ruled band (there are for duty cycle, power, etc.), but they ed in real radio conditions of an operating LoRaWAN net- are more exposed to the non-respect of these few rules as work in the city of Rennes (France). In Section 7, we present regulation is relaxed and, thus, controls as well. measurement 3 made in an anechoic chamber with an emulat- ed radio traffic generator. We reproduce here the future very 2.3 Other issues dense IoT networks radio conditions and validate the pro- posed learning approach for future ultra-dense LoRaWAN Other issues can affect IoT transmission success. First one is networks. propagation. Depending on the specific environment condi- tions around each device, it is unpredictable to know if radio propagation does affect all channels in the same way. Then, a 2 Collisions, hypothesis, and advantages channel facing bad propagation conditions would make IoT of decentralization network to suffer from the same effect as made by collisions. Then electromagnetic circumstances could be disturbed in the 2.1 Collisions vs. autonomy area of devices, due to the proximity of other electric or elec- tronic devices suffering from leakage radiations, as in factory Radio collisions are the main drawback for IoT in unlicensed environment, for instance. IoT signals may be very weak, and band, both in terms of battery autonomy and also of IoT via- receivers should have a very low sensitivity in order to con- bility in the ISM bands itself.