Demo: Simulating a 6TiSCH Network using Connectivity Traces from Testbeds

International audienceThe 6TiSCH simulator is an existing Python-based simulation tool that captures the full behavior of 6TiSCH, the Industrial IoT protocol stack standardized by the IETF. The existing 6TiSCH simulator uses a radio propagation model. In this demo, we present an extension to the 6TiSCH simulator which allows a simulation to be run against connectivity traces previously gathered on testbeds and real-world deployments. We demonstrate four elements. First, Mercator, the OpenWSN-based tool we developed to collect connectivity traces from different testbeds. Second, K7, the generic format we defined for these connectivity traces. Third, the set of 17 connectivity traces we gathered from testbeds and real-world deployments, and which are publicly available. Fourth, the extension of the 6TiSCH simulator which enables it to replay K7 connectivity traces rather than using a propagation model. Using connectivity traces for simulation is a way to increase the confidence the result are representative of a real-world deployment. Furthermore, it allows better repeatability than re-running an experiment on a testbed where the connectivity necessarily changes over time

Optimized Scheduling for Time-Critical Industrial IoT

International audienceThe main requirement of Industrial Internet of Things (IIoT) is reliability with a targeted reliability of above 99.999%. Then come latency and energy-efficiency requirements. The next step for the IIoT is to target time-critical applications. Even if IIoT technologies are now adopted worldwide, challenges remain and some of the limits of the technologies are still not fully understood. In this paper, we address TSCH-based (Time Slotted Channel Hopping) Wireless Sensor Networks and study their latency and lifetime limits under real-world conditions. We compute theoretical bounds on the end-to-end latency in a perfect radio environment and then in real deployments with unreliable links. We compare the performance of different scheduling algorithms and evaluate the impact of unreliable links on end-to-end latency and network lifetime, by means of simulations using traces collected from real deployed TSCH networks

Trace-Based Simulation for 6TiSCH

International audienceSimulation is a powerful tool for development of wireless networks. One key drawback of simulation-based studies for wireless networking is the need to use a radio propagation model. In this paper, we propose trace-based simulation for 6TiSCH (IPv6 over the TSCH mode of IEEE802.15.4e). We make the 6TiSCH Simula-tor run against connectivity traces collected in a real-world deployment. Our evaluation shows the trace-based simulation can yield almost the same results as experiments, with 1,200 times faster speed in execution time

PEACH: predicting frost events in peach orchards using IoT technology

In 2013, 85% of the peach production in the Mendoza region (Argentina) was lost because of frost. In a couple of hours, farmers can lose everything. Handling a frost event is possible, but it is hard to predict when it is going to happen. The goal of the PEACH project is to predict frost events by analyzing measurements from sensors deployed around an orchard. This article provides an in-depth description of a complete solution we designed and deployed: the low-power wireless network and the back-end system. The low-power wireless network is composed entirely of commercial off-the-shelf devices. We develop a methodology for deploying the network and present the open-source tools to assist with the deployment and to monitor the network. The deployed low-power wireless mesh network is 100% reliable, with end-to-end latency below 2 s, and over 3 years of battery lifetime. This article discusses how the technology used is the right one for precision agriculture applications.EEA JunínFil: Watteyne, Thomas. Institut National de Recherche en Informatique et en Automatique (INRIA). EVA Team; FranciaFil: Diedrichs, Ana Laura. Universidad Tecnológica Nacional (UTN), Mendoza; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Brun-Laguna, Keoma. Institut National de Recherche en Informatique et en Automatique (INRIA). EVA Team; FranciaFil: Chaar, Javier Emilio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Junín; ArgentinaFil: Dujovne, Diego. Universidad Diego Portales (UDP), Santiago; ChileFil: Taffernaberry, Juan Carlos. Universidad Tecnológica Nacional (UTN), Mendoza; ArgentinaFil: Mercado, Gustavo. Universidad Tecnológica Nacional (UTN), Mendoza; Argentin

Demo: SierraNet: Monitoring the Snowpack in the Sierra Nevada

International audienceNext-generation hydrologic science and monitoring requires real-time, spatially distributed measurements of key variables including: soil moisture , air/soil temperature, snow depth, and air relative humidity. The SierraNet project provides these measurements by deploying low-power mesh networks across the California Sierra Nevada. This demo presents a replica of the end-to-end SierraNet monitoring system deployed in the Southern Sierra. This system is a highly reliable, low-power turn-key solution for environmental monitoring

(Not so) Intuitive Results from a Smart Agriculture Low-Power Wireless Mesh Deployment

International audienceA 21-node low-power wireless mesh network is deployed in a peach orchard. The network serves as a frost event prediction system. On top of sensor values, devices also report network statistics. In 3 months of operations, the network has produced over 4 million temperature values, and over 350,000 network statistics. This paper presents an in-depth analysis of the statistics, in order to precisely understand the performance of the network. Nodes in the network exhibit an expected lifetime between 4 and 16 years, with an end-to-end reliability of 100%. We show how – contrary to popular belief – wireless links are symmetric. Thanks to the use of Time Slotted Channel Hopping (TSCH), the network topology is very stable, with ≤5 link changes per day in the entire network

Real-time Alpine Measurement System Using Wireless Sensor Networks

International audienceMonitoring the snow pack is crucial for many stakeholders, whether for hydro-poweroptimization, water management or flood control. Traditional forecasting relies on regressionmethods, which often results in snow melt runoff predictions of low accuracy in non-averageyears. Existing ground-based real-time measurement systems do not cover enough physiographicvariability and are mostly installed at low elevations. We present the hardware and software designof a state-of-the-art distributedWireless Sensor Network (WSN)-based autonomous measurementsystem with real-time remote data transmission that gathers data of snow depth, air temperature,air relative humidity, soil moisture, soil temperature, and solar radiation in physiographicallyrepresentative locations. Elevation, aspect, slope and vegetation are used to select networklocations, and distribute sensors throughout a given network location, since they govern snowpack variability at various scales. Three WSNs were installed in the Sierra Nevada of NorthernCalifornia throughout the North Fork of the Feather River, upstream of the Oroville dam and multiplepowerhouses along the river. The WSNs gathered hydrologic variables and network health statisticsthroughout the 2017 water year, one of northern Sierra’s wettest years on record. These networksleverage an ultra-low-power wireless technology to interconnect their components and offer recoveryfeatures, resilience to data loss due to weather and wildlife disturbances and real-time topologicalvisualizations of the network health. Data show considerable spatial variability of snow depth, evenwithin a 1 km2 network location. Combined with existing systems, these WSNs can better detectprecipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoffduring precipitation or snow melt, and inform hydro power managers about actual ablation andend-of-season date across the landscape

A Demo of the PEACH IoT-based Frost Event Prediction System for Precision Agriculture

International audienceIn 2013, 85% of the peach production in the Mendoza region (Argentina) was lost because of frost. In a couple of hours, farmers can lose everything. Handling a frost event is possible, but it is hard to predict when it is going to happen. The goal of the PEACH project is to predict frost events by analyzing measurements from sensors deployed around an orchard. This demo provides an overview of the complete solution we designed and deployed: the low-power wireless network and the back-end system. The low-power wireless network is composed entirely of commercial off-the-shelf devices. We develop a methodology for deploying the network and present the open-source tools to assist with the deployment, and to monitor the network. The deployed low-power wireless mesh network, built around SmartMesh IP, is 100% reliable, with end-to-end latency below 2 s, and over 3 years of battery lifetime

Réseaux déterministes pour l'internet des objets industriel

The Internet of Things (IoT) evolved from a connected toaster in 1990 to networks of hundreds of tiny devices used in industrial applications. Those “Things” usually are tiny electronic devices able to measure a physical value (temperature, humidity, etc.) and/or to actuate on the physical world (pump, valve, etc). Due to their cost and ease of deployment, battery-powered wireless IoT networks are rapidly being adopted. The promise of wireless communication is to offer wire-like connectivity. Major improvements have been made in that sense, but many challenges remain as industrial application have strong operational requirements. This section of the IoT application is called Industrial IoT (IIoT). The main IIoT requirement is reliability. Every bit of information that is transmitted in the network must not be lost. Current off-the-shelf solutions offer over 99.999% reliability. That is, for every 100k packets of information generated, less than one is lost. Then come latency and energy-efficiency requirements. As devices are battery-powered, they need to consume as little as possible to be able to operate during years. The next step for the IoT is to target time-critical applications. Industrial IoT technologies are now adopted by companies over the world, and are now a proven solution. Yet, challenges remain and some of the limits of the technologies are still not fully understood. In this work we address TSCH-based Wireless Sensor Networks and study their latency and lifetime limits under real-world conditions. We gathered 3M network statistics 32M sensor measurements on 11 datasets with a total of 170,037 mote hours in real-world and testbeds deployments. We assembled what we believed to be the largest dataset available to the networking community. Based on those datasets and on insights we learned from deploying networks in real-world conditions, we study the limits and trade-offs of TSCH-based Wireless Sensor Networks. We provide methods and tools to estimate the network performances of such networks in various scenarios. We believe we assembled the right tools for protocol designer to built deterministic networking to the Industrial IoT.L’Internet des Objets (IoT) a évolué d’un toaster connecté en 1990 vers des réseaux de centaines de petit appareils utilisés dans des applications industrielle. Ces « Objects » sont habituellement de petit appareils électroniques capable de mesurer une valeur physique (température, humidité, etc.) et/ou d’agir sur le monde physique (pump, valve, etc.). De part leur faible coût et leur facilité de déploiement, ces réseaux sans fil alimentés par batteries ont étés rapidement adoptés. La promesse des communications sans fil est d’offrir une connectivité similaire au réseau filaires. De nombreuses amélioration ont étés fait dans ce sens, mais plein de défis restent à surmonter car les applications industrielles ont de fortes exigences opérationnelles. Cette section de l’IoT s’appelle l’Internet Industriel des Objets. La principale exigence est la fiabilité. Chaque bout d’information transmit dans le réseau ne doit pas être perdu. Des solutions commerciales sont aujourd’hui accessibles et propose des fiabilités de l’ordre de 99.999 %. C’est à dire, pour chaque centaine de paquet d’information généré, moins d’un est perdu. Vient ensuite la latence et l’efficience énergétique. Comme ces appareils sont alimentés par des batteries, ils doivent consommer le moins possible et être capable d’opérer pendant des années. La prochaine étape pour l’IoT est d’être appliqué au applications nécessitant des garanties de latence. Les technologies de l’IIoT sont maintenant adoptés par de nombreuses entreprises autour du monde et sont maintenant des technologies éprouvées. Néanmoins des défis restent à accomplir et certaines limites de ces technologies ne sont pas encore connues. Dans ce travail, nous nous adressons au réseaux sans fils fondés sur TSCH dont nous testons les limites de latence et de durée de vie dans des conditions réelles. Nous avons collecté plus de 3M statistiques réseaux et 32M données de capteurs dans 11 déploiements sur un total de 170,037 heures machines dans des environnements réels ainsi que dans des bancs d’essais. Nous avons réuni ce que nous pensons être le plus grand jeu de données de réseau TSCH disponible à la communauté réseau. En s’appuyant sur ces données et sur notre expérience des réseaux sans fils en milieu réel, nous avons étudié les limites des réseaux TSCH et avons fourni des méthodes et outils qui permettent d’estimer des performances de ces réseaux dans diverses conditions. Nous pensons avoir assemblé les bons outils pour que les architectes de protocoles réseaux construise des réseaux déterministes pour l’IIoT