Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

SolSystem Deployments

SolSystem ( is a complete sensor-to-cloud solution, which the Inria-EVA team uses to federate the different real-world deployments it is conducting.


Participants : Ziran Zhang, Keoma Brun-Laguna, Thomas Watteyne.

Marinas are quickly evolving from sailing spots to floating neighborhoods. It is now common for people to live on their boat year-round, and for boats to be rented for just a week-end through online platforms. Today, living or staying on a boat is often cheaper that buying or renting an apartment. Similarly, in coastal areas, the marina is often the center of the city, so an ideal location for lodging. As a result, the trend is not going to end any time soon. Today's marinas are tomorrow's smart cities.

And as the marina is evolving, so are its needs.

The combination of embedded micro-controllers, low-power wireless communication and sensors/actuators offers tremendous opportunities for marinas. Off-the-shelf “Internet of Things” technology can now be used to detect the presence of boats in moorings, track usage of water and electricity on a per-boat basis, track a boat in real-time as it enters the marina, etc. Because no wires need to be installed – neither for power, nor communication – installation can be done in a matter of hours in a peal-and-stick fashion. Pontoons can be moved, rearranged or removed, without having to worry about the smart devices mounted on it.

The goal of the SmartMarina project ( is to build a system composed of sensors deployed all over the marina, and advanced software to monitor the occupation of moorings, and the electricity and water consumption on each spot. The result is a system that allows more efficient management and new services. The first sensor was installed in April 2017, and the Inria-EVA team is looking at turning this activity into a startup company.


Participants : Keoma Brun-Laguna, Thomas Watteyne.

In 2013, 85% of the peach production in the Mendoza region (Argentina) was lost because of frost. Because less fruit was produced in the region, 600.000 less work days were needed to process the harvest between November 2013 and March 2014, a reduction in work force of 10.600 people. Across the Mendoza region, frost has caused a loss of revenue of 950 million Argentine pesos roughly 100 million USD in the peach business alone.

A frost event happens when the temperature is so low that the crops cannot recover their tissue or internal structure from the effects of water freezing inside or outside the plant. For the peach production, a critical period is when the trees are in bloom and fruit set (Aug./Sept. in Mendoza), during which the temperature needs to be kept above 3 C. Even a few hours below that temperature causes flowers to fall, preventing fruits to grow.

Because of the huge economic impact, countermeasures exist and are used extensively. Today, virtually all industrial peach orchards are equipped with a small number of meteorological stations which monitor temperature and humidity. If the temperature drops dangerously low, the most effective countermeasures is to install a number of furnaces in the orchard (typically coalfueled) and fly helicopters above the orchard to distribute the heat and avoid cold spots. This countermeasure is effective, but suffers from false positives (the helicopters are called in, but there is no frost event) and false negatives (the meteorological stations don't pick up a frost event happening in some part of the orchard).

What the SaveThePeaches project ( has developed in 2016-2017 is a dense 120-sensor real-time monitoring solution deployed in the orchard, and feeding a frost prediction model. A node is the size of a deck of cards, is self-contained and battery-operated. When switched on, nodes form a multi-hop low-power wireless network, automatically. Rather than being installed at a fixed location, these nodes can be hung directly in the trees. A network is deployed in an orchard in a matter of hours, and if needed, sensing points can be moved to improve the accuracy of the prediction model in minutes. We use machine learning and pattern recognition to build an micro-climate predictive model by continuously analyzing the gathered sensor data in real time. This model generates early frost warnings. Ones demonstrated, the solution can be extended to other crops, and other regions.


Participants : Keoma Brun-Laguna, Thomas Watteyne.

Between 2012 and 2015, California suffered from the highest water drought since recordings started in this state. Up to 2/3 of its water resources are coming from the Sierra Nevada snowpack. Understanding the effect of the droughts on the mountain snowpack is crucial.

Historically, the study of mountain hydrology and the water cycle has been largely observational, with variables extrapolated from a few infrequent manual measurements. Low-power wireless mesh networking technology has evolved significantly over recent years. With this technology, a node is the size of a deck of cards, is self-contained and battery-operated. When switched on, nodes form a multi-hop low-power wireless network, automatically. Next-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 SnowHow project ( provides these measurements by deploying low-power mesh networks across the California Sierra Nevada. Off-the-shelf commercial solutions are available today which offer >99.999% end-to-end data reliability and a decade of battery lifetime. A new wireless network can be deployed in a couple of hours and report sensor data minutes after it was measured.