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21/06/2016
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Report written by Hugo Bonnaffé


#IoT Connit: “Among the Internet of Things, machine to machine is the submerged part of the iceberg.”


What do a billboard, water meter, box compactor and a recharging station for electrical vehicles have in common? It seems nothing, except that these items are becoming more and more connected. “Through sensors, they are able to transmit information regarding their state. This facilitates the recuperation of important data and maintains operational conditions remotely,” explains Pierric Cistac, developer at Connit. Deployed in mass around the world, these devices make up a farm of connected objects “a utility that is superior to connected watches, bracelets, tooth brushes or scales which, among the collective imagination, make up the Internet of Things (IoT),” according to him.





“Most usages remain to be invented”


A smart electric vehicle charging station can transmit information regarding its status, signaling if it is out of order. The water meter can send readings remotely or aggregate all the readings from the same zone, making it also possible to detect leaks in the water distribution network. The box compacter can transmit data regarding its level and request a truck be dispatched when full. “The examples are numerous, with still much more left to imagine. And this is without taking into account any new services made possible through statistical analysis once data is collected. From data to algorithms, algorithms to big data, from big data to machine learning, the path is well on its way towards a new revolution.”

Located in France, in an area known as the IoT Valley, the startup Connit, specializes in Industrial IoT and machine to machine (M2M), that is to say, the monitoring and control of equipment remotely via intelligent applications. Historically focused on smart metering (smart meters), today Connit manages a wide variety of projects for the industrial accounts: Odeolis/DBT for Nissan’s electrical vehicle recharging stations, Engie for electric meters, Sogedo for water meters, Tecsol for solar panels… In total, Connit has more than 50,000 connected devices deployed around the world, which it collects, stores and process their data (around 25 million logs each month).



“In IoT, you have to be good everywhere: electronics, design, development, infrastructure, organization of systems…”


These projects require specific skills from one end of the chain, from the sensors and onboard electronics (hardware) to the platform that receives the data (software) and the associated services, using the low bandwidth communications network dedicated to IoT (Sigfox, LoRa) or mobile network (GPRS).

“This is a challenge, because we must be good everywhere, including in the design of devices and interfaces.” This is why Connit chose Dedicated Cloud for its data collection and management platform: “This solution allows us to free ourselves from server management, administration and maintenance, all of which is carried out by OVH. This is a time consuming activity and is clearly not in the area where we have the most value. The Dedicated Cloud also permits us to benefit from high availability (essential for not missing data which are only available once), high load resistance, (via low bandwidth networks, data arriving in packets, causing irregular loads) and high scalability,” explains Pierric Cistac.



Towards OVH’s DBaaS Time Series


Today, accompanied by OVH through the Digital Launch Pad program, the startup looks to employee DBaaS Time Series, in order to delegate even more management of its platform to OVH, so that it can concentrate on matters where its expertise adds the most value. “With DBaaS Time Series, OVH would manage completely and without any intervention on our part, load increases, the storage and replication of data, the scaling our platform’s core, alerting via connectors… so, we would only host our Live M2M platform and API on webservers. This would be extremely convenient and make economic sense as billing would be based on usage.” The time that we save could be spent on extracting more intelligent information from the collected data. “One example: we have developed a solution to monitor the temperature of refrigerators, used especially by super markets to assure the cold chain is respected. We use the data for monitoring and triggering alerts if needed. From the same data, we could also provide information about the performance of the equipment, quality of maintenance, or even customer traffic within stores - higher customer traffic results in the refrigerators being opened more frequently which causes temperatures to rise.”