Lessons From Building Dev Communities Around Parking IoT APIs

Parking optimisation with sensors and APIs is one of the clearest use cases for industrial and smart city IoT implementation.There is a clear business opportunity: maximising revenue from car parking spaces.There is a clear asset that can be opened up via API: realtime availability on parking bay vacancy.

And there are clear opportunities to grow new quality services: through leveraging machine learning to create predictive services.Tarik Hammadou, is CEO and cofounder at VIMOC Technologies, an IoT startup that builds both sensor hardware gateways as well as a hardware-agnostic platform for pulling in sensor data and mapping it.Using their neuBox sensor hardware, VIMOC has been able to work with parking garages to install sensors that track accurate counts into their facilities.

In the past, garages often used magnetic loop sensors, which have insufficient accuracy to guide potential drivers to vacant bays. So there was a strong business case for garages to use more advanced sensors. In the same way that HotelTonight leverages APIs to make last minute hotel rooms available, with accurate vacancy data parking operators can maximise their vacancy rates (a parking space in the Bay Area, San Francisco, for example, could be worth between $25,000 and $100,000 a year).

So being able to optimise car parking spaces is a business imperative.And the industry gets it.Hammadou says that when he presented to the U.

S. national parking association, we were overwhelmed by the demand for this type of tech. One of the requirements that we are getting from garage owners is that they want to be able to push vacancy data in realtime to app developers to allow way-finding and GPS maps to show vacant spaces.

So garage owners are wanting an open API.Because of the industry uptake and clear business case, VIMOC is now moving towards incorporating machine learning at the edge to make sure API developers have what they need to make meaningful apps built off the parking data. The main challenge in managing and growing a community of developers is the quality of the data exposed via API and its ability to create strong business cases, says Hammadou.

The API in itself can be well managed by establishing the right engineering process and software architecture design choices for scalability, security and availability. However, it is critical to deliver accurate sensor data if it is to solve complex infrastructure problems. Our main mission when engaging with any developer is to help them create more value to our customers than if we deliver the service on our own.

Establishing a strong business process to manage different projects with developers is critical. If the quality of data, the business process, and monetisation strategy are not established as part of the API engagement, there is no sustainable incentives and rewards to either the API provider, the developer or the end user. Hammadou sees product-market fit as a combination of high value data being made available by their API so that developers can create quality apps and services, and that there is a revenue-sharing program in place that recognises the developers as business creators in their own right.

Other examples of parking API providers include:World SensingSmartparkingCitibrainWhat this means for supporting a developer community around your IoT APIVIMOCs experiences offer some great lessons for any IoT provider looking to build API developer communities around their solution.Document the use case. Be very clear about how your IoT solution can be used by particular industries and build relationships with those industries so they understand the API-to-revenue connection.

VIMOC were able to explain their tech and get a traditional industry like parking garages asking for APIs and integrations.Make sure your IoT solution is generating accurate data that is made available to developers so they can create feature-rich, high quality products and services. VIMOC built trust and credibility with third party developers by ensuring their data provided a magnitude of accuracy greater than previous IoT solutions.

Once your IoT solution is in place and generating accurate data, then it is possible to introduce new cutting edge technologies like machine learning to drive new innovation. Do not start with deep learning and ML: this is the second wave of feature development for IoT infrastructure projects.Think about programmable business models.

Look to how you can partner with API developers through shared revenue models rather than by creating a transactional API pricing approach where developers must pay for access to your API. If you like this article, please click on the button below and follow this publication so you dont miss out.Ready to start engaging and growing your API community with Hitch?

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