IBM Research and Partners Help Traffic Run Smoothly in Madrid
We’re also using a dashboard that monitors traffic at different locations in Madrid with the capability to display alerts in locations where the speed or the intensity of traffic goes beyond a certain threshold. That threshold is calculated using machine learning. It’s different for each location and for rush-hour periods versus nighttime periods, and so on.
ISM: How do OpenStack Swift and Apache Spark come into play?
PTS: OpenStack Swift is a scalable, low-cost way to store data. This is crucial for IoT data, which is quickly becoming the “biggest” big data ever. That’s because so many things are connecting to the internet and generating data. We’re also making it more efficient to analyze data on OpenStack Swift for our machine-learning computations.
Apache Spark is a cluster-computing framework that enables analytics to be done on the data. So, OpenStack Swift provides the storage, and Apache Spark provides the compute power for the analytics. It’s doing the machine learning and computation on the data.
ISM: So, you combined Swift and Spark to work almost as a single entity?
PTS: We’re also using other open-source tools we helped play nicely together. Apache Kafka and Pinterest Secor are used as a message hub and a way to get the data into Swift. Apache Parquet, a special-data format, is good for analytics in general and for IoT data in particular. We use Elasticsearch to make the analytics more efficient, and IBM Node-RED hooks together systems and sensors to improve the management of the IoT.
It’s a complex challenge. Clients don’t just want to run the solution on their laptops. They want it to be deployed in the cloud and scalable so they can start with a small amount of data or number of users and grow elastically. One of the exciting outcomes of our work is our collaboration with another IBM team, led by Naeem Altaf, to port the whole Madrid traffic use case to run on the IBM Bluemix* platform.
ISM: How can this technology be used?
PTS: In COSMOS, for example, we did some work on occupancy detection and the ability to save energy by turning appliances off when people have left their home or office. You can potentially do similar work with other types of sensors collecting data.
For example, by putting sensors along pipes and performing anomaly detection, you can detect and send a warning if a pipe leaks. You can even build actuators into your solutions so that when a pipe is leaking, the mains automatically shut off to prevent further damage to your home. If a company has an oil pipeline traveling across the country, you could use similar technology to help avoid an environmental disaster.
ISM: Where do you see the IoT heading in the future?
PTS: IoT is leading the way to new types of data being collected in unprecedented quantities. For example, in healthcare, you may use this technology to get more insight into the reasons behind illnesses.
For traffic, we could gain new insight into the conditions and locations under which accidents occur. Having this additional data can provide new opportunities and approaches we may never have considered before.
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