JavaOne 2015 - CON6489 - Smart Open Spaces Powered by Low Cost Computers

Editor's Notes

• #2: Hello, Wellcome everybody to our conference sesión six thousand four hundred eighty nine, Smart Open Spaces.
• #3: My name is Julio Palma, I’m Technology Architect from Accenture based in Málaga, in Spain.
• #6: To help us to visualize this problem I need help from a friend of mine. Let me introduce you Mark.He's very excited in his new role as an open space manager.
• #7: As an Open Space Manager he needs to manage spaces with a little number of customers …
• #8: ... and others with a huge amount of them.
• #9: Customers can be indoors ...
• #10: ... or outdoors
• #11: He is not restricted to one specific industry, he can manage Department stores, smaller stores, hypermarkets
• #12: Museums, airports, train/bus stations
• #13: City downtowns, hospitals
• #14: Manufacturing plants, oil rigs
• #15: and, of course, offices
• #16: and, of course, offices
• #18: Mark wants to implement a “Smart” aproach to his new role. We can imagine a scenario where our common friend is facing a simple (aparentely) problem. How can he make this space Smart?
• #19: One of the first things we can imagine is to create some “Presence Zones” within we are going to monitor people activity.
• #20: In order to be as unintrusive as possible we use signals of their personal devices: Smart phones, weareables, laptops, tablets…
• #21: When customers are into the “presence zones” this information is captured and retrieved in different times
• #22: , we are taking “shots” of where each device is at this exact moment in time.
• #23: In order to have a look that let us know, let us… understand, how customers are ‘moving’ into our “Open Space”
• #24: We can use collected information in multiple ways: We can use it in “real time” trying to answer questions: Where are the customers located at this exact moment in time? Or … Which places are capturing the customer attention? Which places has a high density of customer.
• #25: We also can use this information to analyse aggregated data, in this case we use this tool as a way to understand customer habits: which path customers follow inside the store, where they spent more time (special offers?, at electronic or music department?), which places in the store, hospital, airport… are capturing people’s attention. All this information helps us to plan, for example, working shifts of attendants or space distribution, better.
• #29: Why do we develop a new solution for presence zones? We know that there is previous products that does exactly what we wont. This aproach has one main objective: TCO should be as low as possible. - we’re going to use open standards, minimise the cost of software licenses. we’re basing the solution on low-cost devices, and we want to use easy to obtain devices We want a simple and lightweight solution, and of course, easy to scale. -
• #30: For the technology selection we indentify Python and Java as the programming languages and runtimes that we wanted to use for the platform. Basically we’ll have a device sniffing tool that uses a Java ME midlet or Python script to sniff all the devices arround the area (know about their ssignal strength, mac adress,…), sending all this information to a data collector. The data collector, also can run on a python script or a Java SE server application, we are going to integrate all the different sniffers, and all this collected information in just one collector platform. To listen to he devices we’re using Bluetooth Low Energy technology because is more precise than WiFi, is nor as frequently use as WiFi but its usage is growing fast, mostly thanks to wearables. All this software runs on single board computers such as Raspberry Pi, Beaglebone or Arduino. In our life demo we’re going to use Rapberry Pi