Context-aware addressing in the Internet of Things using Bloom filters

There is a consensus today among researchers that the Future Internet will be characterized by a significant shift towards the Internet of Things. Experts expect more than 50 billion devices connected to the Internet by 2020 (sensors, smartphones, laptops, cars, clothes, wristwatches, etc.). To ensure communication among these smart “things”, first we need a proper addressing scheme. However, while in the traditional Internet (of People) usually an end-user communicated with another user identified by a specific (IP) address, in the Internet of Things it will be a common scenario to see that the communication targets a specific set of “things”, identified by the current status or values of their resources, interfaces, or other context parameters. For example, imagine that we want to receive some traffic information from all the sensor nodes embedded in cars or smartphones that are on a certain street, have moved at least once in the last 5 minutes, and have their batteries charged at more than 20%. Or, we want to send some targeted advertisements to all the devices that are in the close neighborhood of our restaurant, have a video screen to display the ad, and have allowed in their profile the reception of ads. Multicast groups could be used for that, but traditional multicasting is not scalable for such a large number of groups, as multicast addresses cannot be aggregated. In this paper we propose therefore a context-aware addressing and routing solution based on Bloom filters, to supplement the traditional IPv6. This is a proof-of-concept paper that does not address all the fine details of such a solution; it just aims to show that Bloom filters could be very efficiently used to summarize context information, identify groups of things with certain similar characteristics, and make rapid routing decisions to reach these devices.