Co-sponsored by: IEEE COMSOC

In this talk, Driven by higher potency and lower cost/size of devices capable of sensing, actuating,

processing and communicating, the Internet of Things and of Everything promises to dramatically

increase our ability to embed intelligence in the surroundings. Subnets of simple devices such as RFIDs

and tiny sensors/actuators deployed in massive numbers in 2D and complex 3D spaces will be a key

aspect of this emerging infrastructure. Most techniques for self-organization, routing and tracking in such

networks rely on distances and localization in the physical domain. While geographic coordinates fit well

with our intuitions into physical spaces, their use is not feasible in complex environments. Protocols

based on geographical coordinates do not scale well to 3D either. We present a novel localization-free

coordinate system, the Topology Coordinates (TC). Interestingly, geographic features such as voids

and shapes are preserved in the resulting Topology-Preserving Maps (TPMs) of 2-D and 3-D networks.

Ability to specify virtual cardinal directions and angles in networks is a radical change from the traditional

approaches. A novel self-learning algorithm is presented to provide network awareness to individual

nodes, a step toward large-scale evolving sensor networks. Application of TCs to social networking will

be illustrated.

Speaker(s): , , Anura Jayasumana,

Location:
Bldg: EV0002.184
1515 Ste. Catherine West
MONTREAL, Quebec
H3G 1M8