Topology Preserving Maps: A Localization-Free Approach for 2-D and 3-D IoT Subnets

Topology Preserving Maps: A Localization-Free Approach for 2-D and 3-D IoT Subnets

Speaker(s): Prof. Anura Jayasumana,

Agenda:

Abstract: 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  Internet of Things. 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 harsh environments and in complex deployments. 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.

Location:
Room: 1919
Bldg: 99
14820 Northeast 36th Street
Redmond,
98052