Topology Preserving Maps: A Localization-Free Approach for 2-D and 3-D IoT SubnetsSpeaker: Anura Jayasumana – Fort Collins, CO, United States
Topic(s): Networks and Communications
AbstractDriven 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. The proposed approach takes significant strides toward bridging the gap between traditional graph theory and coordinate based approaches for networking. 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, with emphasis on recovery of network topology from partial information.
About this LectureNumber of Slides: 40 - 50
Duration: 50 minutes
Languages Available: English
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