1 / 20

An Information-theoretic View of Connectivity in Large Wireless Networks

This paper from SECON 2004 explores connectivity in wireless networks through an information-theoretic lens, defining and quantifying connectivity at a rate R for active nodes. It delves into various network models, such as planar and linear grids, considering factors like node activity probability and power constraints. The study aims to determine guaranteed data rates for active sensor nodes and coverage areas based on data rates. The findings highlight the importance of quantifying connectivity in networks with infrequent communications and point-to-point communication patterns.

feder
Download Presentation

An Information-theoretic View of Connectivity in Large Wireless Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. An Information-theoretic View of Connectivity in Large Wireless Networks Xin Liu Department of Computer Science Univ. of California, Davis Joint work with R. Srikant SECON 2004

  2. What’s new? • Traditional approach: qualify connectivity. • Yes or No. • Far away nodes may still communicate. • “An ocean of possibilities”- from an information-theoretic viewpoint • Coherent relay, broadcast, multi-access, interference cancellation, network coding, etc. • Multi-path routing, multi-hop relay, etc. • Our approach: quantify connectivity SECON 2004

  3. Definition • The network is connected at rate R, if • any single node • communicate with its randomly chosen destination node at rate R • assuming all other nodes are helpers. • For a sensor network, the destination can be the sink node. SECON 2004

  4. Active Node Inactive Node System Model • A regular grid network with unreliable nodes Planar Linear SECON 2004

  5. System Model Cont’d • p: probability a node is active • Out of energy, out of sync, damaged, etc. • Can reflect the temporal property of a network • Pinv: average power constraint per node • Does not limit to multi-hop relay • Include possible approaches • Coherent relay, broadcast, multi-access, interference cancellation, etc. • Multi-path routing, multi-hop relay, etc. SECON 2004

  6. System Model Cont’d • AWGN channel • Signal attenuation model • >1. • Asymptotic bounds SECON 2004

  7. d Active Node Inactive Node Objective 1 • What is the guaranteed data rate? • for any single active sensor node • with other active nodes as helpers • given the topology. Sink SECON 2004

  8. Objective 2 • How large an area can be covered by n nodes? • given the desired data rate R • for each single active sensor node • with other active nodes as helpers. SECON 2004

  9. Applications • Infrequent yet important communications • Surveillance network with rare events • Lower bound on data rate for ALL nodes • Isolated nodes are important in terms of information gathering and event detection SECON 2004

  10. Upper Bound SECON 2004

  11. Notes • Some nodes may achieve higher rates. • Upper bound cannot be guaranteed for ALL. • Achievable rate is bounded by the total received power • With a certain probability, there exists an isolated node • An isolated node is a node far away from others • Rate is bounded. SECON 2004

  12. Lower Bound SECON 2004

  13. Notes • Guaranteed lower bound • Achievability • Divide the linear network into intervals • Each interval has at least one node • Multi-hop relay with interference cancellation. SECON 2004

  14. Linear Network • Upper bound • ((log(n))-2+1) • Lower bound • O((log(n))-2) SECON 2004

  15. Impact of n SECON 2004

  16. Impact of p SECON 2004

  17. Planar Networks • Upper bound • ((log(n))-+1) • Lower bound • O((log(n))-) SECON 2004

  18. Take-home Message • Quantify connectivity • Connectivity is associated with guaranteed achievable data rate. • Applies to networks with infrequency communications • Applies to wireless networks with a p-2-p communication pattern. SECON 2004

  19. To-do list • Gap between upper and lower bounds • Random deployed networks • Fading channels Thank you! SECON 2004

  20. System Model • A regular grid network with unreliable nodes Linear Network Planar Network SECON 2004

More Related