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Wireless Networking Using Grid

Wireless Networking Using Grid. Douglas S. J. De Couto http://www.pdos.lcs.mit.edu/grid (revised). A. F. D. E. C. G. J. I. H. B. Goal: Build Networks from Chaos. Constraints: All wireless No centralized infrastructure Mobile Scalable. Examples: Rooftop networks

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Wireless Networking Using Grid

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  1. Wireless Networking Using Grid Douglas S. J. De Couto http://www.pdos.lcs.mit.edu/grid (revised)

  2. A F D E C G J I H B Goal: Build Networks from Chaos • Constraints: • All wireless • No centralized infrastructure • Mobile • Scalable • Examples: • Rooftop networks • Sensor networks • Rapid deployment Douglas S. J. De Couto — MIT Lab for Computer Science

  3. A F D E C G J I H B Grid Research Problems “A to J: Hello!” • Challenges: • Routing • i. Forwarding • ii. Path selection • Capacity • Power Douglas S. J. De Couto — MIT Lab for Computer Science

  4. A F D B E C G J I H Finding Good Routes • Shortest path routing finds bad links A’s max range Douglas S. J. De Couto — MIT Lab for Computer Science

  5. Link Quality isn’t Bimodal 17 node indoor network Broadcast 4-byte UDP packets Douglas S. J. De Couto — MIT Lab for Computer Science

  6. Indoor Testbed • 17 static nodes on 5th/6th floors • iPaq handhelds 5 5 6 5 6 5 5 5 6 5 6 6 5 6 5 5 5 wired gateway Douglas S. J. De Couto — MIT Lab for Computer Science

  7. Effects of Bad Links • Problem: lossy links slow throughput and reduce capacity • 802.11 features positive ACKs & link-level retransmissions • Lost packet transmissions waste time and spectrum • Solution: choose routes other than shortest path • A longer route may have better links • Use different metric than hopcount Douglas S. J. De Couto — MIT Lab for Computer Science

  8. Proposed Route Metric: Transmission Count vs. • Tradeoff: longer route with fewer retransmits vs. shorter route with more retransmits • Quantify tradeoff by estimated transmission count metric • Per-packet tx count = number of failed tx + number of successful tx • Normally exactly 1 successful tx per packet at each hop • Metric features • Route metric is sum of link metrics • Directly measures spectrum use of route • Estimate as tx_count = 1/(fwd_rate * rev_rate) Douglas S. J. De Couto — MIT Lab for Computer Science

  9. Geographic Forwarding C’s radio range A • Packets addressed to idG,locationG • Next hop is chosen from neighbors to move packet geographically closer to destination location • Per-node routing overhead constant as network size (nodes, area) grows • Requires location service, which adds overhead D F C G B E Douglas S. J. De Couto — MIT Lab for Computer Science

  10. Grid Location Service (GLS) E L H B D J G A • Each node has a few servers that know its location • 1. Node D sends location updates to its servers (B, H, K) • 2. Node J sends a query for D to one of D’s close servers • Spatial hierarchy makes GLS space and communications overhead O(log n) “D?” I F K C Douglas S. J. De Couto — MIT Lab for Computer Science

  11. s n s s s s s s s s The GLS Spatial Hierarchy All nodes agree on the global origin of the grid hierarchy All nodes agree on square sizes Douglas S. J. De Couto — MIT Lab for Computer Science

  12. Understanding Network Capacity • Measure with “packet-hops”: number of simultaneous packet transmissions • Total capacity scales with number of nodes • Spatial reuse allows capacity to scale with area • Maximum node density  adding nodes adds area • Simulation result: 802.11 ad hoc total capacity can scale • Per-node capacity depends on communications patterns • Global communication won’t scale • Local communication will (e.g. GLS) Douglas S. J. De Couto — MIT Lab for Computer Science

  13. Understanding Per-node Capacity • Network provides O(n) packet-hops, n nodes • “Random” communication won’t scale • Expected path length = O(sqrt n)  each packet uses O(sqrt n) packet-hops • Per-node packet rate = n / (n * sqrt n) = O(1 / sqrt n) • Local communication scales • Expected path length = O(1) • Per-node packet rate = n / (n * 1) = O(1) • GLS • Expected path length = O(log n) • Per-node packet rate = n / (n * log n) = O(1 / log n) Douglas S. J. De Couto — MIT Lab for Computer Science

  14. Grid Monitor Douglas S. J. De Couto — MIT Lab for Computer Science

  15. Rooftop Testbed 6 5 • Omnidirectional antennas • LCS/AI node has directional (yagi) antenna 4 3 2 1 LCS/AI Douglas S. J. De Couto — MIT Lab for Computer Science

  16. Grid Summary • Grid protocols are • Self-configuring • Easy to deploy • Scalable • Adaptable • http://www.pdos.lcs.mit.edu/grid Douglas S. J. De Couto — MIT Lab for Computer Science

  17. SPAN: Reducing Power Consumption • Reduce network power consumption by turning off radios • Select coordinators to stay powered on • Maintain network connectivity • Preserve capacity • Routes composed of coordinator nodes • Distributed election algorithm elects, rotates, and withdraws coordinators • Simulation result: network lifetime doubled Douglas S. J. De Couto — MIT Lab for Computer Science

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