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Wireless Capacity

Wireless Capacity. A lot of hype. Self-organizing sensor networks reporting on everything everywhere Bluetooth personal networks connecting devices City wide 802.11 networks run by individuals and companies No more Cat5 in homes/businesses. Capacity.

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Wireless Capacity

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  1. Wireless Capacity

  2. A lot of hype • Self-organizing sensor networks reporting on everything everywhere • Bluetooth personal networks connecting devices • City wide 802.11 networks run by individuals and companies • No more Cat5 in homes/businesses

  3. Capacity • As systems researchers, the most glaring question is “Does this scale?” • What do we mean by scaling? • What is the aggregate network capacity? • What is the per-node capacity for node-originated data

  4. Observed capacity • Das et al. simulation of 100 nodes • 2Mbps base throughput • 7 simultaneous transmissions • Per-node bandwidth few kbps • Others see similar capacity

  5. Physical limit • Competition for physical bandwidth • Signal power degrades with distance as 1/ra for some a>2 As an order of magnitude, in ns transmission range ~250 meters, interference ~550 meters

  6. Network capacity • Upper bound total capacity,arbitrary destination • Why? Intuitively, assuming constant density: total area/capacity ~n, diameter/average path length ~n • Global scheduling can achieve:

  7. What is the limit? • As density increases, the number of nodes a packet interferes with increases • Constant power, nodes per unit area larger • Lower power/more hops, total transmissions increase

  8. 802.11 Chain propagation (simulation) • Achieve 1/7 of maximum 1.7Mbps • Expected ¼ of maximum 1.7Mbps

  9. MAC inefficiency? • 802.11 works until offered load exceeds capacity • Waste bandwidth at first node • Waste time backed off

  10. Simulation vs. Reality

  11. Solutions? • Smaller networks? • Suggested in papers • Only helpful if lower overall use • Add extra repeater nodes • Requires exorbitant number of nodes • Factor of k repeaters, k extra per-node capacity • Local communication patterns? • Widespread base stations • Local data processing • Be sneaky

  12. Traffic pattern Power law traffic pattern Per-node capacity a<-2 Approaches constant a=-2 O(1/log(n)): GLS uses this a>-1 O(1/n)

  13. Be sneaky • If we achieve three properties, we should be able to get scalability • All direct communication is local • Message paths are short (preferably O(1)) • Squander no opportunities to send • Can we still achieve full connectivity? • Maybe: Mobility

  14. Mobility • Nodes move randomly • Ergodic (uniform space filling) motion • No proof that this is NECESSARY • Persistent communication patterns • Random source/destination patterns • Unlimited data • Buffering • Nodes can buffer data

  15. Mobility • To achieve scalability, we want three properties • All direct communication is local • Send messages only to nearest neighbor • Distant communication depends on chance movement • Message paths are short (preferably O(1)) • Squander no opportunities

  16. Mobility • To achieve scalability, we want three properties • All direct communication is local • Message paths are short (preferably O(1)) • Never forward along paths longer than 2 hops • Squander no opportunities

  17. Mobility • To achieve scalability, we want three properties • All direct communication is local • Message paths are short (preferably O(1)) • Squander no opportunities Send data through everyone • Whenever you are near any node, give it a (new) packet for the destination. • On average should have data for every possible destination

  18. Requirements • Know closest node/range • Schedule local transmissions • They found the standard MAC may be ok • Buffering • Scales with radio bandwidth? • Scales with expected time to see a destination node?

  19. Model • Is this useful? • Potentially very long time to delivery • Potentially wide variance in delivery times • Unknown dependence on movement model • Space filling unrealistic(destructive to homes) • Another submission claims that travel along random line segments also works • Unclear generalization to multiple hops • Static population model/bounded movement model unrealistic for many random movement models • Existing applications seem unlikely consumers

  20. What next? • Radio people • MAC layers tuned to ad hoc mode • Wasn’t clear from results presented this is more than a moderate constant factor • Systems/applications people • Communication patterns with good locality • Take advantage of external sources of bandwidth (fiber optics or station wagons of tapes)

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