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Wireless Capacity: Scaling Challenges and Solutions in Self-Organizing Networks

Explore the hype around wireless capacity in self-organizing networks and the challenges of scalability. Find out what limits network capacity, observed challenges, physical limitations, and more. Delve into solutions, simulation vs. reality, MAC inefficiency, and traffic patterns. Discover how mobility impacts scalability, the importance of local communication, and potential strategies for sending data efficiently. Dive into the implications for existing models and the future of wireless networks.

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Wireless Capacity: Scaling Challenges and Solutions in Self-Organizing Networks

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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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