Wireless Networking Using Grid

L C S

Wireless Networking Using Grid

Douglas S. J. De Couto

http://www.pdos.lcs.mit.edu/grid

(revised)


Oxygen Alliance Annual Meeting — June 12 - 13, 2002Douglas S. J. De Couto — MIT Lab for Computer Science

Goal: Build Networks from Chaos

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Examples:1. Rooftop networks2. Sensor networks3. Rapid deployment

Constraints:1. All wireless2. No centralized

infrastructure3. Mobile4. Scalable


Grid Research Problems

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“A to J: Hello!”

Challenges:1. Routing

i. Forwarding ii. Path selection

2. Capacity3. Power


Finding Good Routes

• Shortest path routing finds bad links

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HA’s maxrange


Link Quality isn’t Bimodal

17 node indoor networkBroadcast 4-byte UDP packets


Indoor Testbed

• 17 static nodes on 5th/6th floors

• iPaq handhelds

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wiredgateway


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


Proposed Route Metric: Transmission Count

• 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)

vs.


Geographic Forwarding

• 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

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C’s radio range

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Grid Location Service (GLS)

• 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)

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“D?”


The GLS Spatial Hierarchy

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All nodes agree on the global origin of the grid hierarchyAll nodes agree on square sizes


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)


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)


Grid Monitor


Rooftop Testbed

• Omnidirectional antennas

• LCS/AI node has directional (yagi) antenna

LCS/AI

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Grid Summary

• Grid protocols are– Self-configuring

– Easy to deploy

– Scalable

– Adaptable

• http://www.pdos.lcs.mit.edu/grid



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

Documents

Wireless Networking Using Grid