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Proposed ad hoc Routing Approaches. Conventional wired-type schemes (global routing, proactive): Distance Vector; Link State Proactive ad hoc routing: OLSR, TBRPF On- Demand, reactive routing: DSR (Source routing), MSR AODV (Backward learning) Scalable routing : - PowerPoint PPT Presentation
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Proposed ad hoc Routing Approaches• Conventional wired-type schemes (global routi
ng, proactive):– Distance Vector; Link State
• Proactive ad hoc routing:– OLSR, TBRPF
• On- Demand, reactive routing:• DSR (Source routing), MSR • AODV (Backward learning)
• Scalable routing :– Hierarchical routing: HSR, Fisheye– OLSR + Fisheye– LANMAR (for teams/swarms)
• Geo-routing: • GPSR, GeRaF, etc• Motion assisted routing
Georouting - Key Idea
• Each node knows its geo-coordinates (eg, from GPS or Galileo)
• Source knows destination geo-coordinates; it stamps them in the packet
• Geo-forwarding: at each hop, the packet is forwarded to the neighbor closest to destination
• Options:– Each node keeps track of neighbor coordin
ates– Nodes know nothing about neighbor coordi
nates
Geographic Routing: Greedy Routing
S D
Closest to D
A
- Find neighbors who are the closer to the destination- Forward the packet to the neighbor closest to the destination
Greedy Perimeter Stateless Routing for Wireless Networks (GPSR)
– key elements• Greedy forwarding
– Each nodes knows own coordinates– Source knows coordinates of destination– Greedy choice – “select” the most forward
node
Greedy Forwarding does NOT always work
If the network is dense enough that each interior node has a neighbor in every 2/3 angular sector, GF will always succeed
GF fails
Got stuck? Perimeter forwarding
> Greedy forwarding failure. x is a local maximum in its geographic proximity to D; w and y are farther from D.> Node x’s void with respect to destination D
Greedy Perimeter Forwarding
D is the destination; x is the node where the packet enters perimeter mode; forwarding hops are solid arrows;
GPSR vs DSR
TCP over GPSR, AODV, DSR and DSDV
Speed(m/s)
Th
rou
gh
pu
t (K
bp
s)
GPSR commentary• Very scalable:
– small per-node routing state – small routing protocol message complexity– robust packet delivery on densely deployed,
mobile wireless networks• TCP is extremely sensitive to path breakage (ti
meout) -- It does very well with georouting• Outperforms DSR and AODV• Drawback: it requires knowledge of dest geo c
oordinates (explicit forwarding node address)– Beaconing overhead– nodes may go to sleep (on and off) in senso
r networks
Energy-Aware geographic routing protocols in ad hoc networks
Next-hop selection in geographic routing
• Different metrics determine different performance
ds
a
b
c
Next-hop selection in geographic routing
va
m
q
k
• Select a node so that is minimized. Here p stands for power.
Next-hop selection in geographic routing
• 1. w is selected as anchor node.
• 2. find a least cost path from u to w.
Geo Location Service
Yinzhe YuYinzhe Yu, et al : , et al : Enhancing Location Service Enhancing Location Service Scalability With HIGH-GRADEScalability With HIGH-GRADE , MASS 2004, Oct 2004
Position-Based Routing
• Assuming each nodes is aware of its own geographical “location” and those of its neighbors
• Forwarding packets based on destination location, using simple greedy forwarding and recovery strategies (Face2, GPSR)
Basic Problem
For a node B wishing to communicate with another node A, how to discover current location of A?
How does A choose a set of nodes as its location servers, and how to update these servers as A moves around? (Location Server Organization)
What exact information about A’s location are stored on its location servers? (Location Information Granularity)
How does B find appropriate server(s) of A to obtain its location?
What is a Location Service?
• A pre-requisite of Position-Based Routing is a Location Service– Allows a source node to obtain the location of a destination before
data traffic follows
• Location Service is a cooperative service – Each node in the MANET stores the current locations of some
other nodes in the network, serving as their location server
– A node updates its location servers as it moves around
– A node trying to communicate with another node queries that node’s location servers to get its current location
Location Server Organization
A
BA
B
Flat structure:
SLURP – Woo and Singh, 2001.
Two-level structure:
SLALoM – Cheng et al. 2002.
DLM – Xue et al. 2002.
Multi-level Hierarchical:
GLS – Li et al., 2001.
A
B
Proposed : HIGH-GRADE
• HIerarchical Geographical Hash with multi-GRained Address DElegation – A better scheme that incorporates good design ch
oices, and provides better scalability
• Possible application: geo-routing in the urban vehicular grid
HIGH-GRADE Location Update
A
• HIGH-GRADE divides a network area recursively into levels of “squares”.
• Each node chooses location servers around some hash points, one in each level of square.
• Each location server stores the information of “which next level square does A resides in ?”.
Location Info at Hash Points
E
F D
C
G
H
When there is no node at the exact location of the hash point, the update packet travels around the “perimeter” of the hash point and the location information is stored on all nodes on the perimeter.
HIGH-GRADE Location Query
A
B
• A querying node B uses the same hash functions to try potential location servers
• Once a location server is found, it follows a series of servers at smaller and smaller area to pin-point A’s location
• The total distance traveled by a location query message is proportional to the side length of A and B’s least common square
Analysis: Model assumption
• A common set of assumptions to analyze costs of maintaining and using a location service– A network with N nodes in an area of A
• constant node density .
– Average progress towards the destination point in each packet forwarding step is z.
– Simplified random way-point mobility model with no pause time. Average node speed is v.
AN
Metrics
• Location Update Cost– Number of forwarding operations each
node needs to perform in a second to handle the location update packets.
• Location Query Cost– Number of forwarding operations each
node needs to perform in a second to handle the location queries.
• Storage Cost– Number of location records a node
needs to store as a location server.
Summary of Results
HIGH-GRADE GLS DLM SLURP SLALoM
Location Update Cost
O ( v log N )
Location Query Cost (uniform traffic)
O ( log N )(localized traffic)
(uniform traffic)
O ( log N )(localized traffic)
(both) (both) (both)
Storage Cost O ( log N ) O ( log N ) O ( 1 )
NO NO 3 NO 3 NO NO
3 2NO 3 NO
3 NvO NvO 3 NvO NvO
Observations:
1. Design of a location service involves tradeoffs among all three metrics.
2. Not all schemes exploit the benefits of a localized traffic equally well.
3. For localized traffic HIGH-GRADE achieves impressive asymptotic scalability.
Simulation
• Compare GLS and HIGH-GRADE– Confirm analytical results
• ns2 with CMU Monarch extensions– N = 100 ~ 600– Node density fixed at 100/km2
– Transmission range is 250 m– Mobility model: random waypoint (w/o pause)
• Maximum speed 10~30 m/s
– Load• Each node generates 15 location queries for random destin
ation nodes during a 300 sec simulation time
Location Update Cost vs. N and v
HIGH-GRADE GLS
Location Update Cost O ( v log N ) NvO
A High-Throughput Path Metric for Multi-
Hop Wireless Routing
D. S. J. D. Couto, D. Aguayo, J. Bicket, and R. Morris. A High-Throughput Path Metric for Multi-Hop Wireless Routing. In Proceedings of ACM MobiCom, 2003.
Background
The most commonly used metric is minimum hop-count.
Links in route share radio spectrum Extra hops reduce throughput
Throughput = 1/2
Throughput = 1
Throughput = 1/3
If algorithm ignoring loss sees A>C as a link, it’ll choose A>C instead of A>B>C.
Hop-count alone is insufficient
Minimize hop-count
100%Delivery ratio = 100%
20%
A
B
C
Trade-off between hops and distance (Thus lossiness)- need to account for delivery rate in routing.
A test A test was setup to see how the minimum hop count
metric REALLY works. Note this was an experimental test, not a simulation.
During the test each packet sent contained 193 bytes (134 of data)
A “best” route was determined by trying 10 different routes and seeing which was best.
5th floor
6th floor
29 PCs with 802.11b radios (fixed transmit power) in ‘ad hoc’ mode
Indoor wireless network
4th floor
3rd floor2nd floor
Testbed UDP throughput• The values above
225 correspond to pairs that communicated along single-hop paths;
• those at or below 225 correspond to multi-hop paths.
above 225 pkts/ s
below 225 pkts /s
Results of the Test
2 Regions Above 250 PPS: 1 ho
p links Below 250 PPS: Multi
hop links
Note the 0 values for 1/5 of the packets, even though a route exists
above 225 pkts/ s
below 225 pkts /s
2 hops
3 hops
4 hops
What throughput is possible?
Routing protocol
‘Best’
when routing multi-hop, there is some throughput reduction to be expected
For one-hop routes, we get approximately as good as we deserve
For the rest of the multi-hop routes, DSDV still finds routes with much lower throughput than is possible
The shortest path does not yield the highest throughput
Paths from 23 to 36
selects randomly from the shortest hopcount routes is unlikely to make the best choice
Challenge: many links are lossy
Smooth link distribution complicates link classification.
One-hop broadcast delivery ratios
‘Good’
‘Bad’
Challenge: many links are asymmetric
Many links are good in one direction, but lossy in the other.
Broadcast delivery ratios in both link directions.
Very asymmetric link.
Minimize total transmissions per packet
(ETX, ‘Expected Transmission Count’)
New metric: ETX
Link throughput 1/ Link ETX
Delivery Ratio
100%
50%
33%
Throughput
100%
50%
33%
Link ETX
1
2
3
Calculating ETX
Assuming 802.11 link-layer acknowledgments (ACKs) and retransmissions: P(TX success) = P(Data success) P(ACK success)
measured fwd delivery ratio rfwd measured rev delivery ratio rrev
Link ETX = 1 / P(TX success) = 1 / [ P(Data success) P(ACK success) ]
Link ETX 1 / (rfwd rrev) Why measure both ACK and Data Success?
Route ETX
Route ETX
1
2
2
3
Route ETX = Sum of link ETXs
5
Throughput
100%
50%
50%
33%
20%
Measuring Delivery Ratios
Each node broadcasts small link probes (134 bytes), once per second
Nodes remember probes received over past 10 seconds
Reverse delivery ratios estimated as
rrev pkts received / pkts sent Forward delivery ratios obtained from neighb
ors (piggybacked on probes)
ETX improves DSDV throughput
better
DSDV overhead
‘Best’
DSDV+ETX
DSDV+hop-count
DSR with ETX
DSR+ETX
‘Best’
DSR+hop-count
Summary
ETX is a new route metric for multi-hop wireless networks
ETX accounts for Throughput reduction of extra hops Lossy and asymmetric links Link-layer acknowledgements
ETX finds better routes!