Transcript Wireless networks

Wireless networks simulation
• Performance evaluation of a protocol for ad hoc networks is usually
performed by simulating the wireless network. Simulation provides
the researcher with a number of significant benefits, including
repeatable scenarios, isolation of parameters and exploration of a
variety of metrics.
• In particular, A Wireless Network simulation MUST model:
– Mobile Nodes’ characteristics such as: transmission range,
limited buffer capacity, battery power limitations, signals radio
propagation ect.
– Communication traffic model (what kind of data flow is there?)
– Mobility model (movements of the users i.e. devices)
– a few others
• This
presentation will focus on the importance of
the MOBILITY PATTERN when simulating adhoc networks [1] ...
Trace vs. Synthetic mobility models (1)
• Trace
Are those mobility models observed in real life scenarios. For
instance, if a mobile phone carrier had the ability to trace the exact
movements and behaviors of all its customers carrying their phone
for a given period of time, a trace would be obtained.
Pros
– Accurate information gathered especially for scenarios with many nodes
Cons
– Privacy issues may prohibit their collection and distribution
– Simulations can’t be run when new environments haven’t even been
created yet.
• Synthetic (used when tracing is not possible)
Models which attempt to realistically represent the behaviors of the
MNs without use of traces.
Trace vs. Synthetic mobility models (2)
• Tracing
approach in Ad-Hoc vs. GSM
– Requires an additional effort to be performed in advance.
– Currently there exist traces for GSM but not for Ad-Hoc
Networks.
While in GSM networks it is enough to trace the movements between
a cell to another, in Ad-hoc networks is required to trace the mobility
with a strict precision during the motion. Hence, a precise positioning
system is needed on each device to be traced. As long as GPS is not
commonly provided on these devices (Laptop, PDA) the tracing is
rather tricky to obtain.
• Synthetic
For the several reasons mentioned, the Synthetic approach is the
only one which researcher can currently follow with ad-hoc. We are
going to focus on these mobility models that are commonly used.
Synthetic mobility models classification
• Entity
MMs
A node’s movement does not influence in anyhow other nodes’
movements. Nodes move independently from each other.
— Random Walk ( & its probabilistic version)
— Random Waypoint
— Random Direction
— Gauss-Markov
— City section mobility models
• Group MMs
represent MNs whose movements are dependent. Used when MNs
collaborate together to accomplish a common goal. Typical situations do
exist in military environments (soldiers move together)…
— Column MM
— Nomadic Community
— Pursue
— Reference Point Group MM
Random Walk (1)
It wants represent the movement of the entities in an unpredictable way. In
particular, a node moves from its current location to a new one by
randomly choosing:
— Direction between [0,2pi]
— Speed between [MinSpeed, MaxSpeed]
— Either duration of movement tm OR distance d
• Direction and speed are both uniformly distributed
• A node which “crashes” against the boundary keeps on moving on an
opposite direction between [0,pi] depending on the incoming one.
OBSERVATIONS:
1) Nodes start moving at t=0. Choosing a DURATION implies that all the
nodes change directions at the same time and travel for different distances. In
contrast, choosing a DISTANCE implies same distances but different duration.
2) The pattern is memory-less i.e. current speed and direction do not depend
upon the previous ones. Therefore, there will be sharp and sudden turns.
3) Short tm or d lead the nodes to move around their current location. Unless it
is necessary to study a semi-static network, they MUST be chosen large.
Random Walk (2)
Example of a travelling pattern of a mobile node using the 2D
Random Walk MM
Surface size 300 x 600 m, tm = 60s
Random Waypoint (1)
It is a variation of Random Walk. It introduces the concept of pause time. A
node randomly chooses (Parameters uniformly distributed):
— Pause Time (to wait before resuming the movement) [Pmin,Pmax]
— Direction [0,2pi]
— Speed [Minvel,Maxvel]
— Destination point (x,y) to reach..
OBSERVATIONS:
1) the duration of the movement depends on the destination point chosen.
2) nodes do not start roaming all together unless Pmin = Pmax = 0.
3) the pattern becomes a Random Walk when (Pmin = Pmax = 0 ) AND
([Minvel,Maxvel] = [MinSpeed, MaxSpeed])
4) it is the most commonly used MM in ad-hoc network simulation studies
(often times is modified).
– It needs particular attention to choose the initial locations. Discard the initial part
of the simulation OR save the node’s location OR do not place the nodes randomly.
– The choice of Pauses and Speeds is relevant. Fast nodes and long pauses
produces a more stable network than slow nodes and short pauses.
– The most argued issue is that nodes are more likely to be in the central part of the
topology rather than close to the bounds.
Random Waypoint (2)
Example of a travelling pattern of a mobile node using the
Random Waypoint MM
• Clearly the motion is
centrally happening
• Nodes appear to
converge, disperse and
converge again
• Nodes tend to have
many neighbors when in
the center and almost
none when they disperse.
Surface size 300 x 600 m
Random Direction (1)
Designed to overcame the concentration of the nodes of which R. Waypoint
suffers. Nodes start moving by choosing
— Direction between [0,2pi]
— Speed between [MinSpeed, MaxSpeed]
• Nodes will travel till the bound is reached. On this position they’ll stand
for a pause time before leaving to a New-Direction [0,pi].
OBSERVATIONS:
1) Nodes are forced to basically stay away from the center for the most of the
time. In fact, they ALL pause somewhere on the perimeter.
– Implications:
• Average Hop count for Data-packets will be much higher than in R.
Waypoint or R Walk. (nodes are on average far from each others)
• Higher probability to have Network partition (especially with few
MNs).
Random Direction (2)
Example of a travelling pattern of a mobile node using the
Random Direction MM
Surface size 300 x 600 m
Gauss-Markov (1)
• It creates movements which are dependent on node’s current
speed and direction. The idea is to eliminate the sharp and
sudden turns present in the R. Walk and R. Waypoint even by
keeping a certain degree of randomness. At fixed intervals of
time n new direction dn and speed sn are chosen as:
sn  f (sn1 )
d n  f (d n1 )
In addition…
• dn and sn keep into account a parameter  to tune the level of
randomness in making the decisions
• the desired statistical distributions SX and DX , for the random variables
Speed and Direction MUST be chosen beforehand
• some “tricks” are used to force the MNs to stay away from the edges
Gauss-Markov (2) Example
• I describe the implementation used in [2]:
sn  sn 1  (1   ) s  (1   2 ) s x n1
d n  d n 1  (1   )d  (1   2 )d x n1
WHERE:
•
is the tuning parameter chosen in [0,1]
• s, d are constants. The mean values for SX and DX (both Gaussian)
• sXn-1 and dXn-1 are the random variables from SX and DX
Observations
• with   0 the movements are totally random, with   1 they are
linear.
• the trick to avoid the edges is to choose d differently when near to the
edge
Gauss - Markov (3)
Example of a travelling pattern of a mobile node using the
Gauss Markov MM
• n=1 sec
•   0.75
• sXn-1 and dXn-1 are
chosen from a gaussian
distribution with mean 0
and std.dev. 1
• s is fixed at 10m/s
• d chosen according to
the current position
Surface size 300 x 600 m
Probabilistic Random WALK
• The movements of the nodes are bases on a matrix of
probabilities P.
• x and y coordinates vary according to a state chart with 3 states:
– State 0: the coordinate does not vary
– State 1: the coordinate decreases i.e. Step Backward
– State 2: the coordinate increases i.e. Step Forward
The probabilities to switch from a state to another are:
 p (0,0)

P   p (1,0)
 p ( 2,0)
p (0,1)
p (1,1)
p ( 2,1)
p (0,2) 

p (1,2) 
p ( 2,2) 
Each non zero probability is a transition in the state chart.
A possible implementation is shown in Chiang [3] (speed const.)
Boundless simulation area
• Sometimes can be interesting to discard the border of the
simulation area. In fact, the perimeter of it does affect nodes
when they reach it.
• Nodes move on the torus internal surface
• It is not any longer needed to describe the node’s behavior
on the border.
• Radio signal propagation must be modelled accordingly
City mobility model
• it represents a section of a city where an ad-hoc network
operates [4]. It models factors as:
– A street network
– A set of buildings
– Destination points ( where nodes randomly start from and
hear for )
• in addition it could include
– safe driving characteristics such as speed limit, minimum
distance allowed between pairs of nodes.
– High speed road along the border of the simulation area
– A boundless topology to represent the whole city
• Pros
– high realistic motion
• Cons
– tricky to fully describe
Column MM
• it is a group MM, Suitable for representing soldiers marching:
– Initially a reference point is chosen and assigned to each
MNs. The peculiarity is to choose points on a line (culumn)
– Nodes are subsequently allowed to move around their
reference point according to an Entity MM
– Reference points change:
New_ref_point = Old_ref_point + advance_vector
where: advance_vector = (x,y)
When this happens, MNs move toward their ref. Point to start
roaming around.
Nomadic community MM
• it is a group MM, Suitable for representing Nomadic
Movements (a class of students visiting a museum ect.):
– Initially a reference point is chosen and SHARED between
all the MNs.
– Nodes are subsequently allowed to move around it
according to an Entity MM
– the reference point randomly changes causing the nodes to
firstly reach it and then to roam around it
Column vs. Nomadic community MM
• Culumn
– A ref. point per MNs
– strict motion around
(short trips, often
changes in dir. & speed
• Nomadic C.
– A unique ref. Point
– more space to roam
(longer lasting
movements)
Pursue MM
• A set of MNs want to catch a running away MN.
NewPosition = OldPosition + Dist + RandomDist
Dist is a vector (x,y) whose components are chosen in
[MinDX, MaxDX] and [MinDY, MaxDY].
RandomDist is a vector (x,y) obtained via Entity MM.
Reference Point Group MM (1)
• A set of MNs want to move in group. The group has a
logical center which moves.
• Each MN is assigned a moving reference point. Nodes
randomly move around it.
Two kinds of motion:
i) Group Motion is represented by a vector GM
ii) nodes Random Motion is represented by RM
The group logical center is assigned a new position at regular intervals.
Subsequently the RPs locations are updated accordingly. Finally MNs
locations are computed based on GM and RM
• GM can be EITHER predefined OR randomly chosen
• RM is randomly chosen. The direction is uniformly distributed
in [0,2pi], the length as well with a radius centered at the
Reference point’s location.
Reference Point Group MM (2)
• Designed to represent avalanche rescue. (humans &
dogs)
• The MNs movements are characterized by the group
logical center’s motion.
• Many particular possible implementations. For
instance:
1) Nomadic Community MM (no separation
between RPs)
2) Column MM (by disposing the RPs in a column)
3) Pursue MM (no separation between RPs)
Simulation to compare the MMs
• In [1] they have done a few simulation experiments to
evaluate and compare:
–
–
–
–
Random Walk
Random Waypoint
Random Direction
RPGM ( Between-groups vs. Intra-group + Between-groups
• Simulation Parameters: • Communication model:
i) # nodes 50
ii) Routing Prot. DSR
iii) Sim. Time 1000 sec
iv) Random initial locations
v) Sim. Results are shown by
averaging 10 trials
i) 20 UDP pairs S/R (CBR)
ii) rate 1pck/s
iii) pkt size 64 bytes each
* RPGM Inter+Intra sends:
-1pkt/2sec Inter (20 pairs
distributed in 16 groups)
-1pkt/5sec Intra.
Performance metrics
• They evaluated DSR’s performance by measuring:
–
–
–
–
data packet delivery ratio Rcv/Snd
end-to-end delay
average hop count
protocol overhead
• Their objective is to show how the protocol
performance changes depending on the mobility
model under study. In particular, they observe the
significant metrics vs. average speed value.
The results obtained (1)
• R. waypoint is the model which less stresses DSR. They
observed: highest data packet delivery ratio, lowest endto-end delay, lowest average hop count.
Motivation: ‘cos nodes often travel through (or to) the
center. The model is Idealistic rather than Realistic!
• In contrast, Last ranked is R. walk. Lowest data packet
delivery ratio, Highest end-to-end delay, highest average
hop count were observed. Reason: nodes move to the
boarder and stop there i.e. nodes are on average far apart.
Network partition are more likely to occur.
• DSR performance with other Entity MMs are ranked
between the presented extremes.
The results obtained (2)
• RPGM pure inter-group communication has roughly the
same Average Hop Count than R-waypoint.
Motivation: both MNs and RPs move according to Rwaypoint MM.
• In contrast, it has much higher end-to-end delay and
lower data packet delivery ratio.
Reason: Since only 16 groups exist, the network will be
sparser than R-waypoint with 50 nodes. Furthermore,
network partitions probably occur.
• RPGM intra+inter comm. Has the lower average hop
count and a high Delivery ratio. The half data sent intra
group, are raising the performance!
The results obtained (3)
Routing ovehead
i) # control packets / Pck_received
ii) # control byte transmitted / Pck_received
it includes control bytes in both control and data packets.
• They noticed that the average hop count is related with
the routing overhead. In fact:
- GRPM had the lowest average hop count, so that’s
the explanation for the model lower routing overhead.
- Likewise, R. walk and R. waypoint had a high hop
count so they have high routing overhead as well.
Conclusions - Importance of the MM
• The protocol performance vary significantly depending upon the
MM being used. It definitely affects a lot!
• The PP vary also in function of the MM’s parameters. Same MM
with different input parameters leads to observe diverse findings.
• GMM: Intra-group communication drastically increases the PP.
Inter-group communication is more tricky to handle.
• The MM should be chosen in a way such that more closely
represents the situation being simulated. Difficulty to know what
is a real-life case. No traces available for ad-hoc networks.
• When the expected real-life scenario is unknown, performance
must be evaluated under multiple MMs.
References
[1] Tracy Camp, Jeff Boleng, Vanessa Davies. A survey of mobility
for ad hoc network research. Dept. of Math. and Computer
Sciences Colorado School of Mines, September 2002.
[2] V. Tolety. Load reduction in ad hoc networks using mobile servers.
Master’s thesis, Colorado School of Mines, 1999.
[3] C. Chiang Wireless Network Multicasting., Phd. Thesis, University of
California, Los Angeles 1998.
[4] F. Bai, N. Sadagopan, A. Helmy, A framework to systematically the
impact of mobility on performance of routing protocols for AdHoc
networks. IEEE Infocom 2003.
Thank you!
Any question?
Write to: Stefano Marinoni
[email protected]
Office: T-B235 lab for theoretical CS