AdHocTutorial.ppt

ETH Zurich Summer Tutorial Algorithmic Foundations of Ad Hoc Networks 1
Algorithmic Foundations of
Ad Hoc Networks: Part II
Rajmohan Rajaraman, Northeastern U.
Andrea Richa, Arizona State U.
www.ccs.neu.edu/home/rraj/AdHocTutorial.ppt

Many Thanks to…
• Roger Wattenhofer and organizers of
the summer school
• ETH Zurich
• All the researchers whose contributions
will be discussed in this tutorial

Outline
Application
Routing
Link layer
Medium Access
Control
Power
Control
Sensor Network
Protocols
Topology
Control
1
3
2
5
Fundamental limits of ad hoc networks
4

What’s Not Covered?
• Frequency (channel) assignment
– Arises in cellular networks
– Modeled as coloring problems
• Ad Hoc Network Security
– Challenges due to the low-power, wireless, and
distributed characteristics
– Authentication, key sharing,…
– Anonymous routing
• Smart antenna:
– Beam-forming (directional) antenna
– MIMO systems
• Many physical layer issues
• …

Medium Access Control

Medium Access Control Protocols
• Schedule-based: Establish transmission
schedules statically or dynamically
– TDMA: Assign channel to station for a fixed
amount of time
– FDMA: Assign a certain frequency to each
station
– CDMA: Encode the individual transmissions
over the entire spectrum
• Contention-based:
– Let the stations contend for the channel
– Random access protocols

Contention Resolution Protocols
• CSMA (Carrier-sense multiple access)
– Ethernet
– Aloha
• MACA [K 90] (Multiple access collision avoidance)
• MACAW [BDSZ 94]
• CSMA/CA and IEEE 802.11
• Other protocols:
– Bluetooth
– Later, MAC protocols for sensor networks

Ingredients of MAC Protocols
• Carrier sense (CS)
– Hardware capable of sensing whether transmission
taking place in vicinity
• Collision detection (CD)
– Hardware capable of detecting collisions
• Collision avoidance (CA)
– Protocol for avoiding collisions
• Acknowledgments
– When collision detection not possible, link-layer
mechanism for identifying failed transmissions
• Backoff mechanism
– Method for estimating contention and deferring
transmissions

Carrier Sense Multiple Access
• Every station senses the carrier before
transmitting
• If channel appears free
– Transmit (with a certain probability)
• Otherwise, wait for some time and try
again
• Different CSMA protocols:
– Sending probabilities
– Retransmission mechanisms

Slotted Aloha
• Proposed for packet radio environments where
every node can hear every other node
• Assume collision detection
• Stations transmit at the beginning of a slot
• If collision occurs, then each station waits a
random number of slots and retries
– Random wait time chosen has a geometric
distribution
– Independent of the number of retransmissions
• Analysis in standard texts on networking
theory [BG 92]

Ethernet
• CSMA with collision detection (CSMA/CD)
• If the adaptor has a frame and the line is idle:
transmit
• Otherwise wait until idle line then transmit
• If a collision occurs:
– Binary exponential backoff: wait for a random
number  [0, 2i-1] of slots before transmitting
– After ten collisions the randomization interval is
frozen to max 1023
– After 16 collisions the controller throws away the
frame

CSMA for Multihop Networks
• In CSMA, sender decides to transmit
based on carrier strength in its vicinity
• Collisions occur at the receiver
• Carrier strengths at sender and receiver
may be different:
A B C
Hidden Terminal

CSMA for Multihop Networks
• In CSMA, sender decides to transmit
based on carrier strength in its vicinity
• Collisions occur at the receiver
• Carrier strengths at sender and receiver
may be different:
A B C
Exposed Terminal
D

Multiple Access Collision Avoidance
• No carrier sense
• Collision avoidance using RTS/CTS handshake
– Sender sends Request-to-Send (RTS)
• Contains length of transmission
– If receiver hears RTS and not currently deferring,
sends Clear-to-Send (CTS)
• Also contains length of transmission
– On receiving CTS, sender starts DATA transmission
• Any station overhearing an RTS defers until a
CTS would have finished
• Any station overhearing a CTS defers until the
expected length of the DATA packet

MACA in Action
• If C also transmits RTS, collision at B
A B C
RTS

MACA in Action
• C knows the expected DATA length from
CTS
A B C
CTS
Defers until DATA
completion

MACA in Action
• Avoids the hidden terminal problem
A B C
DATA

MACA in Action
• CTS packets have fixed size
A B C D
RTS
Defers until CTS

MACA in Action
• C does not hear a CTS
A B C D
CTS

MACA in Action
• C is free to send to D; no exposed
terminal
A B C D
DATA

MACA in Action
• Is C really free to send to D?
A B C D
DATA RTS

MACA in Action
• In fact, C increases its backoff counter!
A B C D
DATA CTS

The CSMA/CA Approach
• Add carrier sense; C will sense B’s
transmission and refrain from sending RTS
A B C D
DATA

False Blocking
• F sends RTS to E; D sends RTS to C
• E is falsely blocked [B 98, RCS 03]
A
B C D E
RTS
RTS
F

False Blocking
Show that false blocking may lead to temporary
deadlocks
RTS
RTS
RTS
DATA
ACK

Alternative Approach: MACAW
• [BDSZ 94]
• No carrier sense, no collision detection
• Collision avoidance:
– Sender sends RTS
– Receiver sends CTS
– Sender sends DS
– Sender sends DATA
– Receiver sends ACK
– Stations hearing DS defer until end of data transmission
• Backoff mechanism:
– Exponential backoff with significant changes for
improving fairness and throughput

The IEEE 802.11 Protocol
• Two medium access schemes
• Point Coordination Function (PCF)
– Centralized
– For infrastructure mode
• Distributed Coordination Function (DCF)
– For ad hoc mode
– CSMA/CA
– Exponential backoff

CSMA/CA with Exponential Backoff
Busy?
Max
window?
Max
attempt?
Increment
attempt
Discard
packet
Double
window
Transmit
frame
Wait inter-
frame period
Wait
U[0,W]
Increment
attempt
No
No
No
Yes
Yes
Yes
Begin

Performance Analysis of 802.11
• Markov chain models for DCF
• Throughput:
– Saturation throughput: maximum load that
the system can carry in stable conditions
• Fairness:
– Long-term fairness
– Short-term fairness
• Focus on collision avoidance and backoff
algorithms

Analysis of Saturation Throughput
• Model assumptions:
– No hidden terminal: all users can hear one
another
– No packet capture: all receive powers are
identical
– Saturation conditions: queue of each station
is always nonempty
• Parameters:
– Packet lengths (headers, control and data)
– Times: slots, timeouts, interframe space

A Stochastic Model for Backoff
• Let denote the backoff time counter for a
given node at slot
– Slot: constant time period if the channel is idle,
and the packet transmission period, otherwise
– Note that is not the same as system time
• The variable is non-Markovian
– Its transitions from a given value depend on the
number of retransmissions
0 1 2 3 4 5
busy medium
DIFS
)
(t
b
t

t
)
(t
b

A Stochastic Model for Backoff
• Let denote the backoff stage at slot
– In the set , where is the maximum
number of backoffs
• Is Markovian?
• Unfortunately, no!
– The transition probabilities are determined by
collision probabilities
– The collision probability may in turn depend on the
number of retransmissions suffered
• Independence Assumption:
– Collision probability is constant and independent of
number of retransmissions
)
(t
s t
}
,...,
0
{ m m
))
(
),
(
( t
b
t
s

Markov Chain Model
Bianchi 00

Steady State Analysis
• Two probabilities:
– Transmission probability
– Collision probability
• Analyzing the Markov chain yields an
equation for in terms of
• However, we also have
• Solve for and

p
 p
1
)
1
(
1 


 n
p 
 p

Saturation Throughput Calculation
• Probability of at least one transmission
• Probability of a successful slot
• Throughput: (packet length )
n
tr
P )
1
(
1 



n
n
s
n
P
)
1
(
1
)
1
( 1








L
P
P
L
P
P
tr
tr
tr
s

 
)
1
(
L

Analysis vs. Simulations
Bianchi 00

Fairness Analysis
• How is the throughput distributed
among the users?
• Long-term:
– Steady-state share of the throughput
• Short-term:
– Sliding window measurements
– Renewal reward theory based on Markov
chain modeling

Long-Term Fairness
• Basic binary exponential backoff:
– Steady-state throughput equal for all nodes
– However, constant probability (> 0) that
one node will capture the channel
Consider two nodes running CSMA with basic exponential
backoff on a shared slotted channel. Assume that both
nodes have an infinite set of packets to send. Prove that
there is a constant (> 0) probability that one node will
have O(1) throughput, while the other will be unable to
send even a single packet.

Long-Term Fairness
• Basic binary exponential backoff:
– Steady-state throughput equal for all nodes
– However, constant probability (> 0) that
one node will capture the channel
• Bounded binary exponential backoff:
– After a certain number of retransmissions,
backoff stage set to zero and packet retried
• MACAW: All nodes have the same
backoff stage

Short-Term Fairness
• Since focus on successful transmissions, need
not worry about collision probabilities
• The CSMA/CA and Aloha protocols can both be
captured as Markov chains
• CSMA/CA has higher throughput, low short-
term fairness
– The capture effect results in low fairness
• Slotted Aloha has low throughput, higher
short-term fairness
• [KKB 00]

Backoff in MACAW
• Refinement of exponential backoff to improve
fairness and throughput
• Fairness:
– Nodes contending for the same channel have the
same backoff counter
– Packet header contains value of backoff counter
– Whenever a station hears a packet, it copies the
value into its backoff counter
• Throughput:
– Sharing backoff counter across channels causes false
congestion
– Separate backoff counter for different streams
(destinations)

Open Problems in Contention
Resolution
• Throughput and fairness analysis for multihop
networks
– Dependencies carry over hops
– In the “single hop” case nodes get synchronized
since every node is listening to the same channel
– Channels that a node can communicate on differ in
the multihop case
– Even the simplest case when only one node cannot
hear all nodes is hard
• Fairness analysis of MACAW
– All nodes contending for a channel use same backoff
number; similar fairness as slotted Aloha?
– Different backoff numbers for different channels

Transmission and Sensing Ranges
Transmission
range
Sensing/interference range
550m
250m

Effect on RTS/CTS Mechanism
A B C D
RTS

A B C D
CTS

A B C D
DATA

A B C D
DATA RTS

Implications of Differing Ranges
• Carrier sense does not completely eliminate
the hidden terminal problem
• The unit disk graph model, by itself, is not a
precise model
• The differing range model itself is also
simplistic
– Radios have power control capabilities
– Whether a transmission is received depends on the
signal-to-interference ratio
– Protocol model for interference [GK 98]

Power Control

What and Why
• The ability of a mobile wireless station
to control its energy consumption:
– Switching between idle/on/off states
– Controlling transmission power
• Throughput:
– Interference determined by transmission
powers and distances
– Power control may reduce interference
allowing more spatial reuse
• Energy:
– Power control could offer significant energy
savings and enhance network lifetime

The Attenuation Model
• Path loss:
– Ratio of received power to transmitted
power
– Function of medium properties and
propagation distance
• If is received power, is the
transmitted power, and is distance
• where ranges from 2 to 4
)
( 
d
P
O
P T
R 
R
P T
P
d


Interference Models
• In addition to path loss, bit-error rate of a
received transmission depends on:
– Noise power
– Transmission powers and distances of other
transmitters in the receiver’s vicinity
• Two models:
– Physical model
– Protocol model

The Physical Model
• Let denote set of nodes that are
simultaneously transmitting
• Let be the transmission power of node
• Transmission of is successfully received by
if:
• is the min signal-interference ratio (SIR)




 
i
k
k
k
i
i
Y
X
d
P
N
Y
X
d
P
)
,
(
)
,
(

}
{ i
X
i
P i
X
i
X Y

The Protocol Model
if for all
• where is a protocol-specified guard-zone to
prevent interference


)
,
(
)
1
(
)
,
( Y
X
d
P
Y
X
d
P
k
k
i
i



i
X Y

k

Scenarios for Power Control
• Individual transmissions:
– Each node decides on a power level on the basis
of contention and power levels of neighbors
• Network-wide task:
– Broadcast
– Multicast
• Static:
– Assign fixed (set of) power level(s) to each node
– Topology control

Review of Proposed Schemes
• Basic power control scheme
• PCM
• POWMAC
• -PCS
• PCMA
• PCDC

}Energy
}Throughput and energy
} Dual channel schemes

The Basic Power Control Scheme
• The IEEE 802.11 does not employ
power control
– Every transmission is at the maximum
possible power level
• Transmit RTS/CTS at
• In the process, determine minimum
power level needed to transmit:
– Function of sender-receiver distance
• DATA and ACK are sent at level
max
P
max
P
P
d
P

Deficiency of the Basic Scheme
A B C D

Power Control MAC (PCM)
• RTS/CTS at
• For DATA packets:
– Send at the minimum power needed, as
in the basic scheme
– Periodically send at , to maintain the
collision avoidance feature of 802.11
• ACK sent at power level
• Throughput comparable to 802.11
• Significant energy savings [JV 02]
max
P
P
P
max
P

POWMAC
• Access window for RTS/CTS exchanges
• Multiple concurrent DATA packet
transmissions following RTS/CTS
• Collision avoidance information
attached in CTS to bound transmission
power of potentially interfering nodes
• Aimed at increasing throughput as well
as reducing energy consumption
• [MK 04]

-PCS
• IEEE 802.11
• Basic power control scheme
• -PCS: [JLNR 04]
0
max d
P
P 


d
P 


)
/
( max
max d
d
P
P
d
P



 

0

• Simulations indicate:
– in the range 2-3 provides best performance
– 30-40% increase in throughput and 3-fold
improvement in energy consumption
– Fair over varying distance ranges



Dual-Channel Schemes
• Use a separate control channel
• PCMA [MBH 01]:
– Receiver sends busy tone pulses advertising
its interference margin
• PCDC [MK 03]:
– RTS/CTS on control channel
• Signal strength of busy tones used to
determine transmission power for data

Open Problems in Power Control
• Develop an analytical model for measuring the
performance of power control protocols
– Model for node locations
– Model for source and destination selections: effect of
transmission distances
– Interaction with routing
– Performance measures: throughput, energy, and
fairness

Topology Control

Connectivity
• Given a set of nodes
in the plane
• Goal: Minimize the
maximum power
needed for
connectivity
• Let denote
the power function
• Induced graph
contains edge if

Connectivity
• To obtain a given
topology , need
• Goal: Minimize the
maximum edge length
• MST!
– MST also minimizes the
weight of the max-
weight edge
• Find MST and set

Connectivity: Distributed Heuristics
• Motivated by need to address mobility [RR 00]
• Initially, every node has maximum power
• Nodes continually monitor routing updates to
track connectivity
• Neighbor Reduction Protocol:
– Each node attempts to maintain degree within a
range, close to a desired degree
– Adjusts power depending on current degree
– Magnitude of change dependent on difference
between current and desired degree
• Neighbor Addition Protocol:
– Triggered if node recognizes graph not connected
– Sets power to maximum level

Connectivity: Total Power Cost
Given a set of nodes in the plane,
determine an assignment of power levels
that achieves connectivity at minimum
total power cost

Bounded-Hops Connectivity
• Goal: Minimize the total power cost needed to
obtain a topology that has a diameter of at
most hops [CPS 99, CPS 00]
• Assume
• Lower bound:
– If minimum distance is , then total power cost is at
least
• Upper bound:
– If maximum distance is , then total power cost is at
most

K-Connectivity
• Goal: Minimize the maximum transmission
power to obtain a k-connected topology
• Critical transmission radius
– Smallest radius r such that if every node sets its
range to r then the topology is k-connected
• Critical neighbor number [WY 04]
– Smallest number l such that if every node sets its
transmission range to the distance to the lth nearest
neighbor then the topology is k-connected
• Characterization of the critical transmission
radius and critical neighbor number for random
node placements [WY 04]

Energy-Efficient Topologies
• Goal: Construct a
topology that contains
energy-efficient paths
– For any pair of nodes,
there exists a path
nearly as energy-
efficient as possible
• Constraints:
– Sparsity
– Constant degree
– Distributed construction

Formalizing Energy-Efficiency
• Given a subgraph of , the complete graph
over the nodes:
– Define energy-stretch of as the maximum, for all
and , of the ratio of the least energy path between
and in to that in
• Variant of distance-stretch
• Since , a topology of distance-stretch
also has energy-stretch

Spanners
• Spanners are topologies with O(1) distance
stretch
• Extensively studied in the graph algorithms
and graph theory literature [Eppstein 96]
• (Distance)-spanners are also energy-spanners
• Spanners for Euclidean space based on
proximity graphs:
– Delaunay triangulation
– The Yao graph

The Yao Graph
• Each node divides
the space into
sectors of angle
• Fixes an edge with
the nearest neighbor
in each sector.
• Sparse: each node fixes
at most edges
• Stretch is at most

The Yao Graph
• Each node divides
the space into
sectors of angle
• Fixes an edge with
the nearest neighbor
in each sector.
• Sparse: each node fixes
at most edges
• Stretch is at most
• Degree could be

Variants of the Yao Graph
• Can derive a constant-degree subgraph by a
phase of edge removal [WLBW 00, LHBW 01]
– Increases stretch by a constant factor
– Need to process edges in a coordinated order
• Locally computable variant of the Yao graph
[LWWF 01, WL 01]
1. Each node divides the space into sectors of angle .
2. Each node computes a neighbor set which consists
of each nearest neighbor in all its sectors.
3. (u,v) is selected if v is in u’s neighbor set and u is
the nearest among those that selected v in its
neighbor set.

Local Postprocessing of Yao Graph
1. Each node divides the space into sectors of angle 

2. Each node computes a neighbor set which
consists of each nearest neighbor in all its sectors.

the nearest among those that selected v into its
nearest neighbor.

Properties of the Topology
• By definition, constant-degree
• For  sufficiently small, the topology
has constant energy stretch for
arbitrary point sets [JRS 03]
– Challenge: Unlike for the Yao graph, the
min-cost path from u to v may traverse
nodes that are farther from u than v
• Does the algorithm yield a distance-
spanner?
– Can establish claim for specialized node
distributions [JRS 03]
– Weak spanner property holds [GLSV 02]

Other Recent Work
• Energy-efficient planar topologies:
– Combination of localized Delaunay
triangulation and Yao structures
– Planar, degree-bounded, and energy-
spanner [WL 03, SWLF 04]

Topology Control and Interference
• Focus thus far on energy-efficiency and
connectivity
• Previous interference models (physical and
protocol models) for individual transmissions
• How to measure the “interference quotient” of
a topology?
– Edge interference number: What is the maximum
number of edges that an edge interferes with?
– Node interference number: What is the maximum
number of nodes that an edge interferes with?

Edge Interference Number
• Defined by [MSVG 02]
• When does an edge
interfere with another
edge?
– The lune of the edge
contains either endpoint
of the other edge

Node Interference Number
• Defined by [BvRWZ
04]
• When does an edge
interfere with another
node?
– The lune of the edge
contains the node

Minimizing NIM
• Goal: Determine
connected topology
that minimizes NIM
• I(e) is independent
of the topology

Minimizing NIM
• Set weight of e to be
I(e)
• Find spanning subgraph
that minimizes
maximum weight
– MST!
• Calculating L(e) possible
using local
communication
• Computing an MST
difficult to do locally
• In general, minimizing
NIM hard to do locally

Sparseness and Interference
Prove that for a random distribution of nodes
on the plane, the Yao graph has an NIM (or
EIM) of O(log n) with high probability

Sparseness and Interference
• Does sparseness necessarily
imply low interference?
• No! [BvRWZ 04]
• Performance of topologies
based on proximity graphs
(e.g., Yao graph) may be bad
1
>1
2 4
>2
>4
Nearest neighbor forest Optimal

Low-Interference Spanners
• Goal: Determine a topology that has distance-
stretch of at most t, and has minimum NIM
among all such topologies [BvRWZ 04]
• Let T, initially empty, be current topology
• Process edges in decreasing order of I(·)
• For current edge e = (u,v):
– Until stretch-t path between u and v in T, repeatedly
add edge with least I(·) to T
• NIM-optimal
• Amenable to a distributed implementation:
– L(e) computable locally
– Existence of stretch-t path can be determined by a
search within a local neighborhood

Minimum Energy Broadcast Routing
• Given a set of nodes
in the plane
• Goal: Broadcast from
a source to all nodes
• In a single step, a
node may broadcast
within a range by
appropriately
adjusting transmit
power

Minimum Energy Broadcast Routing
• Energy consumed by a
broadcast over range
is proportional to
• Problem: Compute the
sequence of broadcast
steps that consume
minimum total energy
• Centralized solutions
• NP-complete [ZBH 02]
r

r

Three Greedy Heuristics
• In each tree, power for each node proportional
to th exponent of distance to farthest child in
tree
• Shortest Paths Tree (SPT)
• Minimum Spanning Tree (MST)
• Broadcasting Incremental Power (BIP)
– “Node” version of Dijkstra’s SPT algorithm
– Maintains an arborescence rooted at source
– In each step, add a node that can be reached with
minimum increment in total cost
• SPT is -approximate, MST and BIP have
approximation ratio of at most 12 [WCLF 00]

Lower Bound on SPT
• Assume
nodes per ring
• Total energy of SPT:
• Optimal solution:
– Broadcast to all nodes
– Cost 1
• Approximation ratio
)
(n

2
/
)
1
( 
n
2
/
)
)
1
(
)(
1
( 


 


n
 

1

Performance of the MST Heuristic
• Weight of an edge equals
• MST for these weights same as
Euclidean MST
– Weight is an increasing function of distance
– Follows from correctness of Prim’s algorithm
• Upper bound on total MST weight
• Lower bound on optimal broadcast tree

Weight of Euclidean MST
• What is the best upper
bound on the weight
of an MST of points
located in a unit disk?
– In [6,12]!
= 6
< 12
• Dependence on
– : in the limit
– : bounded

Structural Properties of MST
≥ 60°
60°
Empty

Upper Bound on Weight of MST
• Assume = 2
• For each edge , its
diamond accounts for an
area of at least
60°
• Total area accounted for is
at most
• MST cost equals
• Claim also applies for

Lower Bound on Optimal
• For a non-leaf node ,
let denote the
distance to farthest
child
• Total cost is
• Replace each star by
an MST of the points
• Cost of resultant
graph at most
MST has cost at most 12 times optimal

Performance of the BIP Heuristic
• Let be the nodes added in order by
BIP
• Let be the complete graph over the same
nodes with the following weights:
– Weight of edge equals incremental power
needed to connect
– Weight of remaining edges same as in original graph
• MST of same as BIP tree

Spanning Trees in Ad Hoc Networks
• Forms a backbone for routing
• Forms the basis for certain network
partitioning techniques
• Subtrees of a spanning tree may be
useful during the construction of local
structures
• Provides a communication framework
for global computation and broadcasts

Arbitrary Spanning Trees
• A designated node starts the “flooding”
process
• When a node receives a message, it
forwards it to its neighbors the first time
• Maintain sequence numbers to
differentiate between different ST
computations
• Nodes can operate asynchronously
• Number of messages is ;worst-case
time, for synchronous control, is
)
(m
O
))
(
Diam
( G
O

Minimum Spanning Trees
• The basic algorithm [GHS 83]
– messages and time
• Improved time and/or message complexity [CT
85, G 86, A 87]
• First sub-linear time algorithm [GKP 93]
• Improved to
• Taxonomy and experimental analysis [FM 96]
• lower bound [RP 00]
)
log
( n
n
m
O  )
log
( n
n
O
)
log
D
( *
61
.
0
n
n
O 
)
log
/
( n
n
D

)
log
( *
n
n
D
O 

The Basic Algorithm
• Distributed implementation of Borouvka’s
algorithm [B 26]
• Each node is initially a fragment
• Fragment repeatedly finds a min-weight
edge leaving it and attempts to merge with
the neighboring fragment, say
– If fragment also chooses the same edge, then
merge
– Otherwise, we have a sequence of fragments, which
together form a fragment
1
F
2
F
2
F

Subtleties in the Basic Algorithm
• All nodes operate asynchronously
• When two fragments are merged, we should
“relabel” the smaller fragment.
• Maintain a level for each fragment and ensure
that fragment with smaller level is relabeled:
– When fragments of same level merge, level
increases; otherwise, level equals larger of the two
levels
• Inefficiency: A large fragment of small level
may merge with many small fragments of
larger levels

Asymptotic Improvements to the
Basic Algorithm
• The fragment level is set to log of the
fragment size [CT 85, G 85]
– Reduces running time to
• Improved by ensuring that computation in
level fragment is blocked for time
– Reduces running time to
)
log
( *
n
n
O
)
(n
O
 )
2
( 
O
Level 1
Level 1
Level 2

A Sublinear Time Distributed
Algorithm
• All previous algorithms perform computation
over fragments of MST, which may have
diameter
• Two phase approach [GKP 93, KP 98]
– Controlled execution of the basic algorithm, stopping
when fragment diameter reaches a certain size
– Execute an edge elimination process that requires
processing at the central node of a BFS tree
• Running time is
• Requires a fair amount of synchronization
)
log
)
(
Diam
( *
n
n
G
O 
)
(n


Open Problems in Topology Control
• Connectivity:
– Energy-optimal bounded-hops topology
– Is the energy-spanner variant of the Yao graph a
spanner?
• Interference number:
– What is the complexity of optimizing the edge
interference number?
• Minimum energy broadcast routing:
– Best upper bound on the cost of an MST in Euclidean
space
– Local algorithms
• Tradeoffs among congestion, dilation, and
energy consumption [MSVG 02]

Capacity of Ad Hoc Networks

The Attenuation Model
• Path loss:
– Ratio of received power to transmitted
power
– Function of medium properties and
propagation distance
• If is received power, is the
transmitted power, and is distance
• where ranges from 2 to 4
)
( 
d
P
O
P T
R 
R
P T
P
d


Interference Models
• In addition to path loss, bit-error rate of
a received transmission depends on:
– Noise power
– Transmission powers and distances of other
transmitters in the receiver’s vicinity
• Two models:
– Physical model
– Protocol model

The Physical Model
• Let denote set of nodes that are
simultaneously transmitting
• Let be the transmission power of node
if:
• is the min signal-interference ratio (SIR)




 
i
k
k
k
i
i
Y
X
d
P
N
Y
X
d
P
)
,
(
)
,
(

}
{ i
X
i
P i
X
i
X Y

The Protocol Model
if for all
• where is a protocol-specified guard-zone to
prevent interference


)
,
(
)
1
(
)
,
( Y
X
d
P
Y
X
d
P
k
k
i
i



i
X Y

k

Measures for Network Capacity
• Throughput capacity:
– Number of successful packets delivered per second
– Dependent on the traffic pattern
– What is the maximum achievable, over all protocols,
for a random node distribution and a random
destination for each source?
• Transport capacity:
– Network transports one bit-meter when one bit has
been transported a distance of one meter
– Number of bit-meters transported per second
– What is the maximum achievable, over all node
locations, and all traffic patterns, and all protocols?

Transport Capacity: Assumptions
• nodes are arbitrarily located in a unit
disk
• We adopt the protocol model
– Each node transmits with same power
– Condition for successful transmission from
to : for any
• Transmissions are in synchronized slots
)
,
(
)
1
(
)
,
( Y
X
d
Y
X
d k
i



i
X
Y k
n

Transport Capacity: Lower Bound
• What configuration and traffic pattern
will yield the highest transport capacity?
• Distribute senders uniformly in the
unit disk
• Place receivers just close enough
to senders so as to satisfy threshold
2
/
n
2
/
n

sender
receiver

• Sender-receiver distance is
• Assuming channel bandwidth W,
transport capacity is
• Thus, transport capacity per node is
)
( n
W

)
/
1
( n

)
(
n
W


Transport Capacity: Upper Bound
• For any slot, we will upper bound the
total bit-meters transported
• For a receiver j, let r_j denote the
distance from its sender
• If channel capacity is W, then bit-
meters transported per second is


j
j
r
W
receiver
)
(

• Consider two successful transmissions
in a slot:
j
k

i

• Balls of radii around , for all , are
disjoint
• So bit-meters transported per slot is
)
1
(
2
O
r
j
j 

)
( j
r
 j j
)
( n
W
O
)
(
)
(
)
( 2
n
O
h
O
r
j
j 


)
( n
O
r
j
j 


Throughput Capacity of Random
Networks
• The throughput capacity of an -node
random network is
• There exist constants and such that
0
]
log
'
Pr[
lim
1
]
log
Pr[
lim






feasible
is
feasible
is
n
n
W
c
n
n
W
c
n
n
)
log
(
n
n
W

'
c
c
n

Implications of Analysis
• Transport capacity:
– Per node transport capacity decreases as
– Maximized when nodes transmit to
neighbors
• Throughput capacity:
– For random networks, decreases as
– Near-optimal when nodes transmit to
neighbors
• Designers should focus on small
networks and/or local communication
n
1
n
nlog
1

Remarks on Capacity Analysis
• Similar claims hold in the physical model as
well
• Results are unchanged even if the channel can
be broken into sub-channels
• More general analysis:
– Power law traffic patterns [L+ 03]
– Hybrid networks [KT 03, LLT 03, T 04]
– Asymmetric scenarios and cluster networks [T 04]

Asymmetric Traffic Scenarios
• Number of destinations smaller than number
of sources
– nd destinations for n sources; 0 < d <= 1
– Each source picks a random destination
• If 0 < d < 1/2, capacity scales as nd
• If 1/2 < d <= 1, capacity scales as n1/2
• [T 04]

Power Law Traffic Pattern
• Probability that a node communicates
with a node units away is
– For large negative , destinations clustered
around sender
– For large positive , destinations clustered
at periphery
• As goes from < -2 to > -1, capacity
scaling goes from to [L+ 03]

 1
)
(



dt
t
x
x
p

)
1
(
O )
/
1
( n
O

x


Relay Nodes
• Offer improved capacity:
– Better spatial reuse
– Relay nodes do not count in
– Expensive: addition of nodes as pure
relays yields less than -fold increase
• Hybrid networks: n wireless nodes and
nd access points connected by a wired
network
– 0 < d < 1/2: No asymptotic benefit
– 1/2 < d <= 1: Capacity scaling by a factor
of nd
n
kn
1

k

Mobility and Capacity
• A set of nodes communicating in random
source-destination pairs
• Expected number of hops is
• Necessary scaling down of capacity
• Suppose no tight delay constraint
• Strategy: packet exchanged when source and
destination are near each other
– Fraction of time two nodes are near one another is
• Refined strategy: Pick random relay node (a la
Valiant) as intermediate destination [GT 02]
• Constant scaling assuming that stationary
distribution of node location is uniform

Open Problems in Capacity Analysis
• Detailed study of impact of mobility
– [GT 02] study is “optimistic”
• Capacity of networks with beam-forming
antennas [R 98]
– Omnidirectional antennas incur a tradeoff between
range and spatial reuse
– A beam-forming antenna can transmit/receive more
energy in preferred transmission and reception
directions
• Capacity of MIMO systems

Algorithms for Sensor Networks

Why are Sensor Networks Special?
• Very tiny nodes
– 4 MHz, 32 KB memory
• More severe power constraints than PDAs,
mobile phones, laptops
• Mobility may be limited, but failure rate higher
• Usually under one administrative control
• A sensor network gathers and processes
specific kinds of data relevant to application
• Potentially large-scale networks comprising of
thousands of tiny sensor nodes

Focus Problems
• Medium-access and power control:
– Power saving techniques integral to most sensor
networks
– Possibility of greater coordination among sensor
nodes to manage channel access
• Synchronization protocols:
– Many MAC and application level protocols rely on
synchronization
• Query and stream processing:
– Sensor network as a database
– Queries issued at certain gateway nodes
– Streams of data being generated at the nodes by
their sensors
– Need effective in-network processing and adequate
networking support

MAC Protocols for Sensor Networks
• Contention-Based:
– Random access protocols
– IEEE 802.11 with power saving methods
• Scheduling-Based:
– Assign transmission schedules (sleep/awake
patterns) to each node
– Variants of TDMA
• Hybrid schemes

Proposed MAC Protocols
• PAMAS [SR 98]:
– Contention-based access
– Powers off nodes that are not receiving or forwarding
packets
– Uses a separate signaling channel
• S-MAC [YHE 03]:
– Contention-based access
• TRAMA [BG 03]:
– Schedule- and contention-based access
• Wave scheduling [D+ 04]:
– Schedule- and contention-based access
• Collision-minimizing CSMA [TJB 04]:
– For bursty event-based traffic patterns

S-MAC
• Identifies sources of energy waste [YHE 03]:
– Collision
– Overhearing
– Overhead due to control traffic
– Idle listening
• Trade off latency and fairness for reducing
energy consumption
• Components of S-MAC:
– A periodic sleep and listen pattern for each node
– Collision and overhearing avoidance

S-MAC: Sleep and Listen Schedules
• Each node has a sleep and listen schedule and
maintains a table of schedules of neighboring
nodes
• Before selecting a schedule, node listens for a
period of time:
– If it hears a schedule broadcast, then it adopts that
schedule and rebroadcasts it after a random delay
– Otherwise, it selects a schedule and broadcasts it
• If a node receives a different schedule after
selecting its schedule, it adopts both
schedules
• Need significant degree of synchronization

S-MAC: Collision and Overhearing
Avoidance
• Collision avoidance:
– Within a listen phase, senders contending to send
messages to same receiver use 802.11
• Overhearing avoidance:
– When a node hears an RTS or CTS packet, then it
goes to sleep
– All neighbors of a sender and the receiver sleep until
the current transmission is over

TRAMA
• Traffic-adaptive medium adaptive protocol
[BG 03]
• Nodes synchronize with one another
– Need tight synchronization
• For each time slot, each node computes an
MD5 hash, that computes its priority
• Each node is aware of its 2-hop neighborhood
• With this information, each node can compute
the slots it has the highest priority within its
2-hop neighborhood

TRAMA: Medium Access
• Alternates between random and scheduled
access
• Random access:
– Nodes transmit by selecting a slot randomly
– Nodes can only join during random access periods
• Scheduled access:
– Each node computes a schedule of slots (and
intended receivers) in which will transmit
– This schedule is broadcast to neighbors
– A free slot can be taken over by a node that needs
extra slots to transmit, based on priority in that slot
– Each node can determine which slots it needs to
stay awake for reception

Wave Scheduling
• Motivation:
– Trade off latency for reduced energy consumption
– Focus on static scenarios
• In S-MAC and TRAMA, nodes exchange local
schedules
• Instead, adopt a global schedule in which data
flows along horizontal and vertical “waves”
• Idea:
– Organize the nodes according to a grid
– Within each cell, run a leader election algorithm to
periodically elect a representative (e.g., GAF)
– Schedule leaders’ wakeup times according to
positions in the grid

Wave Scheduling: A Simple Wave

Wave Scheduling: A Pipelined Wave

Wave Scheduling: Message Delivery
• When an edge is scheduled:
– Both sender and receiver are awake
– Sender sends messages for the duration of the
awake phase
– If sender has no messages to send, it sends an NTS
message (Nothing-To-Send), and both nodes revert
to sleep mode
• Given the global schedule, route selection is
easy
– Depends on optimization measure of interest
– Minimizing total energy consumption requires use of
shortest paths
– Minimizing latency requires a (slightly) more
complex shortest-paths calculation

Collision-Minimizing CSMA
• Focus on bursty event-based traffic [TJB 04]
– Room monitoring: A fire triggers a number of redundant
temperature and smoke sensors
– Power-saving: When a node wakes up and polls, all
coordinators within range may respond
• Goal: To minimize latency
• Scenario:
– N nodes contend for a channel
– There are K transmission slots
– Sufficient for any one of them to transmit successfully
– No collision detection: collisions may be expensive since
data packet transmission times may be large
• Subgoal: To maximize the probability of a
collision-free transmission

Collision-Free Transmission
• Probability of transmission varies over slots
• Probability of successful collision-free
transmission in K slots
• Can calculate probability vector p* that optimizes
above probability
• MAC protocol: CSMA/p*

Synchronization in Sensor Networks

Synchronization in Sensor Networks
• Sensor data fusion
• Localization
• Coordinated actuation
– Multiple sensors in a local area make a
measurement
• At the MAC level:
– Power-saving duty cycling
– TDMA scheduling

Synchronization in Distributed Systems
• Well-studied problem in distributed
computing
• Network Time Protocol (NTP) for
Internet clock synchronization [M94]
• Differences: For sensor networks
– Time synchronization requirements more stringent
(s instead of ms)
– Power limitations constrain resources
– May not have easy access to synchronized global
clocks

Network Time Protocol (NTP)
• Primary servers (S1)
synchronize to
national time
standards
– Satellite, radio,
modem
• Secondary servers
(S2, …) synchronize
to primary servers
and other secondary
servers
– Hierarchical subnet
S3 S3 S3
S4
S2 S2 S2 S2
S3 S3
S1 S1 S1 S1
S2
S1 S1
S2 S2
Primary
Secondary
http://www.ntp.org

Measures of Interest
• Stability: How well a clock can maintain
its frequency
• Accuracy: How well it compares with
some standard
• Precision: How precisely can time be
indicated
• Relative measures:
– Offset: Difference between times of two
clocks
– Skew: Difference between frequencies of
two clocks

Synchronization Between Two Nodes
• A sends a message to B; B sends an ack back
• A calculates clock drift and synchronizes
accordingly
A
B

Error Analysis
A
B

Sources of Synchronization Error
• Non-determinism of processing times
• Send time:
– Time spent by the sender to construct packet;
application to MAC
• Access time:
– Time taken for the transmitter to acquire the channel
and exchange any preamble (RTS/CTS): MAC
• Transmission time: MAC to physical
• Propagation time: physical
• Reception time: Physical to MAC
• Receive time:
– Time spent by the receiver to reconstruct the packet;
MAC to application

Sources of Synchronization Error
• Sender time = send time + access time +
transmission time
– Send time variable due to software delays at the
application layer
– Access time variable due to unpredictable contention
• Receiver time = receive time + reception time
– Reception time variable due to software delays at the
application layer
• Propagation time dependent on sender-
receiver distance
– Absolute value is negligible when compared to other
sources of packet delay
– If node locations are known, these times can be
explicitly accounted for

Two Approaches to Synchronization
• Sender-receiver:
– Classical method, initiated by the sender
– Sender synchronizes to the receiver
– Used in NTP
– Timing-sync Protocol for Sensor Networks (TPSN)
[GKS 03]
• Receiver-based:
– Takes advantage of broadcast facility
– Two receivers synchronize with each other based on
the reception times of a reference broadcast
– Reference Broadcast Synchronization (RBS) [EGE 02]

TPSN
• Time stamping done at
the MAC layer
– Eliminates send, access, and
receive time errors
• Creates a hierarchical
topology
• Level discovery:
– Each node assigned a level
through a broadcast
• Synchronization:
– Level i node synchronizes to
a neighboring level i-1 node
using the sender-receiver
procedure
3
3
3
2
2
2
1
1
1
0

Reference Broadcast Synchronization
• Motivation:
– Receiver time errors are significantly smaller than
sender time errors
– Propagation time errors are negligible
– The wireless sensor world allows for broadcast
capabilities
• Main idea:
– A reference source broadcasts to multiple receivers
(the nodes that want to synchronize with one
another)
– Eliminates sender time and access time errors

• Simple form of RBS:
– A source broadcasts a
reference packet to all
receivers
– Each receiver records
the time when the
packet is received
– The receivers exchange
their observations
• General form:
– Several executions of
the simple form
• For each
receiver , receiver
derives an estimate of

• Clock skew:
– Averaging assumes equals 1
– Find the best fit line using least
squares linear regression
– Determines and
• Pairwise synchronization in
multihop networks:
– Connect two nodes if they were
synchronized by same reference
– Can add drifts along path
– But which path to choose?
– Assign weight equal to root-
mean square in regression
– Select path of min-weight

Pairwise and Global Synchronization
• Global consistency:
– Converting times from i to j and then j to k
should be same as converting times from i to k
• Optimal precision:
– Find an unbiased estimate for each pair
with minimum variance
• [KEES 03]

Consistency and Optimal Precision
• Min-variance pairwise
synchronizations are globally
consistent!
• Maximally likely set of offset
assignments yield minimum variance
synchronizations!
• Flow in resistor networks
– Bipartite graph connecting the
receivers with the sources
– Resistance of each edge equal to
the variance of the error
corresponding to that source-
receiver pair
– Min-variance is effective resistance
– Estimator can be obtained from the
current flows
1
1

Algorithmic Support for Query
Processing in Sensor Networks

The Sensor Network as a Database
• From the point of view of the user, the sensor
network generates data of interest to the user
• Need to provide the abstraction of a database
– High-level interfaces for users to collect and process
continuous data streams
• TinyDB [MFHH 03], Cougar [YG 03]
– Users specify queries in a declarative language (SQL-
like) through a small number of gateways
– Query flooded to the network nodes
– Responses from nodes sent to the gateway through a
routing tree, to allow in-network processing
– Especially targeted for aggregation queries
• Directed diffusion [IGE 00]
– Data-centric routing: Queries routed to specific
nodes based on nature of data requested

Classification of Queries
• Long-running vs ad hoc
– Long-running: Issued once and require periodic
updates
– Ad hoc: Require one-time response
• Temporal:
– Historical
– Present
– Future: e.g., trigger queries
• Nature of query operators
– Aggregation vs. general
• Spatial vs. non-spatial

Processing of Aggregate Queries
• Aggregation query q:S
– Sum, minimum, median, etc.
• Queries flooded within the
network
• An aggregation tree is
obtained
• Query results propagated
and aggregated up the tree
• Aggregation tree selection
• Multi-query optimization

Multi-Query Optimization
• Given:
– An aggregation tree
– Query workload
– Update probabilities of sensors
• Determine an aggregation procedure that
minimizes communication complexity:
• Push vs. pull:
– When should we proactively send up sensor data?
• Problem space [DGRTY 03]:
– Deterministic queries, deterministic updates
– Deterministic queries, probabilistic updates
– Probabilistic queries, deterministic updates
– Probabilistic queries, probabilistic updates

• Two queries: A+B and A+C,
each with probability 1-
• =0: Proactively forward
each sensor reading up the
tree
•  nearly 1: Let parent pull
information
• Intermediate case depends
on the ratio of result/query
message sizes
A
I
B C
R
2r
q+2(1-)r
q+(1-)r
q+(1-2)r
r
r

• q > 2r:
– Push on every edge
• r < q <2r:
– Pull on (I,R)
– Push on other edges
• 2r < q < r:
– Push on (A,I)
– Pull on other edges
• q < 2r:
– Pull on every edge
• Optimizations:
– Send results of a basis of the projected query set
along an edge
A
I
B C
R
2r
q+2(1-)r
q+(1-)r
q+(1-2)r
r
r

Aggregation Tree Selection
• Given:
– An aggregation procedure for a fixed aggregation
tree
– Query workload: e.g., probability for each query
– Probability of each sensor update
• Determine an aggregation tree that minimizes
the total energy consumption
• Clearly NP-hard
– Minimum Steiner tree problem is a special case
• Approximation algorithms for interesting
special cases

Approximations for Special Cases
• Individual queries:
– Any approximation to minimum Steiner tree suffices
– MST yields 2-approximation, improved approximations
known
• Universal trees [JLNRS 04]:
– There exists a single tree whose subtree induced by any
query is within polylog(n) factor of the optimum
– Unknown query, deterministic update
• A single aggregation tree for all concave
aggregation functions [GE 03]
– All sensor nodes participate
– The aggregation operator is not known a priori, but
satisfies a natural concaveness property
– There exists a single tree that achieves an O(log n)-
approximation

Simultaneous Optimization for
Concave Aggregation Functions
• A function that gives the size of the
aggregated data given the number of items
being aggregated
• Binary aggregation method:
– Find a min-cost matching
– For each pair, select one node at random and make it
the parent of the other
– Repeat the procedure with the parents until have
exactly one node

Simultaneous Optimization for
Concave Aggregation Functions
• Independent of the function f
• Binary aggregation method yields an O(log n)
approximation for any function
– n is the number of nodes
• Can be derandomized to yield the same
asymptotic result

Data-Centric Storage and Routing
• Need to ensure the query originator rendezvous
with nodes containing matching data
– Flooding queries is expensive
• Data-centric storage [R+ 02, G+ 03]:
– Designated collection of nodes storing data items
matching a certain predicate
– These nodes can also perform in-network processing to
compute intermediate values
• Data-centric routing [R+ 02, G+ 03]:
– Gateway determines node(s) storing data matching a
particular predicate
– Routes query to these nodes using unicast or multicast

Open Problems in Sensor Network
Algorithms
• Topology control:
– Aggregation tree selection
– Scheduling node and edge activations for specific
communication patterns
• Multi-query optimization:
– Need to address general (non-aggregate) queries
– Related to work in distributed databases; energy
consumption a different performance measure

Outline
Application
Routing
Medium Access
Control
Power
Control
Sensor Network
Protocols
Topology
Control
1
3
2
5
Fundamental limits of ad hoc networks
4

AdHocTutorial.ppt

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