chapter 5 topology control 2015/10/261. outline 5.1. motivations and goals 5.2. power control and...
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Chapter 5Topology Control
112/04/201
Outline
5.1. Motivations and Goals
5.2. Power Control and Energy Conservation
5.3. Tree Topology
5.4. k-hop Connected Dominating Set
5.5. Adaptive node activity
5.6. Conclusions
112/04/202
Outline
5.1. Motivations and Goals
5.2. Power Control and Energy Conservation
5.3. Tree Topology
5.4. k-hop Connected Dominating Set
5.5. Adaptive node activity
5.6. Conclusions
112/04/203
Motivations
A typical characteristic of wireless sensor networks deploying many nodes in a small area
ensure sufficient coverage of an area, or protect against node failures
Networks can be too dense: too many nodes in close (radio) vicinity
112/04/204
Motivations In a very dense networks, too many nodes
Too many collisions Too complex operation for a MAC protocol Too many paths to be chosen from for a routing protocol, …
112/04/205
Goals This chapter looks at methods to deal with such
networks by Reducing/controlling transmission power Deciding which links to use Turning some nodes off
112/04/206
Topology Control Topology control: Make topology less complex
Topology: Which node is able/allowed to communicate with which
other nodes Topology control needs to maintain invariants, e.g.,
connectivity
112/04/207
Options for topology control
Topology controlTopology control
Flat network
All nodes have essentially same role
Hierarchical network
Assign different roles to nodes and then control node/link activity
Dominating sets
Tree
Adaptive node activity
HybridPower control Clustering
112/04/208
Outline
5.1. Motivation and Goals
5.2. Power Control and Energy Conservation
5.3. Tree Topology
5.4. k-hop Connected Dominating Set
5.5. Adaptive node activity
5.6. Conclusions
112/04/209
Introduction of Power Control Power control
The transmitter’s power can be adjusted dynamically over a wide range Typical radio adjusts their transmitter’s power based on received signal strength
Controls the transmission power Topology control for desired connectivity Compensate topology changes incurred
by mobility and dead nodes
A
BC
D
A
Connected
Disconnected
112/04/2010
Interactions
Introduction of Power Control
Battery drain
Power control
Large Battery makes Longer Lifetime
112/04/2011
Interactions
Introduction of Power Control
A
B
C
DDestination
Source
Battery drain
Power control
Large Power makes Performance Degradation
Interference
Large Battery makes Longer Lifetime
112/04/2012
Interactions
Introduction of Power Control
CB
D
ASource
Destination
A
B
C
Source
Battery drain
Power control
Large Battery makes Longer Lifetime
Large Power makes Performance Degradation
Different Power makes Load Unbalancing
Interference
A consumes much more power than C
Adjusting power can balance the power consumption
DDestination
112/04/2013
Interactions
Introduction of Power Control
CB
D
A
Source
B
C A
B
C
E
D
Battery drain
Power control
Large Power makes Performance Degradation
Different Power makes Load Unbalancing
Destination
Small Power creates more Spatial Reuse Opportunities
Large Battery makes Longer Lifetime
Interference
A consumes much more power than C
A
D
Destination
C is forbid to communication with B
Adjusting the power of A can improve the spatial reuse
Source
112/04/2014
Interactions
Introduction of Power Control
CB
D
ASource
B
C
Battery drain
Error performance
Error rate
dB
Power control
Large Power makes Performance Degradation
Different Power makes
Load Unbalancing
Destination
Small Power creates more
Spatial Reuse Opportunities
Small Power causes More Retransmissions
A
B
C
E
D
Large Battery makes
Longer Lifetime
Interference
A consumes much more power than C
AD
Destination
Adjusting the power of A can improve the spatial reuse
Large power, small error rate
Source
112/04/2015
Introduction of Power Control Targets and Issues
Improve network throughput Improve transmission range Improve fairness Improve connectivity Power control helps in scheduling Reduce the interference and energy consumption Partial combination of above targets etc.
112/04/2016
Power Control and Energy Conservation
IEEE INFOCOM 2000IEEE INFOCOM 2000
R. Ramanathan and R. Rosales-HainR. Ramanathan and R. Rosales-Hain
Topology Control of Multihop Wireless Networks using Transmit Power Adjustment
Topology Control of Multihop Wireless Networks using Transmit Power Adjustment
112/04/2017
Introduction Topology
The set of communication links between node pairs used by routing mechanism Uncontrollable factor: mobility, weather, interference,
noise Controllable factor: transmission power, antenna direction
112/04/2018
Introduction A graph is called connected if every
pair of distinct vertices in the graph can be connected through some path
A bi-connected graph is a connected graph that is not broken into disconnected pieces by deleting any single vertex (and its incident edges)
Connected
Bi-connected
112/04/2019
Motivation Drawbacks of wrong topology
Reduce network capacity Increase interference Increase end-to-end packet delay
Sparse network A danger of network partitioning High end to end delays
Dense network Many nodes interfere with each other
112/04/2020
Static Networks: Min-Max Power Algorithm Goal
Find a per-node minimal assignment of transmitted power p
such that (1) the induced graph is connected and (2) max p is minimum
112/04/2021
Min-Max Power Algorithm- Connected Networks Phase I: CONNECTION
Construct a Minimum cost spanning tree
Successful transmit power between i and j
A B
C D
E F
2
2
1 1
3 3( ( , ))ij i jp d l l s
s : the receiver sensitivity
: path loss between i and j ( ( , ))i jd l l1
1
1
122
3
3 3
34
il : the location of node i
112/04/2022
Min-Max Power Algorithm- Connected Networks Phase II : Per Node Minimizing Power
A B
C D
E F
2
2
1 1
3 3
1
1
1
122
3
3 3
3
side-effect-edge :The edge of (C, D) is automatically connected
1 1
4
A has a path to B via C with smaller power→A adjusts the transmitted power from 2 to 1.
B has a path to A via D with smaller power→B adjusts the transmitted power from 2 to 1.
The edge (A, B) can be disconnected to save more energy
112/04/2023
Min-Max Power Algorithm- Bi-Connectivity Augmentation Phase I: BICONN-AUGMENT
Construct a Connected Minimum cost spanning tree
A B
C D
E F
2
2
1 1
3 3
1
1
1
122
43
3 3
3 Successful transmit power between i and j
( ( , ))ij i jp d l l s
s : the receiver sensitivity
: path loss between i and j ( ( , ))i jd l l
il : the location of node i
112/04/2024
Min-Max Power Algorithm- Bi-Connectivity Augmentation Phase I: BICONN-AUGMENT
Add (u, v) to graph G until the network is bi-connected
=> Add (C, D)
C
E
A
C
Bi-Conn. Comp. of C Bi-Conn. Comp. of DA B
C D
E F
2
2
1 1
3 3
1
1
1
122
3
3 3
34
D
F
B
D
Bi-Connected component of C
Bi-Connected component of D
112/04/2025
Min-Max Power Algorithm- Bi-Connectivity Augmentation Phase I: BICONN-AUGMENT
Add (u, v) to graph G until the network is bi-connected
A B
C D
E F
2
2
1 1
3 3
1
1
1
122
3
3 3
34
C
E
D
F
Bi-Connected component of E
Bi-Connected component of F
=> Add (E, F)
Bi-Conn. Comp. of E Bi-Conn. Comp. of F
112/04/2026
Min-Max Power Algorithm- Bi-Connectivity Augmentation
Phase II: Per Node Minimizing Power No side-effect-edge →Finish
A B
C D
E F
2
2
1 1
3 3
1
1
1
122
4
3 3
4
4
112/04/2027
Min-Max Power Algorithm- Bi-Connectivity Augmentation Phase II: Per Node Minimizing Power
An other example has side-effect-edge
1
2
2
3 3
3A
B
C D
1 1
2
2
3 3
3 3
side-effect-edge :
The edge of (A, D) is automatically connected
Disconnect the edge (A, C) and still Bi-Connectivity→C adjusts the transmitted power from 3 to 2 112/04/2028
Min-Max Power Algorithm- Bi-Connectivity Augmentation Phase II: Per Node Minimizing Power
An other example has side-effect-edge
1
2
2
3 3
3
A B
C D
1 1
2
2
3
3 32 Disconnect the edge (B, D) and still Bi-Connectivity→B adjusts the transmitted power from 3 to 2
112/04/2029
Min-Max Power Algorithm- Bi-Connectivity Augmentation Phase II: Per Node Minimizing Power
Finish
1
2
2
3 3
3
A B
C D
1 1
2
2
3
3 32
112/04/2030
Outline
5.1. Motivation and Goals
5.2. Power Control and Energy Conservation
5.3. Tree Topology
5.4. k-hop Connected Dominating Set
5.5. Adaptive node activity
5.6. Conclusions
112/04/2031
Introduction of Tree Topology Control Example:
MPR (Multi-Point Relay) election
Retransmission node
(a)
Retransmission node
(b)(b) is better than (a) 112/04/2032
Introduction of Tree Topology Control
g h
a
b c
d e f
g h
a
b c
d e f
Example:
a to d needs 2 hops
(a) (b)
(a) is better than (b)a to d needs 7 hops
112/04/2033
Tree Topology
IEEE INFOCOM 2003IEEE INFOCOM 2003
N. Li, J. C. Hou, and L. ShaN. Li, J. C. Hou, and L. Sha
Design and Analysis of an MST-Based Topology Control Algorithm
Design and Analysis of an MST-Based Topology Control Algorithm
112/04/2034
Motivation
The advantage of Topology Control Minimize the overhearing and then optimize the network spatial reuse Maintain a connected topology by minimal power Power-efficient
(1) No Topology Control
A
B
C
D
E
F
GH
I
(2) With Topology Control
A
B
C
D
E
F
GH
I
112/04/2035
Goal Determine the transmission power of each node
Maintain network connectivity Minimal power consumption
112/04/2036
Local Minimum Spanning Tree Algorithm (LMST) Local Minimum Spanning Tree Algorithm (LMST)
Step 1: Information Collection Step2: Topology Construction Step3: Determination of Transmission Power
112/04/2037
Information Exchange Each node broadcasts periodically a Hello message using its
maximal transmission power. The Hello message includes the ID and Location of the node.
LMST – Step1: Information Collection
u‘’s ID and Location
c
a
u
b
d
Maximal Transmission Power
112/04/2038
Information Exchange Since Hello message includes the node’s ID and Location,
after obtaining the Hello message of 1-hop neighbors, node u can construct the local view.
LMST – Step1: Information Collection
c
a
u
b
d
112/04/2039
LMST – Step2: Topology Construction The weight of edge between the two nodes is based on
Euclidean distance.
The weight of an edge also denotes the transmission power (or distance) between the two nodes
2, rdc r c : Coefficientd : distance
c
a
u
b
d
e
3
4
5
5 6
7
7
7
10
6
112/04/2040
c
a
u
b
d
e
LMST – Step 2: Topology Construction Each node applies Prim’s algorithm independently to obtain its
Local Minimum Spanning Tree.
3
4
5
5 6
7
7
7
10
6
local view of node u
Node u constructs the Local Minimum Spanning Tree using Prim’s algorithm according to its local view
According to the constructed Local Minimum Spanning Tree, node u will use small power to communicate with node a via node b instead of using large power to communicate with node a directly.
According to the constructed Local Minimum Spanning Tree, node u will use small power to communicate with node a via node b instead of using large power to communicate with node a directly.
Small power:Creates more spatial reuse opportunityDecreases energy consumption
112/04/2041
LMST – Step 3: Determination of Transmission Power By measuring the receiving power of Hello message, each node can
determine the specific power levels it needs to reach each of its neighbors. Two commonly-used propagation models
Free Space
Two-Ray
Sign Meaning
Pt Transmit power
Pr Receive power
Gt Antenna gain of the transmitter
Gr Antenna gain of the receiver
Wave length
d Distance between nodes
L System loss
ht Antenna height of the transmitter
hr Antenna height of the receiver
Ld
GGPP rtt
r 2
2
)4(
Ld
hhGGPP rtrtt
r 2
22
112/04/2042
In general, the relation between Pr and Pt is of the following form
Where G is a function of
Example Pth is the required power threshold to successfully receive the message
Pmax is the maximal transmission power
LMST – Step 3: Determination of Transmission Power
GPP tr Ld
GG rt2
2
)4(
max/ PPG r
rthth PPPGP // max
Node b will compute:
c
a
u
b
d
e
HelloData
Node b transmits data to u:
Hello with Pmax
Data with PthG
112/04/2043
Conclusions Advantages
Maintain network connectivity by low energy consumption Reduce the probability of interference Increase the spatial reuse Achieve high throughput
112/04/2044
Tree Topology
IEEE INFOCOM 2000IEEE INFOCOM 2000
J. Wieselthier, G. Nguyen, and A. EphremidesJ. Wieselthier, G. Nguyen, and A. Ephremides
On the Construction of Energy-Efficient Broadcast and Multicast Trees in Wireless
Networks
On the Construction of Energy-Efficient Broadcast and Multicast Trees in Wireless
Networks
112/04/2045
Introduction The paper studies the problems of broadcasting and
multicasting in wireless networks.
To form a minimum-energy tree Energy efficiency Maintain network connectivity
112/04/2046
Network Assumptions The power level of a transmission can be chosen
within a given range of values.
The availability of a large number of bandwidth resources.
Sufficient transceiver resources are available at each of the nodes in the network.
112/04/2047
Wireless Communications Model Node-based transmission cost evaluation
Pi,(j,k) = max{Pij, Pik},
Pij : Transmission power for node i to transmit packets to node j
i
j
k
Pij
Pik
Pik > Pij
The larger power (Pik ) can cover both of node j and node k
The smaller power (Pij ) can only cover node j
112/04/2048
The Broadcast Incremental Power Algorithm
Assume node a is the source node
Step 1: Determining the node that the Source can reach with minimum expenditure of power.
0
1
2
3
4
5
0 1 2 3 4 5
i
c
h
g
f
d
e
b a
0.30.5
0.3
0.5
a
a c
b
j
10.9
1.1
1.21.5
1.71.3
0.7 0.81.3
112/04/2049
The Broadcast Incremental Power Algorithm
0
1
2
3
4
5
0 1 2 3 4 5
i
c
h
g
f
d
e
b a
0.31 0.5 ΔPa = 0.5 – 0.3 = 0.2
ΔPb= 1 – 0 = 1
j
10.9
1.1
1.21.5
1.71.3
0.7 0.81.3
0.5a cPac
Pa0.3a b
ΔPa
1b dPbd
Pbb
ΔPb
Minimum additional cost
Step 2: Determine which “new” node can be added to the tree at minimum additional cost.
112/04/2050
The Broadcast Incremental Power Algorithm Step 2: Determine which “new”
node can be added to the tree at minimum additional cost.
0
1
2
3
4
5
0 1 2 3 4 5
ij
c
h
g
f
d
e
b a
0.31 0.5
0.71.3 0.8
0.9
1.1
1.21.5
1.71.3
ΔPa = 1.3 – 0.5 = 0.8
1.3a jPaj
Pa0.5a c
ΔPa
ΔPc = 0.7 – 0 = 0.7
0.7c jPcj
Pcc
ΔPc
ΔPb = 1 – 0 = 1
1b dPbd
Pbb
ΔPb
Minimum additional cost
112/04/2051
The Broadcast Incremental Power Algorithm
Step 2: Determine which “new” node can be added to the tree at minimum additional cost.
0
1
2
3
4
5
0 1 2 3 4 5
ij
c
h
g
f
d
e
b a
0.31 0.5
0.71.3 0.8
0.9
1.1
1.21.5
1.71.3
And so forth:c → i
c → h
b → d
b → e
b → f
b → g
112/04/2052
The Broadcast Incremental Power Algorithm BIP is similar in principle to Prim’s algorithm.
One fundamental difference: The inputs to Prim’s algorithm are the link cost Pij. BIP must dynamically update the costs at each step.
112/04/2053
Conclusions Propose a centralized algorithm: The Broadcast
Incremental Power(BIP) Algorithm Advantages
Improved performance can be obtained when exploiting the properties of the wireless medium
Energy-efficient
112/04/2054
Outline
5.1. Motivation and Goals
5.2. Power Control and Energy Conservation
5.3. Tree Topology
5.4. k-hop Connected Dominating Set
5.5. Adaptive node activity
5.6. Conclusions
112/04/2055
Connected Dominating Set Connected dominating set (CDS) - construct a virtual backbone. Communicate through the virtual backbone by dominators. Example: virtual backbone construction
Sensor node
112/04/2056
Connected Dominating Set Connected dominating set (CDS) - construct a virtual backbone. Communicate through the virtual backbone by dominators. Example: virtual backbone construction
CDS edgeVirtual backbone
Sensor node
Dominators
1-hop Connected Dominating Set 112/04/2057
Connected Dominating Set Connected dominating set (CDS) - construct a virtual backbone. Communicate through the virtual backbone by dominators. Example: virtual backbone construction
CDS edgeVirtual backbone
Sensor node
Dominators
1-hop Connected Dominating Set
2-hop Connected Dominating Set 112/04/2058
A Hardness Result The MDS (minimum dominating set) problem is NP-hard, it is
even a hard problem to approximate in general.
For the case of unit disk graphs, it is possible to find a Polynomial Time Approximation Scheme (PTAS).
112/04/2059
k-hop Connected Dominating Set
Journal of Communications and Networks 2002Journal of Communications and Networks 2002
Jie Wu, Fei Dai, Ming Gao, and Ivan StojmenovicJie Wu, Fei Dai, Ming Gao, and Ivan Stojmenovic
On Calculating Power-Aware Connected Dominating Sets for Efficient Routing in Ad Hoc
Wireless Networks
On Calculating Power-Aware Connected Dominating Sets for Efficient Routing in Ad Hoc
Wireless Networks
112/04/2060
Introduction Routing based on a connected dominating set is a promising-
approach Each gateway host keeps following information: gateway
domain membership list and gateway routing table.
Gateway host
Non-Gateway host
1
2
56
74
9
3
8 10
11
dominated set
destination member list next hop distance
9 (1,2,3,11) 9 1
4 (5,6) 7 2
7 (6) 7 1
Gateway routing table of host 8
3
10
11
Gateway domain member list of host 8
Receiver
Sender
112/04/2061
Introduction In order to prolong the life span of each node, power
consumption should be minimized and balanced among nodes. Unfortunately, nodes in the dominating set consume more
energy than nodes outside the set.
Propose a method of calculating power-aware connected dominating set based on a dynamic selection process.
Dominated set
Gateway host
Non-Gateway host
1
2
56
74
9
3
8 10
11
12
112/04/2062
Every v exchanges its neighbor set N(v) with all its neighbors. Each node has two-hop neighbors information.
Every v is marked if there exist two unconnected neighbors
Network Initialization
25
Non-Gateway host
Gateway host
1
2
3
5
6
7
8
910
4
11
12 1314
15
16
17
19
20
18
21 2227
262423
unconnected
Become a Gateway host
112/04/2063
Gateways Selection
Gateways Selection (Rules 1 and 2)Gateways Selection (Rules 1 and 2)
112/04/2064
Gateways Selection (by applying Rule 1) Rule 1: Consider two vertices v and u in G’. If N[u] N[v]in G and id( u )
< id(v), the marker v is unmarked, i.e., G' is changed to G' - {u}.
id N(id)
21 22, 23, 24
22 20, 21, 23, 24, 25, 26, 27
20
2122
27
2524
23
26
N(21) N(22)N(21) N(22)
Non-Gateway host
Gateway host
112/04/2065
Rule 2: Assume that u and w are two marked neighbors of marked vertex u in G’. If N(u) N(v) N(w) in G and id(u) = min{id(v),id(u),id( w)},then the marker of u is unmarked.
Gateways Selection (by applying Rule 2)
id N(id)
2 1, 3, 4, 5, 6, 7, 8, 9
4 1, 2, 3, 9, 10, 11
9 2, 4, 5, 6, 7, 8, 10
112/04/2066
1
2
3
5
6
7
8
910
4
11
N(2) N(4) N(9)and id(2) = min{id(2), id(4), id(9)}N(2) N(4) N(9)and id(2) = min{id(2), id(4), id(9)}
Non-Gateway host
Gateway host
Extended Rules Several extended approaches for selective removal
The node-degree-based approach aims at reducing the size of the connected dominating set
The energy-level-based approach tries to prolong the average life span of each node.
112/04/2067
Node-degree-based Approach (Rule 3) Rule 3: Consider two marked vertices v and u in G’. The marker v is
unmarked if one of the following conditions holds: N[u] N[v] in G and nd(u) < nd(v) N[u] N[v] in G and id(u) < id(v) when nd(u) = nd(v), where nd()
returns node degree.
id nd(id) N(id)
21 3 22,23,24
22 7 20,21,23,24,25,26,27
27 3 22,25,26 20
21 2227
252423
26
N(21) N(22) andnd(21)=3 < nd(22)=6N(21) N(22) andnd(21)=3 < nd(22)=6
N(27) N(22)nd(27)=3 < nd(22)=6N(27) N(22)nd(27)=3 < nd(22)=6
112/04/2068
Non-Gateway host
Gateway host
Node-degree-based Approach (Rule 4)
4
11
1213
15
16
17
19
20
18
22
Rule 4: Assume that u and w are two marked neighbors of marked vertex v in G . The marker v is unmarked if one of the following conditions holds:
Case 1. N(u) N(v) N(w), but N(v) N(u) N(w) and N(w) N(u) N(v) in G.
id N(id)
11 4,12,13,15,16,17,18,20
18 11,17,19,20
20 11,18,19,22
N(18) N(11) N(20)butN(11) N(18) N(20)N(20) N(11) N(18)
N(18) N(11) N(20)butN(11) N(18) N(20)N(20) N(11) N(18)
112/04/2069Non-Gateway host
Gateway host
Node-degree-based Approach (Rule 4)
1
2
3
5
6
7
8
910
4
11
Rule 4: Case 2. N(u) N(v) N(w) and N(v) N(u) N(w), but N(w) N(u)
N(v) in G; and one of the following conditions holds: (a) nd(u) < nd(v)(b) nd(u) = nd(v) and id(u) < id(v)
id nd(id) N(id)
2 8 1, 3, 4, 5, 6, 7, 8, 9
4 6 1, 2, 3, 9, 10, 11
9 7 2, 4, 5, 6, 7, 8, 10
nd(9)=7 < nd(2)=8nd(9)=7 < nd(2)=8
N(2) N(4) N(9)N(9) N(2) N(4)
butN(4) N(2) N(9)
N(2) N(4) N(9)N(9) N(2) N(4)
butN(4) N(2) N(9)
112/04/2070
Non-Gateway host
Gateway host
Node-degree-based Approach (Rule 4)
4
11
12 13 14
15
16
17
20
18
Rule 4: Case 3. N(u) N(v) N(w), N(v) N(u) N(w) and N(w) N(u)
N(v) in G; marker u should be unmarked if one of the following conditions holds: (a) nd(u) < nd(v) and nd(u) < nd(w) (b) nd(u) = nd(v) < nd(w) and id(u) < id(v)(c) nd(u) = nd(v) = nd(w) and id(u) = min{id(v), id(u), id(w)}
id nd(id) N(id)
11 8 4,12,13,15,16,17,18,20
13 4 11,12,14,15
15 4 11,13,14,16
id(13) < id(15)id(13) < id(15)
nd(13) = nd(15) = 4nd(13) = nd(15) = 4
N(13) N(11) N(15)N(15) N(11) N(13)
butN(11) N(13) N(15)
N(13) N(11) N(15)N(15) N(11) N(13)
butN(11) N(13) N(15)
112/04/2071
Non-Gateway host
Gateway host
Energy-level-based Approach (Rules 5 、 6、 7 、 8)
Energy-level-based rules Let EL denote energy level Rules 5, 6
Similar to rules 1 and 2, the only difference is to compare EL prior to node ID.
Rules 7, 8 Similar to rules 3 and 4 The only difference: when nodes u and v have the same
EL, they compare ND prior to node ID.
112/04/2072
Conclusions Advantages
Overall energy consumption is balanced A relatively small connected dominating set is generated
112/04/2073
Outline
5.1. Motivation and Goals
5.2. Power Control and Energy Conservation
5.3. Tree Topology
5.4. k-hop Connected Dominating Set
5.5. Adaptive node activity
5.6. Conclusions
112/04/2074
What’s Adaptive Node Activity?
Influence the topology of a graph by Selecting certain nodes to be turned on or Selecting certain nodes to be turned off
An operation that of course also fits well into the context of clustering or backbone mechanisms.
Nodes that are sources or sinks of data are always kept active
112/04/2075
Adaptive node activity
ACM/IEEE MobiCom 2001ACM/IEEE MobiCom 2001
Y. Xu, J. Heidemann, and D. EstrinY. Xu, J. Heidemann, and D. Estrin
Geography-Informed Energy Conservation for Ad Hoc Routing
Geography-Informed Energy Conservation for Ad Hoc Routing
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Introduction Motivation
Nodes consume high energy during routing, especially during transmission
Reduce the energy consumption in ad hoc wireless networks Increase the network lifetime
Goal Identifies equivalent nodes for routing
Based on location information Turns off unnecessary nodes Load balancing energy usage
Lifetime of all nodes remain as long as possible
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Geographical Adaptive Fidelity(GAF) Routing Distribute routing duties by electing new local leaders
periodically. Leaders (active nodes) handle all routing traffic,
allowing other nodes to sleep for extended periods of time and conserve energy.
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Determining Node Equivalence The physical space is divided into equal size squares.
Based on radio communication range Any two nodes in adjacent squares can communicate with each other. In each grid, one node will stay in active state.
5
Rr
r : the length of each gridR : communication range of sensor node
r r r
r
r
r
Rr
2r
Active node
Sleeping node
Source
Destination
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GAF State Transitions GAF consists of three states
Discovery: Due to mobility, node in this state aims to discover all nodes in the same grid
Active: In each grid, one node will stay in active state Sleeping: In a grid, all nodes except the active node will stay in sleeping state
After TdAfter Td
Dis
cove
ry m
sg
Dis
cove
ry m
sg
Afte
r Ts
Afte
r Ts D
iscovery msg
Discovery m
sg
After TaAfter Ta
Sleeping
Discovery Active
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GAF State Transitions
75%92%
23%
54%
: sleeping state : discovery state : active state
Initially nodes start in the Discovery state
Node turns on its radio and find the other nodes within the same grid.
The node finish the discovery duration Td, broadcasts its discovery message (node id, grid id, estimated node active time, and node state) and enters Active state. Td = random [0 ~ constant]
The other node switches its state into Sleeping state after receive the discovery message sentby the node which has higher rank value then itself.
a
b
c
d
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Given any two node i and jRanki > Rankj , if and only if (enati > enatj)
enat = estimated node active time duration
(enlt = expected node lifetime)
Node Ranking Rule
, when enlt becomes less than a threshold
, when enlt larger than a threshold
If node’s lifetime is less than a threshold, stay active state until energy exhaustion.
If node’s lifetime is larger than a threshold, balancing the remain energy to avoid frequent switches between active/sleep states.
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GAF State Transitions
75%92%
23%
54%
: sleeping state : discovery state : active state
70%
A node in the Sleeping state wakes up after an application-dependent sleep time Ts, and switches its state into Discovery state. Avoiding the active node
leaving the grid and energy unbalance.
Ts = random [enat/2 ~ enat]
Larger remain energy, higher rank
Energy drain
Switches to Discovery state after Ts
a
b
c
d
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The active node periodically rebroadcasts its discovery message
The active node leave active state: After the time duration Ta = enat. Receiving discovery message send
by the other node which has higher rank value than itself.
70%
GAF State Transitions
75%
23%
54%
: sleeping state : discovery state : active state
Larger remain energy, higher rankBroadcasts its discovery messageBecome the active node
Receiving discovery messageSwitches to Discovery state
a
b
c
d
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Conclusions GAF increases the network lifetime without decreases
the performance substantially
Distribute routing duties by electing new local leaders periodically
All nodes remain up for as long as possible
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Conclusions Various approaches exist to adjust the topology of a
network to a desired shape Most of them produce some non-negligible overhead
Some distributed coordination among neighbors require additional information.
Constructed structures can turn out to be somewhat brittle and the overhead might be wasted.
Benefits have to be carefully weighted against risks for the particular scenario at hand
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Y. Xu, J. Heidemann, and D. Estrin. Geography-Informed Energy Conservation for Ad Hoc Routing. In Proceedings of the 7th Annual International Conference on Mobile Computing and Networking (MobiCom), pages 70–84, Rome, Italy, July 2001. ACM.)
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