The Capacity of EnergyConstrained Mobile Networks
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c
Xiangyu Chen Riheng Jia Xinbing Wang
Contents
1
Introduction
2
Our Work
3
Future Work
Conclusion
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Introduction
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Related Work
2
Our contribution
3
Realistic significance
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Related Work
 Gupta & Kumar
The throughput in fixed ad-hoc network
in its best performance is  1 n
 
 Grossglauser
Mobility can increases the capacity to
with relay
 Seung-Woo Ko
1
The capacity can achieve 1 in energy
constrained Mobile network using vehicle
charging model
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Our Contribution
 Using Base Station to charge the node
in the Mobile Network
 Combine vehicle and Base Station to
the Mobile Network
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Realistic significance
 Proof the througput 1 in variety
energy constrained circumstance.
 Present the parameter's influence on
the throughput
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Our Work
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1
Network model
2
Base Station Charging
3
Combine BS & vehicle
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Network model
Pc: probabilities that a BS charges a node
Pt: a node transmits a packet given the node has at
least one unit of energy
R:Charging range
r:transmission range
W:number of Base Station
m:number of nodes
X: number of node that Base Station can charge.
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Network model
 Two-phase scheduling policy
Phase 1.In odd time slots, source can transmit packets
to relays or destination
Phase 2. source and relay nodes can transmit packets
to destination.
In each time slot, a node becomes a transmitter with probability q or
a receiver with probability 1−q
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Network model
 Interference
Transmitter i successfully delivers a
packet to receiver j when the following
conditions are satisfied:
• The distance between them is no more
than r.
• The distances between node j and the
other transmitting nodes are no less than
r.
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Charging model
0
P(d ) 
 Charging model
where Rx={d:E×τ(d)=x} τ(d)is a non-increasing
function of d
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Base Station Charging
 Pc
the possibility that BS can charge a node given
the energy of the node is not full
m 1



 X 
2 i
2
Pc  WRB  min 1,
C
i

R
1


R
m 1
B
B

i

1


i 0
2
 Pt

m 1i

the possibility that node can send a package to
another node given the node has at least one energy

Pt  q 1  [1  (1  q)r 2 ]m1
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
Base Station Charging
 Markov Chain
 (3) pc
 (2) pc
 (2) pc
 (1) pc
pt
 (i )
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p0
 (1) pc
 (2) pc
 (1) pc
π(2)
π(1)
π(0)
 (3) pc
pt
 (1) pc
π(4)
π(3)
pt
pt
state that a node have i energy
possibility of the steady state that a node have 1 energy
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Base Station Charging
 equilibrium Equation
k 1
( pc  pt ) pk  pt pk 1   pi pc  k i
i 0
Using G conversion,we have
pt (1   )(1  z )
P( z ) 
pt (1  z )  pc z[1  G ( z )]
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pc G ' (1)
1  p0   
pt
Base Station Charging
 Pon
G ' (1) pc
Pon  1  p0 
pt
m
( )
pon 
n
(1)
and as n increase
if m=O(n)
Otherwise
 Throughput
Throughput 
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(1  e
m

 ( q 1)
4
2
)
 q  pon  e

 qpon
4

m
( con n )
n
if m<O(n)
(1)
Otherwise
Combine BS & vehicle
 Now suppose that we use vehicle to
charge node and vehicle also need
Base Station to provide its energy
PBS: probabilities that a BS charges a vehicle
Pt: a node transmits a packet given the node has at
least one unit of energy
pC: probabilities that a vehicle charges a node
RBS:BS charging range
Rc:Vehicle charging range
r:transmission range
W:number of Base Station m:number of nodes
X: number of node that Base Station can charge.
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Combine BS & vehicle

The vehicle's steady state Markov Chain
p BS
pc
 (i )
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 ( 0)
π(2)
π(1)
π(0)

pc

p BS
p BS
p BS
π(4)
π(3)
pc

pc

state that a vehicle have i energy
possibility of the steady state that a vehicle have i energy
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Combine BS & vehicle

The node's steady state Markov Chain
pt
 (i )
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 ( 0)
π(2)
π(1)
π(0)
pc
pc
pc
pt
pc
π(4)
π(3)
pt
pt
state that a node have i energy
possibility of the steady state that a node have i energy
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Combine BS & vehicle
 Pon-vehicle
2
pBS
WRB
Pon  1  p0  

2 n

1

(
1


R
)
pc
 Pon-node
ponv pc
Pon node  

pt
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Wm
O( 2 )
n
O(1)
W<O(m) & m<O(n)
Otherwise
Combine BS & vehicle
 Throughput
wm
Throughput 
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(1  e

 ( q 1)
4
2
)
 q  pon  e

 qpon
4

Wm
2
( 2 con n )
n
(1)
if m<O(n) &
W<O(m)
Otherwise
Simulation
 BS charging
m=O(1)
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m=O(n)
Simulation
 BS && vehicle charging
m=O(1) W=O(1)
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m=O(n) W=O(m)
Future Work
 BS && vehicle
charging
Sub Text
 (3) pc
 (3) pc
 (2) pc
 (2) pc
 (1) pc
 (1) pc
π(1)
π(0)
π(2)
 (2) pc
 (1) pc
 (1) pc
π(4)
π(3)
 (1) pt  (1) pt  (1) pt
 (1) pt
 (2) pt
 (2) pt
 (2) p
t
 (3) pt
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 (3) pt
Future Work
2
 (3)(1    (i )) pc
2
 (3)(1    (i )) pc
i 0
i 0
Sub Text
1
1
i 0
i 0
 (2)(1    (i )) pc  (2)(1    (i )) pc  (2)(1   (i )) p

c
1
i 0
 (1)(1   (0)) pc (1)(1   (0)) pc  (1)(1   (0)) pc  (1)(1   (0)) pc
π(1)
π(0)
pt
Yourπ(2)
Text
Here
pt
π(4)
π(3)
pt
Your
Text
Here
pt
If we can solve these Markov Chain above,we can solve
the problem of multilevel charging in the condition of
BS & vehicle charging.
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Q&A
Q & A
Conclusion
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Thank
you!
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