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An Energy-Efficient Flooding
Algorithm in ad hoc network(APE)
Concrete Mathematic
mid-term presentation of term project
Professor: Kwangjo Kim
Group 16: Tran Minh Trung, Nguyen Duc Long
An Energy-efficient Flooding
Algorithm in ad hoc network (EFA)
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Introduction
Related works
Proposed solution
Simulation (Ongoing)
I. Introduction(1) – Ad hoc Network
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Ad hoc Network
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lack of fixed infrastructure
peer-to-peer (all nodes act as routers)
multi-hop routing
frequent connection / topology changes
Challenges:
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Security, Scalability
QOS, load balancing
Effect on device’s battery life – Network’s
life time
I. Introduction(2) – Paper objective
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Objective
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Prolong Network life
Reduce traffic load at Routing discovery phase
Related works: MBCR, MMBCR
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Limitations:
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Make power consumptions eventually distributed on every node.
Redundancy routing discovery processes
All nodes take part in a routing process passively that makes a
nodes run out of energy fast, especially, when it has to serve many
routing process at the same time
Proposed solution:
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enable each node actively in saving its residual energy
capacity while the network connectivity is still be guarantied
II. Related work(1) - MBCR
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MBCR: (Minimum battery cost routing)
This protocol use remaining battery capacity of
each host as a metric to describe the lifetime of
each mobile host.
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1
f i (c )  t
ci
t
i
Rj 
Over Used
Node
f(i)=40
i 0
Chosen route
Route 1
D j 1

f(i)=10
f i (cit )
R j  min{ R j | j  A}
Source
Destination
Route 2
f(i)=30
f(i)=30
II. Related work(2) - MMBCR
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MMBCR: Min-Max battery cost routing
 Eliminate routing containing week node:
R j  max ( f i (cit ))
R j  min{ R j | j  A}
f(i)=40
irouteJ
f(i)=10
Eng=8W
Eng=0W
Eng=4
Eng=2
Route 1
Source
Destination
Route 2
f(i)=30
f(i)=30
f(i)=20
Waste energy in
case of short
time connection
III. Proposed solution:EFA(1)
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Overview
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Enable each node actively in saving its residual
energy capacity while the network connectivity is
still be guarantied
Algorithm: Flooding filter
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New RREQ header:
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Source Addr, current seq#, Dest Addr, Dest seq#
Broadcast ID
Require energy level:
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Eth = (packets)*Pcs
Eth = (packets)*Prc
III. Proposed solution:EFA(2)
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Immediate node:
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Calculate available energy
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In case of serving j node at the same time
 Eav = Nre Erq(j)
Otherwise
 Eav = Nre
Comparing available energy with require energy
level
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Case 1: Eav >= Eth : take part in routing process
Case 2: Eav < Eth : reject routing process
III. Proposed solution:EFA(3)
Flooding filter
Traffic load
M B C R ,M M B C R
16
14
12
10
8
6
4
2
0
Traffic load
A PR A
1
2
3
4
5
6
Broadcasting tim e
• Advantages of flooding filter:
Reduce traffic load at discovery
routing phase
 Reduce interference between
nodes
 Reduce power consumption at
discovery routing phase
Reduce the deviation between
require energy level and the
energy available of each node
III. Proposed solution:EFA(4)
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Case 1: Eav >= Eth
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Check current routing process in routing table
(Check fresh route, hope count …)
Update/add routing table if necessary (set reserve
path for new routing process:
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source node’s IP address, seq.#
the number of hops to the source
IP address of the neighbor from which the RREQ
was received
Energy requirement for this routing process
Send IACK back to the node which the RREQ was
received from
III. Proposed solution:EFA(5)
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Case 2: Eav < Eth
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Discard RREQ packet
If : P = {Ni | Eav ≥ Eth, Ni Є
Immediate nodes} = Ø
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After Tfck, Reduce Eth at source node
automatically
Eth = Eth - Dst; Dst = Sre/λ (λ=10)
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This step will repeat until P ≠ Ø Or Tfck ≥ TTL
Re broadcast RREQ with new Eth
IV. Simulation
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Simulation model:
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50 mobile nodes
are generated randomly in an area of 500M*500M.
The moving speed of each node is 10m/s.
20 connections is established during 900 seconds
simulation times.
The energy model:
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initial energy of each node is 20mW.
The energy usage for receiving and sending each
packet are txPower = 0.6mW and rxPower =
0.3mW respectively.