An Energy-Efficient Flooding Algorithm in ad hoc Network (EFA)

Concrete Mathematic Final 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)

Related Works Problem statement Proposed Solution Simulation & Evaluation

Related Works

Related work (Previous paper: PAODV, APRA, MMBCR) Congested node, Week node: Reject or relay the coming

connection -> Reduce the network connectivity Single path from source to destination: Slow transmission

speed, Increase control packet over head -> Waste energy

Disjoint a single path in to multiple paths (dependent on energy capacity of each sub path)

Balance the power consumption between Strong node and week node -> reduce the partition problem (1)

Increase Network Connectivity -> Reduce routing discovery phase (2) 1,2 -> Increase Network life time

Proposed solution

Problem statement (1)

Run “Routing discovery phase” again many times -> Waste time

+ Energy consuming

Run “Routing discovery phase”fewer time -> Save time, Energy

Problem statement (2)

Ad Hoc model: Directed Graph

G(V, E) where V is the set of all nodes and E is the set of all directed links (i, j) where i, j V.

Ni Set of all neighbors nodes of node (i)

(i) (j)

- Directed graph: G(V,E)

- Weighted link: E(i,j)

- Set of neighbor node: N(i)

Problem statement (2)

Node (i) Energy available:

In case of serving j node at the same time

Otherwise

∑f(j,i) =∑f(i,k)

e(i) = er(i) - erq(i)

e(i) = er(i)

f(j,i)

f(i,k)

i

Problem statement (3)

Directed link (i,j) exist if and only if

J Ni

Energy capacity of link (i,j)

Life time of a routing path = Life time of each link or each node

Source

e(1)=10 e(2)=40

Dest.

e(3)=60 e(4)=40

Link 1: e(sd)= Min (e(3),e(4))=40Link 2: e(sd)= Min (e(1),e(2))=10

…

e(i,j) = Min (e(i),e(j))

e(5)=10

Path 1 is created with energy

capacity = 8,Hop count = 3

RREQ Flooding method

RREQ 10

RREQ(1) 4 RREQ(1) 4

RREQ(2) 8

RREQ(2) 8

RREQ(2) 8

Path 1 is created with energy

capacity = 4, Hop count = 4

RREQ(1) 4RREQ(1) 8

Lexicographic order A routing path will be chosen dependent

on 3 information Fresh sequence number: F(i) Min Energy capacity: E(i) Hop count to destination: H(i)

The path will be selected dependent on lexicographic order Path i: (F(i), E(i), H(i)) The number of path is dependent on the

total energy requirement, and the energy available of all possible paths

X

X

XX

The mesh network example (1)

X

X

X

X

X

X

X

X

X

X

X

X

Node with energy level 2

Node with energy level 3

Node with energy level 1

RREQ for link level 1RREQ for link level 2

RREQ for link level 3

• Full capacity: 10• Capacity levels:

- Level 1: 1 -> 4- Level 2: 4 -> 7- Level 3: 7 -> 10

Eliminated because of containing

weaker node

Eliminated because of backward flooding

(Increase hop count)

The mesh network example (2)

Node with energy level 2

Node with energy level 3

Node with energy level 1

RREP for link level 1RREP for link level 2

RREP for link level 3

• Full capacity: 10• Capacity levels:

- Level 1: 1 -> 4- Level 2: 4 -> 7- Level 3: 7 -> 10

Simulation & Evaluation(1) Simulation model:

10 - 50 mobile nodes are generated randomly in an area of

500M*500M. The moving speed of each node is 5m/s. 2-20 connections is established during 900

seconds simulation times. The energy model:

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.

Simulation & Evaluation(2)

Expiration sequences of nodes Routing overhead (control messages)

Simulation & Evaluation(3)

Route reliability End to End Delay

Conclusion (1) Final achievement:

Use concrete mathematic knowledge for writing higher quality paper

Graph theory Directed graph Weighted link

Lexicographic Order Set theory

Experiences in dealing with NS2, Perl, gnuplot

Conclusion (2) Contribution:

Proposed new Energy-Efficient Flooding Algorithm for Ad-Hoc routing protocol

Simulation results shows betters performance Future plan:

Complete full paper (With more different & complicated scenarios – mobility measurement)

After getting review & advice from Profesor -> submit to international conference

Apply some Stochastic and Mathematical model -> Journal paper

Progress after midterm report Writing simulation program by NS2

( Tran Minh Trung) Generate scenarios by TCL script Apply energy model to standard scenarios Write simulation results to log files

Analisys simulation log files ( Nguyen Duc Long) Write perl modules (Collect & Split data) Write drawing script by gnuplot (linux)

Reference Paper:

T.M Trung, S.-L. Kim, “An Adaptive Power Aware Routing Algorithm for Ad Hoc Networks”, Submitted to ICWC – Toronto Canada 2003

H.X.Tung, T.M Trung, V.D Liem, P.V Su, “Power – Aware Ad-Hoc Ondemand Distance Vector Routing Protocol”, KISA 2003

Charles E. Perkins, Elizabeth M. Belding-Royer, and Samir Das. "Ad Hoc On Demand Distance Vector (AODV) Routing." IETF Internet draft, draft-ietf-manet-aodv-10.txt, March 2002 (Work in Progress).

C.-K. Toh, H. Cobb, and D.A. Scott, “Performance evaluation of battery-life aware routing schemes for wireless Ad Hoc Networks” in Proc. IEEE, ICC

Books & Link: Discrete Mathematics and Its Applications, 4th edition , Kenneth H. Rosen,

McGRAW-HILL, 1999

http://mathworld.wolfram.com/LexicographicOrder.html