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Transcript of An Energy-Efficient Flooding Algorithm in ad hoc Network (EFA) Concrete Mathematic Final...
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