AEDG:AUV aided Efficient Data Gathering Routing

Protocol for UWSNs

Prepared by: Mr. Naveed Ilyas

CIIT, Islamabad, Pakistan1

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Related work and motivation

• In AUV-aided underwater routing protocol for underwater acoustic sensor networks (AURP) [1]

Low stability period High energy consumption

• In AUV aided energy efficient routing protocol for underwater acoustic sensor network (AEERP) [2]

Number of member nodes per GN High energy depletion at GNs Low throughput

No energy threshold mechanism to balance the energy consumption

No mechanism to limit the number of associated members with the GNs

Majority of nodes alive for small duration which decreases the network throughput

[1] Yoon, S., Azad, A. K., Oh, H., & Kim, S.. "AURP: An AUV-aided underwater routing protocol for underwater acoustic sensor networks." Sensors 12.2 (2012): 1827-1845.[2] Ahmad, A., Wahid, A., & Kim, D. "AEERP: AUV aided energy efficient routing protocol for underwater acoustic sensor network." Proceedings of the 8th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks. ACM, 2013.

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Research Aim/Objective

• Research Aim• Maximize the total amount of data collected by AUV• Improve the energy efficiency for data gathering

• Research Idea• Optimized elliptical path for efficient data gathering• Mathematical modeling for elliptical trajectory

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AUV-aided Efficient Data Gathering routing protocol (AEDG)-Acoustic attenuation models

• Attenuation A (l, f) can be computed by Thorp’s model [3] as follows:

10log(A(l, f))= k x 10log(l)+l x 10log(α(f))

where the first term denotes spreading loss and the second term is the absorption loss. k defines the geometry of the signal propagation.

• Calculation of ambient noise [4]

N(f) = Nt (f) + Ns (f) + Nw (f) + Nth (f)

where Nt , Ns , Nw and Nth represent the noise due to turbulence, shipping, wind and thermal activities.

[3] M. Stojanovic , On the relationship between capacity and distance in an underwater acoustic communication channel, ACM Mobile Computing and Communications Review, 11, (4), (2007), 34–43.[4] A. F. Harris III, M. Zorzi , Modeling the underwater acoustic channel in ns2, in: Proceedings of the 2nd international conference on Performance evaluation methodologies and tools, ICST, 2007, p. 18.

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Proposed routing protocol: AEDG-Acoustic attenuation models

• Computation of Transmission Loss (TL) by MMPE [5] model

TL = m (f , s , d A , dB ) + w(t) + e(n)

where:

m( f , s , d A , dB): Propagation loss due to haphazard and periodic constituents

f : Frequency of acoustic signal in kHz

d A : Depth of sender node A in m

d B: Depth of receiver node B in m

s: Euclidean distance between node A and node B in m

w(t): Function to estimate loss due to wave movement

e(n): Signal loss function caused by random noise error[5]K. B. Smith, “Convergence, stability, and variability of shallow water acoustic predictions using a split-step fourier parabolic equation model,” Journal of Computational Acoustics, vol. 9, no. 01, pp. 243–285, 2001.

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Proposed routing protocol: AEDG- Constraint Optimized Model

Maximize (1)

Subject to:• ≤ Etotal (1.a)

• (1.b)

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Proposed routing protocol: AEDG- Constraint Optimized Model

• (1.c)

• (1.d)

• (1.e)

• , , ≥ 0 d ij =d ij+µ

Fig 1. Data flow between nodes

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Proposed routing protocol: AEDG- Two Phase Communication Protocol

• Initialization phase• GN selection criterion• Member nodes association

• Data transmission phaseBased on RSSI value of ‘hello packet’Selected from direct communication range of AUVRotated on the basis of residual energy threshold

Member nodes are associated through SPTRestriction on count of member nodes

Data transmission by using SPTResidual energy based threshold for GNsSelection of next eligible GN on the basis of maximum residual energy

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Proposed routing protocol: AEDG-Performance evaluation

Parameter Value

Number of nodes 100

Network size 300m x 200m

Initial energy of normal nodes 70 J

Packet size 125 bytes

Transmission Range 30 m

Number of AUVs 1

• Network Parameters

Table. I Network performance parameters used in simulation

10Figure 8: Number of dead nodes in AEDG, AEERP and AURP

•Stability period of AURP decreases because of unbalanced energy consumption.Next GN is selected when first one die out which decreases its stability period. • Stability period of AEERP increases due to residual energy threshold at GNs.•AEDG has more stability period because of restriction on number of member nodes association and residual energy based threshold at GNs.

AEDG: Performance evaluation

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AEDG: Performance evaluation

Figure 9: Network throughput in AEDG, AEERP and AURP

•In AEDG, the maximum number of nodes alive for long duration•Restriction on GNs enhances the stability period and hence more nodes are available to relay the data of far end nodes which leads to increase the network throughput. •AEDG has enhanced network throughput as compared to AURP and AEERP because nodes transmit packets for longer duration.

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AEDG: Performance evaluation

• Average network throughput

Figure 10: Average network throughput in AEDG, AEERP and AURP

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AEDG: Performance evaluation

Figure 11: End-to-end delay in AEDG, AEERP and AURP

•End-to-end delayof AEDG is greater than AURP and AEERP because nodes transmit for longer time.

•End-to-end delay of AEDG is 25% more than AEERPand 32% more than AURP.

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AEDG: Performance evaluation

• Average end-to-end delay

Figure 12: End-to-end delay in AEDG, AEERP and AURP

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AEDG: Performance evaluation

Figure 13: Path-loss in AEDG, AEERP and AURP

•Path loss depends upon distance between sender and receiver and is effected by wave movement also.

•AURP/ AEERP - > network evolves -> intermediate nodes die quickly -> path loss increases..

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Conclusion• Thesis presents efficient data gathering routing schemes for

UWSNs that considers• MILP model• Optimal trajectory of AUV by using CDS• Optimal calculation of β through Monte Carlo simulation

• Addressed problems of:• low data delivery ratio• energy hole problem • high energy consumption

• Simulation results have proved that our protocol performs well in harsh oceanic condition in terms of:• data gathering• energy consumption