AEDG:AUV aided Efficient Data Gathering Routing Protocol for UWSNs Prepared by: Mr. Naveed Ilyas CIIT, Islamabad, Pakistan 1.

Slides:



Advertisements
Similar presentations
Underwater Sensor Network Presented By: Sabbir Ahmed Khan
Advertisements

Mobility Increase the Capacity of Ad-hoc Wireless Network Matthias Gossglauser / David Tse Infocom 2001.
Network Coding Schemes for Underwater Networks The Benefits of Implicit Acknowledgement Daniel E. Lucani, Muriel Médard, Milica Stojanovic Massachusetts.
Kyung Tae Kim, Hee Yong Youn (Sungkyunkwan University)
Energy–efficient Reliable Broadcast in Underwater Acoustic Networks Paolo Casari and Albert F Harris III University of Padova, Italy University of Illinois.
DAREPage 1 Distance Aware Relaying Energy-efficient: DARE to Monitor Patients in Multi-hop Body Area Sensor Networks Prepared by: Anum Tauqir.
Good afternoon everyone.
Ad-Hoc Networking Course Instructor: Carlos Pomalaza-Ráez D. D. Perkins, H. D. Hughes, and C. B. Owen: ”Factors Affecting the Performance of Ad Hoc Networks”,
On the Construction of Energy- Efficient Broadcast Tree with Hitch-hiking in Wireless Networks Source: 2004 International Performance Computing and Communications.
Online Data Gathering for Maximizing Network Lifetime in Sensor Networks IEEE transactions on Mobile Computing Weifa Liang, YuZhen Liu.
Adaptive Self-Configuring Sensor Network Topologies ns-2 simulation & performance analysis Zhenghua Fu Ben Greenstein Petros Zerfos.
Component-Based Routing for Mobile Ad Hoc Networks Chunyue Liu, Tarek Saadawi & Myung Lee CUNY, City College.
Talha Naeem Qureshi Joint work with Tauseef Shah and Nadeem Javaid
UnderWater Acoustic Sensor Networks (UW-ASN) -Xiong Junjie
Special Topics on Algorithmic Aspects of Wireless Networking Donghyun (David) Kim Department of Mathematics and Computer Science North Carolina Central.
M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol
EAIT, February 2006 A Pragmatic Approach towards the Improvement of Performance of Ad Hoc Routing ProtocolsOptimizations To Multipath Routing Protocols.
07/21/2005 Senmetrics1 Xin Liu Computer Science Department University of California, Davis Joint work with P. Mohapatra On the Deployment of Wireless Sensor.
A Simple and Effective Cross Layer Networking System for Mobile Ad Hoc Networks Wing Ho Yuen, Heung-no Lee and Timothy Andersen.
1 Optimal Power Allocation and AP Deployment in Green Wireless Cooperative Communications Xiaoxia Zhang Department of Electrical.
CSE 6590 Fall 2010 Routing Metrics for Wireless Mesh Networks 1 4 October, 2015.
10/6/20151 Mobile Ad hoc Networks COE 549 Power Control Tarek Sheltami KFUPM CCSE COE
1 Core-PC: A Class of Correlative Power Control Algorithms for Single Channel Mobile Ad Hoc Networks Jun Zhang and Brahim Bensaou The Hong Kong University.
Wireless Sensor Networks COE 499 Energy Aware Routing
Influence of Transmission Power on the Performance of Ad Hoc Networks Crystal Jackson SURE 2004.
A Non-Monetary Protocol for P2P Content Distribution in Wireless Broadcast Networks with Network Coding I-Hong Hou, Yao Liu, and Alex Sprintson Dept. of.
Prediction Assisted Single-copy Routing in Underwater Delay Tolerant Networks Zheng Guo, Bing Wang and Jun-Hong Cui Computer Science & Engineering Department,
Energy-Efficient Protocol for Cooperative Networks IEEE/ACM Transactions on Networking, Apr Mohamed Elhawary, Zygmunt J. Haas Yong Zhou
1 Mobility Increases the Capacity of Ad-hoc Wireless Networks Matthias Grossglauser, David Tse IEEE Infocom 2001 (Best paper award) Oct 21, 2004 Som C.
Efficient Deployment Algorithms for Prolonging Network Lifetime and Ensuring Coverage in Wireless Sensor Networks Yong-hwan Kim Korea.
Abdul Wahid, Sungwon Lee, Dongkyun Kim Kyungpook National University , Korea IEEE Oceans 2011.
Maximum Lifetime Routing in Wireless Sensor Networks by Collins Adetu Nicole Powell Course: EEL 5784 Instructor: Dr. Ming Yu.
VAPR: Void Aware Pressure Routing for Underwater Sensor Networks
Cross-layer Packet Size Optimization for Wireless Terrestrial, Underwater, and Underground Sensor Networks IEEE INFOCOM 2008 Mehmet C. Vuran and Ian F.
PRoPHET+: An Adaptive PRoPHET- Based Routing Protocol for Opportunistic Network Ting-Kai Huang, Chia-Keng Lee and Ling-Jyh Chen.
REECH ME: Regional Energy Efficient Cluster Heads based on Maximum Energy Routing Protocol Prepared by: Arslan Haider. 1.
SIMPLE: Stable Increased Throughput Multi-hop Link Efficient Protocol For WBANs Qaisar Nadeem Department of Electrical Engineering Comsats Institute of.
Efficient Energy Management Protocol for Target Tracking Sensor Networks X. Du, F. Lin Department of Computer Science North Dakota State University Fargo,
Junfeng Xu, Keqiu Li, and Geyong Min IEEE Globecom 2010 Speak: Huei-Rung, Tsai Layered Multi-path Power Control in Underwater Sensor Networks.
KAIS T High-throughput multicast routing metrics in wireless mesh networks Sabyasachi Roy, Dimitrios Koutsonikolas, Saumitra Das, and Y. Charlie Hu ICDCS.
A Power Assignment Method for Multi-Sink WSN with Outage Probability Constraints Marcelo E. Pellenz*, Edgard Jamhour*, Manoel C. Penna*, Richard D. Souza.
By Naeem Amjad 1.  Challenges  Introduction  Motivation  First Order Radio Model  Proposed Scheme  Simulations And Results  Conclusion 2.
1 Mitigate the Bottleneck of Underwater Acoustic Sensor Networks via Priority Scheduling Junjie Xiong, Michael R. Lyu, Kam-Wing Ng.
S& EDG: Scalable and Efficient Data Gathering Routing Protocol for Underwater Wireless Sensor Networks 1 Prepared by: Naveed Ilyas MS(EE), CIIT, Islamabad,
Authors: N. Javaid, M. Aslam, K. Djouani, Z. A. Khan, T. A. Alghamdi
Presented by: Sheeraz Ahmed 1.  ARCUN a reliable, energy-efficient and high throughput routing protocol  Cooperative routing a potential scheme for.
Maximizing Lifetime per Unit Cost in Wireless Sensor Networks
Sanjay K. Dhurandher, Mohammad S. Obaidat, Fellow of IEEE and Fellow of SCS, Siddharth Goel and Abhishek Gupta CAITFS, Division of Information Technology,
Tufts Wireless Laboratory School Of Engineering Tufts University Paper Review “An Energy Efficient Multipath Routing Protocol for Wireless Sensor Networks”,
CSR: Cooperative Source Routing Using Virtual MISO in Wireless Ad hoc Networks IEEE WCNC 2011 Yang Guan, Yao Xiao, Chien-Chung Shen and Leonard Cimini.
1 A Multi-Rate Routing Protocol with Connection Entropy for MANETs Cao Trong Hieu, Young Cheol Bang, Jin Ho Kim, Young An Kim, and Choong Seon Hong Presenter:
Wireless Access and Networking Technology Lab WANT Energy-efficient and Topology-aware Routing for Underwater Sensor Networks Xiaobing Wu, Guihai Chen and.
An Enhanced Cross-Layer Protocol for Energy Efficiency in Wireless Sensor Networks Jaehyun Kim, Dept. of Electrical & Electronic Eng., Yonsei University;
Chance Constrained Robust Energy Efficiency in Cognitive Radio Networks with Channel Uncertainty Yongjun Xu and Xiaohui Zhao College of Communication Engineering,
“LPCH and UDLPCH: Location-aware Routing Techniques in WSNs”. Y. Khan, N. Javaid, M. J. Khan, Y. Ahmad, M. H. Zubair, S. A. Shah.
Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime Z. Maria Wang, Emanuel Melachrinoudis Department of Mechanical and Industrial Engineering.
Abstract 1/2 Wireless Sensor Networks (WSNs) having limited power resource report sensed data to the Base Station (BS) that requires high energy usage.
On Mobile Sink Node for Target Tracking in Wireless Sensor Networks Thanh Hai Trinh and Hee Yong Youn Pervasive Computing and Communications Workshops(PerComW'07)
Efficient Geographic Routing in Multihop Wireless Networks Seungjoon Lee*, Bobby Bhattacharjee*, and Suman Banerjee** *Department of Computer Science University.
Optimizing Network Performance through Packet Fragmentation in Multi- hop Underwater Communications Stefano Basagni ∗, Chiara Petrioli † Roberto Petroccia.
-1/16- Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks C.-K. Toh, Georgia Institute of Technology IEEE.
Ing-Ray Chen, Member, IEEE, Hamid Al-Hamadi Haili Dong Secure and Reliable Multisource Multipath Routing in Clustered Wireless Sensor Networks 1.
Reporter: Hung-Wei Liu Advisor: Tsung-Hung Lin 1.
Author:Zarei.M.;Faez.K. ;Nya.J.M.
ABSTRACT   Recent work has shown that sink mobility along a constrained path can improve the energy efficiency in wireless sensor networks. Due to the.
Xiaohua (Edward) Li and Juite Hwu
Distributed Energy Efficient Clustering (DEEC) Routing Protocol
Net 435: Wireless sensor network (WSN)
Capacity of Ad Hoc Networks
A Distributed Clustering Scheme For Underwater Sensor Networks
Presentation transcript:

AEDG:AUV aided Efficient Data Gathering Routing Protocol for UWSNs Prepared by: Mr. Naveed Ilyas CIIT, Islamabad, Pakistan 1

 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 2  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): [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.

 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 3

 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) = N t (f) + N s (f) + N w (f) + N th (f) where N t, N s, N w and N th represent the noise due to turbulence, shipping, wind and thermal activities. 4 [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.

 Proposed routing protocol: AEDG -Acoustic attenuation models Computation of Transmission Loss (TL) by MMPE [5] model TL = m (f, s, d A, d B ) + w(t) + e(n) where: m( f, s, d A, d B ): 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 [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.

 Proposed routing protocol: AEDG - Constraint Optimized Model 6

7 Fig 1. Data flow between nodes

 Proposed routing protocol: AEDG - Two Phase Communication Protocol Initialization phase GN selection criterion Member nodes association Data transmission phase 8  Based on RSSI value of ‘hello packet’  Selected from direct communication range of AUV  Rotated on the basis of residual energy threshold  Member nodes are associated through SPT  Restriction on count of member nodes  Data transmission by using SPT  Residual energy based threshold for GNs  Selection of next eligible GN on the basis of maximum residual energy

 Proposed routing protocol: AEDG - Performance evaluation ParameterValue Number of nodes100 Network size300m x 200m Initial energy of normal nodes70 J Packet size125 bytes Transmission Range30 m Number of AUVs1 9 Network Parameters Table. I Network performance parameters used in simulation

10 Figure 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

11 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.

 AEDG: Performance evaluation Average network throughput 12 Figure 10: Average network throughput in AEDG, AEERP and AURP

 AEDG: Performance evaluation 13 Figure 11: End-to-end delay in AEDG, AEERP and AURP End-to-end delay of AEDG is greater than AURP and AEERP because nodes transmit for longer time. End-to-end delay of AEDG is 25% more than AEERP and 32% more than AURP.

 AEDG: Performance evaluation Average end-to-end delay 14 Figure 12: End-to-end delay in AEDG, AEERP and AURP

 AEDG: Performance evaluation 15 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..

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 16