Shi Bai, Weiyi Zhang, Guoliang Xue, Jian Tang, and Chonggang Wang University of Minnesota, AT&T Lab, Arizona State University, Syracuse University, NEC.

Slides:



Advertisements
Similar presentations
Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 1 Chapter 12 Cross-Layer.
Advertisements

Multipath Routing for Video Delivery over Bandwidth-Limited Networks S.-H. Gary Chan Jiancong Chen Department of Computer Science Hong Kong University.
Mobility Increase the Capacity of Ad-hoc Wireless Network Matthias Gossglauser / David Tse Infocom 2001.
1 EP2210 Fairness Lecture material: –Bertsekas, Gallager, Data networks, 6.5 –L. Massoulie, J. Roberts, "Bandwidth sharing: objectives and algorithms,“
A 2 -MAC: An Adaptive, Anycast MAC Protocol for Wireless Sensor Networks Hwee-Xian TAN and Mun Choon CHAN Department of Computer Science, School of Computing.
Jaringan Komputer Lanjut Packet Switching Network.
An Energy Efficient Routing Protocol for Cluster-Based Wireless Sensor Networks Using Ant Colony Optimization Ali-Asghar Salehpour, Babak Mirmobin, Ali.
LOGO Video Packet Selection and Scheduling for Multipath Streaming IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 9, NO. 3, APRIL 2007 Dan Jurca, Student Member,
1 EL736 Communications Networks II: Design and Algorithms Class8: Networks with Shortest-Path Routing Yong Liu 10/31/2007.
Cooperative Multiple Input Multiple Output Communication in Wireless Sensor Network: An Error Correcting Code approach using LDPC Code Goutham Kumar Kandukuri.
1 Sensor Relocation in Mobile Sensor Networks Guiling Wang, Guohong Cao, Tom La Porta, and Wensheng Zhang Department of Computer Science & Engineering.
Supporting Stored Video: Reducing Rate Variability and End-toEnd Resource Requirements through Optimal Smoothing By James D. salehi, Zhi-Li Zhang, James.
Deployment of Surface Gateways for Underwater Wireless Sensor Networks Saleh Ibrahim Advising Committee Prof. Reda Ammar Prof. Jun-Hong Cui Prof. Sanguthevar.
Lecture 3. Preview of Markov Process A sequence of random variables X 1, X 2,….,X n,….. such that –X i+1 is independent of X 1,….X i-1 given X i –Pr(X.
Path Protection in MPLS Networks Ashish Gupta Design and Evaluation of Fault Tolerance Algorithms with Performance Constraints.
Dissemination protocols for large sensor networks Fan Ye, Haiyun Luo, Songwu Lu and Lixia Zhang Department of Computer Science UCLA Chien Kang Wu.
An Efficient Clustering-based Heuristic for Data Gathering and Aggregation in Sensor Networks Wireless Communications and Networking (WCNC 2003). IEEE,
A General approach to MPLS Path Protection using Segments Ashish Gupta Ashish Gupta.
The Impact of Multihop Wireless Channel on TCP Throughput and Loss Presented by Scott McLaren Zhenghua Fu, Petros Zerfos, Haiyun Luo, Songwu Lu, Lixia.
Maximum Network lifetime in Wireless Sensor Networks with Adjustable Sensing Ranges Mihaela Cardei, Jie Wu, Mingming Lu, and Mohammad O. Pervaiz Department.
Maximizing the Lifetime of Wireless Sensor Networks through Optimal Single-Session Flow Routing Y.Thomas Hou, Yi Shi, Jianping Pan, Scott F.Midkiff Mobile.
A Cross Layer Approach for Power Heterogeneous Ad hoc Networks Vasudev Shah and Srikanth Krishnamurthy ICDCS 2005.
September 12, 2006IEEE PIMRC 2006, Helsinki, Finland1 On the Packet Header Size and Network State Tradeoff for Trajectory-Based Routing in Wireless Networks.
A Transmission Control Scheme for Media Access in Sensor Networks Alec Woo, David Culler (University of California, Berkeley) Special thanks to Wei Ye.
Wireless Video Sensor Networks Vijaya S Malla Harish Reddy Kottam Kirankumar Srilanka.
Experimental study of the effects of Transmission Power Control and Blacklisting in Wireless Sensor Networks Dongjin Son, Bhaskar Krishnamachari and John.
Distributed Quality-of-Service Routing of Best Constrained Shortest Paths. Abdelhamid MELLOUK, Said HOCEINI, Farid BAGUENINE, Mustapha CHEURFA Computers.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2007 (TPDS 2007)
DaVinci: Dynamically Adaptive Virtual Networks for a Customized Internet Jennifer Rexford Princeton University With Jiayue He, Rui Zhang-Shen, Ying Li,
1 IEEE Trans. on Smart Grid, 3(1), pp , Optimal Power Allocation Under Communication Network Externalities --M.G. Kallitsis, G. Michailidis.
QoS-Aware In-Network Processing for Mission-Critical Wireless Cyber-Physical Systems Qiao Xiang Advisor: Hongwei Zhang Department of Computer Science Wayne.
A Simple and Effective Cross Layer Networking System for Mobile Ad Hoc Networks Wing Ho Yuen, Heung-no Lee and Timothy Andersen.
1 Optimal Multicast Smoothing of Streaming Video over an Internetwork S. Sen, D. Towsley, Z-L. Zhang, J. Dey
On Renewable Sensor Networks with Wireless Energy Transfer IEEE INFOCOM 2011 Yi Shi, Liguang Xie, Y. Thomas Hou, Hanif D. Sherali.
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.
Computer Networks Performance Metrics. Performance Metrics Outline Generic Performance Metrics Network performance Measures Components of Hop and End-to-End.
DARP: Distance-Aware Relay Placement in WiMAX Mesh Networks Weiyi Zhang *, Shi Bai *, Guoliang Xue §, Jian Tang †, Chonggang Wang ‡ * Department of Computer.
Copyright: S.Krishnamurthy, UCR Power Controlled Medium Access Control in Wireless Networks – The story continues.
Optimal Base Station Selection for Anycast Routing in Wireless Sensor Networks 指導教授 : 黃培壝 & 黃鈴玲 學生 : 李京釜.
Logical Topology Design
Congestion Control in CSMA-Based Networks with Inconsistent Channel State V. Gambiroza and E. Knightly Rice Networks Group
QoS Routing in Networks with Inaccurate Information: Theory and Algorithms Roch A. Guerin and Ariel Orda Presented by: Tiewei Wang Jun Chen July 10, 2000.
Cross-layer Packet Size Optimization for Wireless Terrestrial, Underwater, and Underground Sensor Networks IEEE INFOCOM 2008 Mehmet C. Vuran and Ian F.
OPTIMUM INTEGRATED LINK SCHEDULING AND POWER CONTROL FOR MULTI-HOP WIRELESS NETWORKS Arash Behzad, and Izhak Rubin, IEEE Transactions on Vehicular Technology,
An Energy-Efficient MAC Protocol for Wireless Sensor Networks Qingchun Ren and Qilian Liang Department of Electrical Engineering, University of Texas at.
Junfeng Xu, Keqiu Li, and Geyong Min IEEE Globecom 2010 Speak: Huei-Rung, Tsai Layered Multi-path Power Control in Underwater Sensor Networks.
Simultaneous routing and resource allocation via dual decomposition AUTHOR: Lin Xiao, Student Member, IEEE, Mikael Johansson, Member, IEEE, and Stephen.
1 - CS7701 – Fall 2004 Review of: Detecting Network Intrusions via Sampling: A Game Theoretic Approach Paper by: – Murali Kodialam (Bell Labs) – T.V. Lakshman.
Tufts Wireless Laboratory School Of Engineering Tufts University Paper Review “An Energy Efficient Multipath Routing Protocol for Wireless Sensor Networks”,
Energy-aware Node Placement in Wireless Sensor Networks Global Telecommunications Conference 2004 (Globecom 2004) Peng Cheng, Chen-Nee Chuah Xin Liu UCDAVIS.
Multicast Scaling Laws with Hierarchical Cooperation Chenhui Hu, Xinbing Wang, Ding Nie, Jun Zhao Shanghai Jiao Tong University, China.
MATE: MPLS Adaptive Traffic Engineering Anwar Elwalid Cheng Jin Steven Low Indra Widjaja Bell Labs Michigan altech Fujitsu 2006.
Delay in packet switched network. Circuit switching In Circuit switched networks the resources needed along a path (buffers and link transmission rate)
LECTURE 12 NET301 11/19/2015Lect NETWORK PERFORMANCE measures of service quality of a telecommunications product as seen by the customer Can.
CprE 458/558: Real-Time Systems (G. Manimaran)1 CprE 458/558: Real-Time Systems Energy-aware QoS packet scheduling.
Indian Institute of Technology Bombay 1 Communication Networks Prof. D. Manjunath
Toward Reliable and Efficient Reporting in Wireless Sensor Networks Authors: Fatma Bouabdallah Nizar Bouabdallah Raouf Boutaba.
1 Low Latency Multimedia Broadcast in Multi-Rate Wireless Meshes Chun Tung Chou, Archan Misra Proc. 1st IEEE Workshop on Wireless Mesh Networks (WIMESH),
Courtesy Piggybacking: Supporting Differentiated Services in Multihop Mobile Ad Hoc Networks Wei LiuXiang Chen Yuguang Fang WING Dept. of ECE University.
Optimizing Network Performance through Packet Fragmentation in Multi- hop Underwater Communications Stefano Basagni ∗, Chiara Petrioli † Roberto Petroccia.
Wireless Access and Networking Technology (WANT) Lab. An Efficient Data Aggregation Approach for Large Scale Wireless Sensor Networks Globecom 2010 Lutful.
Optimization-based Cross-Layer Design in Networked Control Systems Jia Bai, Emeka P. Eyisi Yuan Xue and Xenofon D. Koutsoukos.
Exploring Random Access and Handshaking Techniques in Large- Scale Underwater Wireless Acoustic Sensor Networks Peng Xie and Jun-Hong Cui Computer Science.
Designing Multi-hop Wireless Backhaul Networks with Delay Guarantees Girija Narlikar, Gordon Wilfong, and Lisa Zhang Bell Lab. Infocom 2006.
Computing and Compressive Sensing in Wireless Sensor Networks
Computer Network Performance Measures
Computer Network Performance Measures
Net301 LECTURE 10 11/19/2015 Lect
Yiannis Andreopoulos et al. IEEE JSAC’06 November 2006
ADVISOR : Professor Yeong-Sung Lin STUDENT : Hung-Shi Wang
Presentation transcript:

Shi Bai, Weiyi Zhang, Guoliang Xue, Jian Tang, and Chonggang Wang University of Minnesota, AT&T Lab, Arizona State University, Syracuse University, NEC Lab 2012 IEEE INFOCOM 1

 1. Introduction  2. Algorithm ◦ 2.1 Definition ◦ 2.2 Problem statement ◦ 2.3 DEAR Algorithm  3. Experiment  4. Conclusion 2

 Wireless Sensor Networks ◦ Key Issue: Energy Consumption  Delay-bounded Energy-constrained Adaptive Routing (DEAR) Problem ◦ Adaptive reliability  Splitting the traffic over multiple paths ◦ Differential delay  Increased memory and buffer overflow ◦ Deliverable energy constraints  Energy consumption of transmitting packet 3

 Def 1. Packet Allocation ◦ P is a set of s-BS paths. ◦ The aggregated packet of link e is the sum of the packet allocations on link e of the paths in P:  q(e) = ƩL(p)  Def 2. Differential delay ◦ d h => the highest path delay ◦ d l => the lowest path delay ◦ => D p = d h – d l 4

 Def 3. Energy Consumption ◦ Transmitting energy consumption  E = w*q  q => packet size transmitted on link  w => Energy consumption of transmitting 1 bit  W=[C*(2^b-1)+F]*(1/b)  C => the quality of transmission and noise power  F => the power consumption of electronic circuitry  Def 4. Latency/Delay ◦ Queuing delay  The time waiting at output link for transmission ◦ Transmission delay  The amount of time required to push all of the packet bits into the transmission media ◦ Propagation delay  The time takes for the head of the signal to travel from the sender to the receiver 5

 Transmission delay ◦ Ignored transmission and queuing delay ◦ Without considering the transmission delay  Allocate of packets have no impact on delivery of packets  Path:p1=(A,B,BS), p2=(A,C,BS), p3=(A,BS)  Path delay: d(p1)=2, d(p2)=3, d(p3)=2  Ex a) packet split => p1=10, p3 = 2  Ex b) packet split => p1= 6, p3 = 6  Path delay are the same  Differential delay  d(p1)-d(p3) = 2 – 2 = 0 6

◦ Considering the transmission delay  Allocations of packets on multiple paths will have impact on path delays  Path delay  d(p1) = Ʃd(e) + ƩƬ(v)  Ex a) d(p1) = 2 + (10 pk/(2 pk/s) + 10/2) = 12, d(p3) = 2 + (2/4) = 2.5  Ex b) d(p1) = 2 +(6/2 + 6/2) = 8, d(p3) = 2 + (6/4) = 3.5  Path delay are different  Ex a) Differential delay is 9.5=( )  Ex b) Differential delay is 4.5 7

 DEAR(Delay-bounded Energy constrained Adaptive Routing) ◦ Seek set of paths P that can provide the following  Delay bounded  Energy constrained  Adaptive reliability  Graph G=(V, E, b, d, w, β) ◦ V represents the set of sensor nodes and BS. ◦ E represents the set of links. ◦ b represents bandwidth ◦ d represents the delay of the path p ◦ w represents transmission energy consumption ◦ β represents the residual energy of sensor v 8

 Delay Bounded ◦ Any path p in P must satisfy the differential delay constraint: d min ≤ d(p) ≤ d max  Energy Constrained ◦ The energy consumption of transmitting packet for each sensor i cannot exceed its residual energy level β(i)  Adaptive reliability ◦ The size of aggregated packet of all paths in P is no less than Q : q(P) ≥ Q ◦ Route the data such that any single link failure does no affect more than x% of the total packets 9

 Feasible and infeasible solution by Adaptive reliability and delay constraint ◦ Ex c ) 2,2,8  In case 8 packet drop => 67% ◦ Ex d) 6,4,2  In case delay is 8 over between 4 and 5 ◦ Ex e) 2,10  In case 10 packet drop => over 70% 10

 IDEAR 11

 Linear Program solution 12

 ODEAR problem ◦ Optimization problem  SPDEAR problem ◦ (1+α) approximation algorithm 13

14 ◦ Each u[t] means that node u can transmit packet at time t. ◦ This bandwidth ensures that the packets sent by u at time i can not exceed b(e). ◦ This ensures that only the packet, which arrive at BS no earlier than d min and no later than d max.

 Requirement Condition ◦ Packet Demand: 12 Packet ◦ Reliability requirement x% = 70% ◦ Delay requirement: d min = 2 and d max = 5  Maximum flow by IDEAR ◦ P1=(A[0],B[2],BS[4],BS[5]) ◦ P2=(A[0],C[3],BS[5]) ◦ P3=(A[0],BS[3],BS[4],BS[5]) ◦ P4=(A[0],A[1],BS[4],BS[5]) 15

 Fully Polynomial Time Approximation Scheme for SPDEAR ◦ Scaling and rounding technique ◦ d Θ = ⌊d(e)*Θ⌋

 Approximation algorithm for ODEAR ◦ d min ≥ 0 17

 Efficient Heuristic for DEAR ◦ Round the propagation delay of each link ◦ d min and d max 18

 Network topologies in an 100 * 100 sq  The power of Sensor node was randomly distributed in [16, 20]  Bandwidth, propagation delay and transmission energy consumption of each communication link was randomly distributed in [6,10], [1,5], [1,3] 19

 Performance of different number of nodes 20

 Performance of different reliability requirements 21

 Performance of different packet sizes 22

 Transmission delay in multipath routing ◦ The previous work ignored  Delay-bounded Energy-constrained Adaptive Routing (DEAR) ◦ Adaptive multipath routing ◦ Energy constraint ◦ Differential delay 23

 Thank you. 24