On Maximizing Network Lifetime of Broadcast in WANETs under an Overhearing Cost Model Guofeng Deng, Sandeep K.S. Gupta IMPACT Lab (http://impact.asu.edu)http://impact.asu.edu.

Slides:



Advertisements
Similar presentations
Mobility Increase the Capacity of Ad-hoc Wireless Network Matthias Gossglauser / David Tse Infocom 2001.
Advertisements

Shi Bai, Weiyi Zhang, Guoliang Xue, Jian Tang, and Chonggang Wang University of Minnesota, AT&T Lab, Arizona State University, Syracuse University, NEC.
Multicast in Wireless Mesh Network Xuan (William) Zhang Xun Shi.
Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks By C. K. Toh.
Gossip Scheduling for Periodic Streams in Ad-hoc WSNs Ercan Ucan, Nathanael Thompson, Indranil Gupta Department of Computer Science University of Illinois.
Wireless Mesh Networks 1. Architecture 2 Wireless Mesh Network A wireless mesh network (WMN) is a multi-hop wireless network that consists of mesh clients.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Wireless Sensor Networks 21st Lecture Christian Schindelhauer.
1 Minimum-energy broadcasting in multi-hop wireless networks using a single broadcast tree Department of Computer Science and Information Engineering National.
CS Dept, City Univ.1 Low Latency Broadcast in Multi-Rate Wireless Mesh Networks LUO Hongbo.
1 Multicast Routing with Minimum Energy Cost in Ad hoc Wireless Networks Xiaohua Jia, Deying Li and Frankie Hung Dept of Computer Science, City Univ of.
PEDS September 18, 2006 Power Efficient System for Sensor Networks1 S. Coleri, A. Puri and P. Varaiya UC Berkeley Eighth IEEE International Symposium on.
Beneficial Caching in Mobile Ad Hoc Networks Bin Tang, Samir Das, Himanshu Gupta Computer Science Department Stony Brook University.
E. AlthausMax-Plank-Institut fur Informatik G. CalinescuIllinois Institute of Technology I.I. MandoiuUC San Diego S. Prasad Georgia State University N.
WiOpt’03: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks March 3-5, 2003, INRIA Sophia-Antipolis, France Session : Energy Efficiency.
On the Construction of Energy- Efficient Broadcast Tree with Hitch-hiking in Wireless Networks Source: 2004 International Performance Computing and Communications.
Symmetric Connectivity With Minimum Power Consumption in Radio Networks G. Calinescu (IL-IT) I.I. Mandoiu (UCSD) A. Zelikovsky (GSU)
Speaker: Li-Sheng Chen 1 Jan 2, 2012 EOBDBR: an Efficient Optimum Branching-Based Distributed Broadcast Routing Protocol for Wireless Ad Hoc Networks.
Energy efficient multicast routing in ad hoc wireless networks Summer.
WiOpt’04: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks March 24-26, 2004, University of Cambridge, UK Session 2 : Energy Management.
LPT for Data Aggregation in Wireless Sensor networks Marc Lee and Vincent W.S Wong Department of Electrical and Computer Engineering, University of British.
Online Data Gathering for Maximizing Network Lifetime in Sensor Networks IEEE transactions on Mobile Computing Weifa Liang, YuZhen Liu.
A Cross Layer Approach for Power Heterogeneous Ad hoc Networks Vasudev Shah and Srikanth Krishnamurthy ICDCS 2005.
Lecture 8. Why do we need residual networks? Residual networks allow one to reverse flows if necessary. If we have taken a bad path then residual networks.
Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) Wireless Sensor Networks:
1 Algorithms for Bandwidth Efficient Multicast Routing in Multi-channel Multi-radio Wireless Mesh Networks Hoang Lan Nguyen and Uyen Trang Nguyen Presenter:
Timing-sync Protocol for Sensor Networks (TPSN) Presenter: Ke Gao Instructor: Yingshu Li.
CuMPE : CLUSTER-MANAGEMENT AND POWER EFFICIENT PROTOCOL FOR WIRELESS SENSOR NETWORKS ITRE’05 Information Technology: Research and Education Shen Ben Ho.
1 Power Control for Distributed MAC Protocols in Wireless Ad Hoc Networks Wei Wang, Vikram Srinivasan, and Kee-Chaing Chua National University of Singapore.
Lifetime and Coverage Guarantees Through Distributed Coordinate- Free Sensor Activation ACM MOBICOM 2009.
Efficient Gathering of Correlated Data in Sensor Networks
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 Cooperative Diversity- Based Robust MAC Protocol in wireless Ad Hoc Networks Sangman Moh, Chansu Yu Chosun University, Cleveland State University Korea,
Network Aware Resource Allocation in Distributed Clouds.
PHD DISSERTATION DEFENSE Receiver-Cost Cognizant Maximal Lifetime Routing in Embedded Networks: Model and Solutions Guofeng Deng Advised by Dr. Sandeep.
IEEE Globecom 2010 Tan Le Yong Liu Department of Electrical and Computer Engineering Polytechnic Institute of NYU Opportunistic Overlay Multicast in Wireless.
Wireless Sensor Networks COE 499 Energy Aware Routing
June 21, 2007 Minimum Interference Channel Assignment in Multi-Radio Wireless Mesh Networks Anand Prabhu Subramanian, Himanshu Gupta.
Maximizing Lifetime of Ad Hoc Networks/WSNs Using Dynamic Broadcast Scheme Guofeng Deng.
Arindam K. Das CIA Lab University of Washington Seattle, WA LIFETIME MAXIMIZATION IN ENERGY CONSTRAINED WIRELESS NETWORKS.
Maximum Network Lifetime in Wireless Sensor Networks with Adjustable Sensing Ranges Cardei, M.; Jie Wu; Mingming Lu; Pervaiz, M.O.; Wireless And Mobile.
Arindam K. Das CIA Lab University of Washington Seattle, WA MINIMUM POWER BROADCAST IN WIRELESS NETWORKS.
1 Multicast Algorithms for Multi- Channel Wireless Mesh Networks Guokai Zeng, Bo Wang, Yong Ding, Li Xiao, Matt Mutka Michigan State University ICNP 2007.
On Energy-Efficient Trap Coverage in Wireless Sensor Networks Junkun Li, Jiming Chen, Shibo He, Tian He, Yu Gu, Youxian Sun Zhejiang University, China.
G-REMiT: An Algorithm for Building Energy Efficient Multicast Trees in Wireless Ad Hoc Networks Bin Wang and Sandeep K. S. Gupta NCA’03 speaker : Chi-Chih.
Energy Efficient Broadcast in WANETs under an Overhearing Cost Model Guofeng Deng IMPACT Lab at ASU.
Multiuser Receiver Aware Multicast in CDMA-based Multihop Wireless Ad-hoc Networks Parmesh Ramanathan Department of ECE University of Wisconsin-Madison.
1 G-REMiT: An Algorithm for Building Energy Efficient Multicast Trees in Wireless Ad Hoc Networks Bin Wang and Sandeep K. S. Gupta Computer Science and.
Algorithms for Energy-Efficient Multicasting in Static Ad Hoc Wireless Networks Mobile Networks and Applications 6, ,2001 Author : JEFFREY E. WIESELTHIER.
Linear Programming & its Applications to Wireless Networks Guofeng Deng IMPACT Lab, Arizona State University.
LOCALIZED MINIMUM - ENERGY BROADCASTING IN AD - HOC NETWORKS Paper By : Julien Cartigny, David Simplot, And Ivan Stojmenovic Instructor : Dr Yingshu Li.
S. K. S. Gupta, Arizona State Univ On Maximizing Lifetime of Multicast Trees in Wireless Ad hoc Networks Bin Wang and Sandeep K. S. Gupta Computer Science.
© Yamacraw, Fall 2002 Power Efficient Range Assignment in Ad-hoc Wireless Networks E. Althous (MPI) G. Calinescu (IL-IT) I.I. Mandoiu (UCSD) S. Prasad.
Distributed Data Gathering Scheduling in Multi-hop Wireless Sensor Networks for Improved Lifetime Subhasis Bhattacharjee and Nabanita Das International.
Self-stabilizing energy-efficient multicast for MANETs.
Multicast Scaling Laws with Hierarchical Cooperation Chenhui Hu, Xinbing Wang, Ding Nie, Jun Zhao Shanghai Jiao Tong University, China.
On Mitigating the Broadcast Storm Problem with Directional Antennas Sheng-Shih Wang July 14, 2003 Chunyu Hu, Yifei Hong, and Jennifer Hou Dept. of Electrical.
1 Low Latency Multimedia Broadcast in Multi-Rate Wireless Meshes Chun Tung Chou, Archan Misra Proc. 1st IEEE Workshop on Wireless Mesh Networks (WIMESH),
Bin Wang, Arizona State Univ S-REMiT: A Distributed Algorithm for Source-based Energy Efficient Multicasting in Wireless Ad Hoc Networks Bin Wang and Sandeep.
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)
March 9, Broadcasting with Bounded Number of Redundant Transmissions Majid Khabbazian.
E. AlthausMax-Plank-Institut fur Informatik G. CalinescuIllinois Institute of Technology I.I. MandoiuUC San Diego S. Prasad Georgia State University N.
Introduction Wireless Ad-Hoc Network  Set of transceivers communicating by radio.
Implementation and Analysis of an Overhearing Cost Model based Broadcast Method for Wireless Sensor Networks Newton Alex Venkatraman Jayaraman.
Confidential & Proprietary – All Rights Reserved Internal Distribution, October Quality of Service in Multimedia Distribution G. Calinescu (Illinois.
Does Topology Control Reduce Interference?
Computing and Compressive Sensing in Wireless Sensor Networks
A Distributed Algorithm for Minimum-Weight Spanning Trees
Power Efficient Range Assignment in Ad-hoc Wireless Networks
Introduction Wireless Ad-Hoc Network
Maximizing Broadcast Tree Lifetime in Wireless Ad Hoc Networks
Presentation transcript:

On Maximizing Network Lifetime of Broadcast in WANETs under an Overhearing Cost Model Guofeng Deng, Sandeep K.S. Gupta IMPACT Lab ( Arizona State University, Tempe, AZ, USA

MPACT I Arizona State S.K.S Gupta, ICDCN'062 Outline Background and motivation Receiver Cost Models –Zero Receiver Cost (ZRC) model –Designated receiver cost (DRC) model –Overhearing Cost (OC) model NP-hardness and Approximation ratio Heuristic solutions Simulation results Conclusions

MPACT I Arizona State S.K.S Gupta, ICDCN'063 Wireless Broadcast Transmission Energy consumed for reliably transmitting to a node at distance d is proportional to d a where a >=2. Energy is also consumed for various tasks such as packet processing at the sender node and the receiver node. Local Broadcast –Wireless multicast advantage Assuming omnidirectional antenna, all the nodes in the transmission range of transmitting node recv the transmitted packet Energy consumption is equal to reach the farthest neighbor node – instead of sum of transmission power to reach each and every neighbor node. Network (Multihop) Broadcast –Flooding –Tree/Mesh based

MPACT I Arizona State S.K.S Gupta, ICDCN'064 Energy Efficient Broadcast in WANETs Minimum energy broadcast (minimizing total transmission power) –NP-hard –BIP [Wieselthier Infocom 2000], EWMA [Cagalj Mobicom 2002] Maximum lifetime broadcast (minimizing maximum transmission power) –Solvable in polynomial time –MST [Camerini IPL 1978][Kang ICC 2003], sub-network solution [Lloyd Mobihoc 2002][Floreen DIALM-POMC 2003], MDLT [Das Globecom 2003], Problem: Receiver cost was ignored. –Receiver cost matters. –TelosB mote: receiver power = peak transmission power

MPACT I Arizona State S.K.S Gupta, ICDCN'065 Maximizing Broadcast Tree Lifetime Broadcast tree lifetime: the period of time for the first node to die, i.e., the ratio of battery capacity (E u )to power consumption (p u ) i.e. E u /p u. Maximizing Broadcast Tree Lifetime (MaxBTL): Find a broadcast tree that maximizes the broadcast tree lifetime among all the broadcast trees rooted at the given source node. In the case of identical battery capacity, broadcast tree lifetime is decided by the maximum nodal power consumption. Here, we assume identical battery capacity for simplicity.

MPACT I Arizona State S.K.S Gupta, ICDCN'066 Receiver Power Models DRC † –The receiver power, which may vary from node to node, is fixed regardless of the signal strength at the receiver. –E.g., p a T = 16mW –If p a R = 5mW, then p a = 21mW TRC † [Cui ICC 2003][Vasudevan et al. Infocom’06] –The receiver power for decoding a signal is a function of the transmission power of the transmitter as well as the distance between them. –E.g., p a R = d 3 /p s T and d = 5m. p a R = 10.4mW when p s T = 12mW; when p s T increases to 20mW, p a R reduces to 6.25mW. † DRC and TRC are called CORP and TREPT in [Deng&Gupta Globecom’06] respectively.

MPACT I Arizona State S.K.S Gupta, ICDCN'067 OC Model A node – whether intended or unintended receiver - consumes energy for receiving packets transmitted by any neighboring nodes. Amount of power consumed for receiving a packet is constant – but can be node-dependent., –N is the set of nodes –X(u,v)=1 if v recv packets from u, otherwise X(u,v)=0. E.g., and.

MPACT I Arizona State S.K.S Gupta, ICDCN'068 OC - Example Assuming required transmission power is symmetric between each pair of neighboring nodes; an unitary receiver cost of s is mW. Then, p s R = 5mW because s overheard the transmission from a to b and c. p s R = 0 under any model that does not take into account overhearing cost.

MPACT I Arizona State S.K.S Gupta, ICDCN'069 Broadcast Lifetime Example s b a t t t+ε The broadcast tree lifetime is decided by the minimum node lifetime. In the case of identical battery capacity, it is determined by the maximum nodal power consumption. We will present the formal definition shortly. t - transmission power r - receiving power ε - a sufficiently small value We assume t=r s b a t t A maximum lifetime tree in the case of 0-receiving cost OC s b a t t t+r r MAX=t+r=2t s b a t t+ε OC An optimal solution MAX=tr s b a t t+ε t r Lifetime is half of the optimal!

MPACT I Arizona State S.K.S Gupta, ICDCN'0610 MaxBTL under ORC: A Difficult Problem NP-hardness: by reducing set cover to MaxBTL Approximation ratio of ZRP and DRP, which are optimal solutions under the ZRC and DRC models respectively, can be as bad as n/2-1. Let t=r. MAX(b)=5r; MAX(c)=3r; MAX(d)=2r. Then put more nodes on the border…

MPACT I Arizona State S.K.S Gupta, ICDCN'0611 Prim-Like Greedy Algorithms Prim-Like Greedy Algorithms: –Starts from a single node tree consisting of the source node –Grows the tree iteratively: choosing the best link that connects an on-tree to a non-on-tree node until all the nodes in the network are included in the tree For example, ZRP weights each link in the network graph in terms of transmission power threshold. Proposed heuristic solutions, CRP & PRP, are Prim-like greedy algorithms. We will discuss the link selection criteria in terms of power consumption by assuming identical battery capacity, but the algorithm can be easily modified to accommodate non-identical capacity case. Notice: Prim’s algorithm is used to generate a Minimum- weight Spanning Tree (MST) in an undirected graph; the resulting tree may not be a MST in a directed graph.

MPACT I Arizona State S.K.S Gupta, ICDCN'0612 CRP: Cumulative Receiver Power Weight of a link (u,v) is defined as the larger of the following values: –Transmission power over link (u,v), denoted by p(u,v), plus the overall cost of u for receiving/overhearing at the time of being selected. –Unitary receiver cost of v The best link is the one with the lowest weight. s a b c d An on-tree link An overhearing link The theoretic worst case of CRP is also n/2-1 as shown in the aforementioned network graph.

MPACT I Arizona State S.K.S Gupta, ICDCN'0613 PRP: Proximity Receiver Power Weight of a link (u,v) is defined as the largest of the following values: –Transmission power over link (u,v) plus the overall cost of u for receiving/overhearing at the time of being selected. –The power consumption of a nearby node that is going to be affected by the added link (increased transmission power) –Unitary receiver cost of v The best link is the one with the lowest weight. s a b c d An on-tree link An overhearing link A potential overhearing link

MPACT I Arizona State S.K.S Gupta, ICDCN'0614 Algorithms Summary

MPACT I Arizona State S.K.S Gupta, ICDCN'0615 Simulation Results Note: Each caption includes battery capacity, peak transmission power and unitary receiving power.

MPACT I Arizona State S.K.S Gupta, ICDCN'0616 Simulation Results (Cont’d) (a)The 4 curves perfectly overlap in the case of 0-receiver cost. (c) Results in asymmetric wireless medium (asymmetric transmission power threshold) (l) Results of non-identical battery capacity.

MPACT I Arizona State S.K.S Gupta, ICDCN'0617 Conclusion TDMA based MAC layer (Designated Receiver Cost) DRC: Proposed an optimal solution [Deng & Gupta Globecom 2006] (Was named CORP) DRA: Proposed a binary search algorithm (optimal) when the tree is given [Deng & Gupta Globecom 2006] (Was named TREPT) Random access MAC layer (Overhearing Cost) ORC (OC): NP-hard problem. Proposed two heuristics solutions [This paper] ORA: NP-hard problem. Future work. Constant Receiver CostAdaptive Receiver Cost Receiver power matters Future directions: distributed solutions and more results on adaptive receiver cost

MPACT I Arizona State S.K.S Gupta, ICDCN'0618 Thank You!

MPACT I Arizona State S.K.S Gupta, ICDCN'0619 Maximizing Broadcast Tree Lifetime Network model –Power consumption is the sum of transmission and receiving power consumption –Transmission power control –Wireless multicast advantage (WMA) –Receiving power will be discussed shortly –Finite battery power capacity and linear battery power model, i.e., the lifetime of a node is the ratio between the amount of battery energy and power consumption. Problem statement –Broadcast tree lifetime: the period of time for the first node to die –MaxBTL: find a broadcast tree that maximizes the broadcast tree lifetime among all the broadcast trees rooted at the given source node.