Scalable Data Aggregation for Dynamic Events in Sensor Networks Kai-Wei Fan, Sha Liu, Prasun Sinha Computer Science and Engineering, Ohio State University.

Slides:



Advertisements
Similar presentations
Advisor : Prof. Yu-Chee Tseng Student : Yi-Chen Lu 12009/06/26.
Advertisements

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.
Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks By C. K. Toh.
A Distributed Algorithm for the Dead End Problem of Location Based Routing in Sensor Networks Le Zou, Mi Lu, Zixiang Xiong, Department of Electrical Engineering,
CLUSTERING IN WIRELESS SENSOR NETWORKS B Y K ALYAN S ASIDHAR.
Tufts Wireless Laboratory Tufts University School Of Engineering Energy-Efficient Structuralized Clustering for Sensor-based Cyber Physical Systems Jierui.
Advisor : Prof. Yu-Chee Tseng Student : Yi-Chen Lu 12009/6/26.
An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Network
Computer Science 1 CSC 774 Advanced Network Security Enhancing Source-Location Privacy in Sensor Network Routing (ICDCS ’05) Brian Rogers Nov. 21, 2005.
Defending Against Traffic Analysis Attacks in Wireless Sensor Networks Security Team
Good afternoon everyone.
Haiyun Luo, Fan Ye, Jerry Cheng, Songwu Lu, Lixia Zhang
Dissemination protocols for large sensor networks Fan Ye, Haiyun Luo, Songwu Lu and Lixia Zhang Department of Computer Science UCLA Chien Kang Wu.
The Impact of Spatial Correlation on Routing with Compression in WSN Sundeep Pattem, Bhaskar Krishnamachri, Ramesh Govindan University of Southern California.
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.
On the Energy Efficient Design of Wireless Sensor Networks Tariq M. Jadoon, PhD Department of Computer Science Lahore University of Management Sciences.
2015/7/15 H igh- S peed N etworking L ab. Dynamic Forwarding over Tree-on-DAG for Scalable Data Aggregation in Sensor Networks High-Speed Networking Lab.
Modeling Data-Centric Routing in Wireless Sensor Networks Bhaskar Krishnamachari, Deborah Estrin, Stephan Wicker.
Steady and Fair Rate Allocation for Rechargeable Sensors in Perpetual Sensor Networks Zizhan Zheng Authors: Kai-Wei Fan, Zizhan Zheng and Prasun Sinha.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2007 (TPDS 2007)
LPT for Data Aggregation in Wireless Sensor Networks Marc Lee and Vincent W.S. Wong Department of Electrical and Computer Engineering, University of British.
Mobility Limited Flip-Based Sensor Networks Deployment Reporter: Po-Chung Shih Computer Science and Information Engineering Department Fu-Jen Catholic.
M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha.
A Framework for Energy- Saving Data Gathering Using Two-Phase Clustering in Wireless Sensor Networks Wook Chio, Prateek Shah, and Sajal K. Das Center for.
A novel gossip-based sensing coverage algorithm for dense wireless sensor networks Vinh Tran-Quang a, Takumi Miyoshi a,b a Graduate School of Engineering,
2015/10/1 A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks Tai-Jung Chang, Kuochen Wang, Yi-Ling Hsieh Department.
SoftCOM 2005: 13 th International Conference on Software, Telecommunications and Computer Networks September 15-17, 2005, Marina Frapa - Split, Croatia.
Rate-based Data Propagation in Sensor Networks Gurdip Singh and Sandeep Pujar Computing and Information Sciences Sanjoy Das Electrical and Computer Engineering.
Scalable Data Aggregation for Dynamic Events in Sensor Networks Kai-Wei Fan Kai-Wei Fan, Sha Liu, and Prasun.
Efficient Deployment Algorithms for Prolonging Network Lifetime and Ensuring Coverage in Wireless Sensor Networks Yong-hwan Kim Korea.
1 An Adaptive Energy-Efficient and Low-Latency MAC for Data Gathering in Wireless Sensor Network Gang Lu, Bhaskar Krishnamachari, and Cauligi Raghavendra.
RELAX : An Energy Efficient Multipath Routing Protocol for Wireless Sensor Networks Bashir Yahya, Jalel Ben-Othman University of Versailles, France ICC.
Dynamic Source Routing in ad hoc wireless networks Alexander Stojanovic IST Lisabon 1.
Convergecast with MIMO Luoyi Fu, Yi Qin, Xinbing Wang Department of Electronic Engineering Shanghai Jiao Tong University, China Xue Liu Department of Computer.
College of Engineering Grid-based Coordinated Routing in Wireless Sensor Networks Uttara Sawant Major Advisor : Dr. Robert Akl Department of Computer Science.
WEAR: A Balanced, Fault-Tolerant, Energy-Aware Routing Protocol for Wireless Sensor Networks Kewei Sha, Junzhao Du, and Weisong Shi Wayne State University.
Energy-Efficient Shortest Path Self-Stabilizing Multicast Protocol for Mobile Ad Hoc Networks Ganesh Sridharan
Secure and Energy-Efficient Disjoint Multi-Path Routing for WSNs Presented by Zhongming Zheng.
Efficient Energy Management Protocol for Target Tracking Sensor Networks X. Du, F. Lin Department of Computer Science North Dakota State University Fargo,
DRP: An Efficient Directional Routing Protocol for Mobile Ad Hoc Networks Hrishikesh Gossain Mesh Networks Product Group, Motorola Tarun Joshi, Dharma.
A Dead-End Free Topology Maintenance Protocol for Geographic Forwarding in Wireless Sensor Networks IEEE Transactions on Computers, vol. 60, no. 11, November.
NTU IM Page 1 of 35 Modelling Data-Centric Routing in Wireless Sensor Networks IEEE INFOCOM Author: Bhaskar Krishnamachari Deborah Estrin Stephen.
MMAC: A Mobility- Adaptive, Collision-Free MAC Protocol for Wireless Sensor Networks Muneeb Ali, Tashfeen Suleman, and Zartash Afzal Uzmi IEEE Performance,
By Naeem Amjad 1.  Challenges  Introduction  Motivation  First Order Radio Model  Proposed Scheme  Simulations And Results  Conclusion 2.
Chih-Min Chao and Yao-Zong Wang Department of Computer Science and Engineering National Taiwan Ocean University, Taiwan IEEE WCNC 2010 A Multiple Rendezvous.
S& EDG: Scalable and Efficient Data Gathering Routing Protocol for Underwater Wireless Sensor Networks 1 Prepared by: Naveed Ilyas MS(EE), CIIT, Islamabad,
Toward a Packet Duplication Control for Opportunistic Routing in WSNs Georgios Z. Papadopoulos, Julien Beaudaux, Antoine Gallais, Periklis Chatzimisios,
Dynamic Forwarding over Tree-on-DAG for Scalable Data Aggregation in Sensor Networks Kai-Wei Fan Sha Liu Prasun Sinha Arun Sudhir.
A Wakeup Scheme for Sensor Networks: Achieving Balance between Energy Saving and End-to-end Delay Xue Yang, Nitin H.Vaidya Department of Electrical and.
1 An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks Tijs van Dam, Koen Langendoen In ACM SenSys /1/2005 Hong-Shi Wang.
Ching-Ju Lin Institute of Networking and Multimedia NTU
SHORT: Self-Healing and Optimizing Routing Techniques for Mobile Ad Hoc Networks Presenter: Sheng-Shih Wang October 30, 2003 Chao Gui and Prasant Mohapatra.
A Coverage-Preserving Node Scheduling Scheme for Large Wireless Sensor Networks Di Tian, and Nicolas D. Georanas ACM WSNA ‘ 02.
Localized Low-Power Topology Control Algorithms in IEEE based Sensor Networks Jian Ma *, Min Gao *, Qian Zhang +, L. M. Ni *, and Wenwu Zhu +
Centralized Transmission Power Scheduling in Wireless Sensor Networks Qin Wang Computer Depart., U. of Science & Technology Beijing Edward Y. Hua Wireless.
Decentralized Energy-Conserving and Coverage-Preserving Protocols for Wireless Sensor Networks Chi-Fu Huang, Li-Chu Lo, Yu-Chee Tseng, and Wen-Tsuen Chen.
Load Balanced Link Reversal Routing in Mobile Wireless Ad Hoc Networks Nabhendra Bisnik, Alhussein Abouzeid ECSE Department RPI Costas Busch CSCI Department.
“LPCH and UDLPCH: Location-aware Routing Techniques in WSNs”. Y. Khan, N. Javaid, M. J. Khan, Y. Ahmad, M. H. Zubair, S. A. Shah.
I-Hsin Liu1 Event-to-Sink Directed Clustering in Wireless Sensor Networks Alper Bereketli and Ozgur B. Akan Department of Electrical and Electronics Engineering.
Scalable and Robust Data Dissemination in Wireless Sensor Networks Wei Liu, Yanchao Zhang, Yuguang Fang, Tan Wong Department of Electrical and Computer.
Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime Z. Maria Wang, Emanuel Melachrinoudis Department of Mechanical and Industrial Engineering.
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)
Structure-Free Data Aggregation in Sensor Networks.
Wireless Access and Networking Technology (WANT) Lab. An Efficient Data Aggregation Approach for Large Scale Wireless Sensor Networks Globecom 2010 Lutful.
Scalable Coverage Maintenance for Dense Wireless Sensor Networks Jun Lu, Jinsu Wang, Tatsuya Suda University of California, Irvine Secon ‘ 06.
Exploring Random Access and Handshaking Techniques in Large- Scale Underwater Wireless Acoustic Sensor Networks Peng Xie and Jun-Hong Cui Computer Science.
Presentation transcript:

Scalable Data Aggregation for Dynamic Events in Sensor Networks Kai-Wei Fan, Sha Liu, Prasun Sinha Computer Science and Engineering, Ohio State University ACM SenSys 2006

Outline Introduction Structure-Less Aggregation Experiments and Simulation Conclusion

Introduction Data Aggregation  Communication cost is often larger than computation cost.  Redundancy in raw data.  Aggregate packets near sources to reduce transmission cost.  Prolong the lifetime. Aggregation Approaches  Static structure  Dynamic structure  Structure-free

Static Structure for Aggregation Routing on a pre-computed structure Pros  Low maintenance cost  Good for unchanged traffic pattern Cons  Long stretch problem  Unsuitable for event-based network Sink

Dynamic Structure for Aggregation Create a structure dynamically Pros  Optimization for source nodes Cons  High maintenance cost Sink

Structure-Free Aggregation No structure  No structure maintenance cost Aggregation without structure  Where to transmit?  Wait for whom? Improve aggregating by transmitting packets to the same node at the same time  Spatial Convergence  Data Aware Anycast  Temporal Convergence  Random Waiting

Data Aware Anycast Anycast  One-to-any forwarding Anycast t o neighbor having packets for aggregating  Class A: Nodes closer to the sink with data for aggregation  Class B: Nodes with data for aggregation  Class C: Nodes closer to the sink Class B Canceled CTS RTS CTS Sender Class A Nbr Class B Nbr Class C Nbr Class A Nbr Class AClass C

Random Waiting Fixed Delay  Nodes close to sink pick high delay. Random Delay  Source nodes pick random delay between 0 and τ before transmission. … Sink τ =n τ =n-1 τ =n-2 τ =1 τ =0

DAA and RW Example Sink Not guarantee aggregation of all packets from a single event !!

Structure-Less Aggregation Structure-free aggregation does not guarantee all packets are completely aggregated to one.  High cost for un-aggregated or partial-aggregated packets Structure-Less Aggregation (2 Phases)  1 st : Based on structure-free aggregation (DAA & RW) Aggregate packets form sources to aggregators locally  2 nd : Further aggregation on an implicitly constructed structure Aggregate packets from aggregators to sink Tree on Directed Acyclic Graphic (ToD)

Definition  Contiguous events  Cell: A square area with side length greater than the diameter which an event can span  F-cluster: First cluster, composed of multiple cells  S-cluster: Second cluster, composed of multiple cells (interleaved with F- cluster) 1D Construction of ToD F-clusterS-cluster

Tree on Directed Acyclic Graphic(ToD) sink F-clusters F-cluster-head Shortest Path a b c d F6 sink S-cluster S-cluster-head Shortest Path a bc d S5 S6 sink a b c d Shortest Path Tree F6 S6S5

Dynamic Forwarding for 1D (1) Forwarding Rules  Rule 0: Forward packets to F-aggregator by structure-free data aggregation protocol.  Rule 1: Event spans two cells in a F-cluster, forward to sink  Rule 2: Event spans one cells, forward to appropriate S-aggregator sink

Dynamic Forwarding for 1D (2) Property 1. Packets will be aggregated at a F-aggregator, or will be aggregated at a S-aggregator.  If only nodes in one cell are triggered and generate the packets  Aggregated at one F-aggregator (all nodes in a cell resides in the same F-cluster)  If nodes in two cells are triggered and generate the packets. Two cells are in the same F-cluster  aggregated at the F-aggregator Two cells are in different F-clusters  aggregated at the S-aggregator

Tree on Directed Acyclic Grahpic(ToD) 2D Construction A1A2B1B2C1C2 A3A4B3B4C3C4 D1D2E1E2F1F2 D3D4E3E4F3F4 G1G2H1H2I1I2 G3G4H3H4I3I4 A B D E GH F I C (a) F-clusters(b) Cells A1A2B1B2C1C2 A3A4B3B4C3C4 D1D2E1E2F1F2 D3D4E3E4F3F4 G1G2H1H2I1I2 G3G4H3H4I3I4 (c) S-clusters S1S2 S3S4 S3S4 S2S1

Dynamic Forwarding for 2D (1) Event may span multiple cells in a F-cluster  Assume the region spanned by an event is contiguous.  Maximum 4 cells (a) 1 Cell(a) 2 Cells(a) 3 Cells(a) 4 Cells No other F-cluster will have packets  Forward to sink Forward to other S-aggregators

Dynamic Forwarding for 2D (2) Forwarding Rules  Rule 0: Forward packets to F-aggregator by structure-free data aggregation protocol.  Rule 1: Event spans three or four cells in a F-cluster, forwards to sink.  Rule 2: Event spans a cell in a F-cluster, forward to a S-aggregator. F-cluster Corresponding S-cluster Cell generating packets

Dynamic Forwarding for 2D (2)  Rule 3: Event spans two cells, forward to two S-aggregators in order. C1C2 F-cluster X F-cluster Y S-cluster I S-cluster II C C  Forward to 1st S-aggregator (near sink), then forward to 2nd S-aggregator Sink F-aggregator S-aggregator

Dynamic Forwarding Example Example C3 C1C2 Sink Rule 0Rule 2Rule 3

Aggregator Selections Nodes play the role of F-aggregator in turn.  With probability based on residual energy  Hash current time to a node within that cluster Delegate the role of S-aggregator to F-aggregator  Select the F-aggregator in the F-cluster near sink as the S-aggregator Sink F-aggregator and S-aggregator (Right-top S-cluster) Sink

Dynamic Forwarding for 2D (3) Property 2. Packets will be aggregated at the F- aggregator, at the 1 st S-aggregator, or at the 2 nd S- aggregator.

Experiments (1) Experiments Environment  105 Mica2-based nodes  7 x 15 grid network  Node spacing: 3 feet  Transmission range: 2 grid-neighbor  2 F-clusters  Fixed event location Protocols  Dynamic Forwarding over ToD (ToD)  Data Aware Anycast (DAA)  Shortest Path Tree (SPT)  Shortest Path Tree with Fixed Delay (SPT-D)

Experiments (2) Event Size Better Performance: More chance of being aggregated Long Stretch Problem

Experiments (3) Delay Stable: Random Delay Better Performance: Heavily depends on delay

Experiments (4) Large Simulation Environment  2000m x 1200m area  1938 nodes (grid network)  Node spacing: 35m  Transmission range: 50m  Cell side length = Event diameter  Event with random way-point model at 10m/s for 400 seconds Protocols  ToD  DAA  SPT  OPT

Experiments (5) Event Size Best but not consider overhead

Experiments (6) Scalability (Event with different distance to sink)  Event Size: 400m  Event Area: 400m x 800m  Area Distance to Sink : 200m ~ 1400m

Experiments (7) Cell Size  Event Size: 200m, 400m, 600m  Best Cell Size: 200m Event  100m Cell 400m Event  200m Cell 600m Event  200m Cell  Future Work: Select appropriate cell size

Conclusion The paper proposes a semi-structured approach (ToD) that locally uses a structure-less technique followed by Dynamic Forwarding. ToD avoids the long stretch problem in fixed structured approach and eliminates the overhead of maintenance of dynamic structure.