ELECTIONEL ECTI ON ELECTION: Energy-efficient and Low- latEncy sCheduling Technique for wIreless sensOr Networks Shamim Begum, Shao-Cheng Wang, Bhaskar.

Slides:



Advertisements
Similar presentations
SELF-ORGANIZING MEDIA ACCESS MECHANISM OF A WIRELESS SENSOR NETWORK AHM QUAMRUZZAMAN.
Advertisements

Decentralized Reactive Clustering in Sensor Networks Yingyue Xu April 26, 2015.
Presented by Rick Skowyra
Routing Protocols for Sensor Networks Presented by Siva Desaraju Computer Science WMU An Application Specific Protocol Architecture for Wireless Microsensor.
Kyung Tae Kim, Hee Yong Youn (Sungkyunkwan University)
An Application-Specific Protocol Architecture for Wireless Microsensor Networks Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan (MIT)
Introduction to Wireless Sensor Networks
Aeon LEACH An Efficient LEACH protocol in Heterogeneous and Homogenous Wireless Sensor Networks Under Guidance Of: Dr. Mohammad Mozumdar Presented By :
Sec-TEEN: Secure Threshold sensitive Energy Efficient sensor Network protocol Ibrahim Alkhori, Tamer Abukhalil & Abdel-shakour A. Abuznied Department of.
Low-Energy Adaptive Clustering Hierarchy An Energy-Efficient Communication Protocol for Wireless Micro-sensor Networks M. Aslam hayat.
An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Network
A Novel Cluster-based Routing Protocol with Extending Lifetime for Wireless Sensor Networks Slides by Alex Papadimitriou.
1 Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks Yingqi Xu, Wang-Chien Lee Proceedings of the 2004 IEEE International.
PEDS September 18, 2006 Power Efficient System for Sensor Networks1 S. Coleri, A. Puri and P. Varaiya UC Berkeley Eighth IEEE International Symposium on.
1 Cross-Layer Scheduling for Power Efficiency in Wireless Sensor Networks Mihail L. Sichitiu Department of Electrical and Computer Engineering North Carolina.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Wireless Sensor Networks 9th Lecture Christian Schindelhauer.
Energy Aware Directed Diffusion for Wireless Sensor Networks Jisul Choe, 2Keecheon Kim Konkuk University, Seoul, Korea
Energy Conservation in wireless sensor networks Kshitij Desai, Mayuresh Randive, Animesh Nandanwar.
Efficient MAC Protocols for Wireless Sensor Networks
Talha Naeem Qureshi Joint work with Tauseef Shah and Nadeem Javaid
1 Energy Efficient Communication in Wireless Sensor Networks Yingyue Xu 8/14/2015.
Presenter: Abhishek Gupta Dept. of Electrical and Computer Engineering
CuMPE : CLUSTER-MANAGEMENT AND POWER EFFICIENT PROTOCOL FOR WIRELESS SENSOR NETWORKS ITRE’05 Information Technology: Research and Education Shen Ben Ho.
TRUST, Spring Conference, April 2-3, 2008 Taking Advantage of Data Correlation to Control the Topology of Wireless Sensor Networks Sergio Bermudez and.
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.
Power Save Mechanisms for Multi-Hop Wireless Networks Matthew J. Miller and Nitin H. Vaidya University of Illinois at Urbana-Champaign BROADNETS October.
UNIVERSITY OF SOUTHERN CALIFORNIA 1 ELECTION: Energy-efficient and Low- latEncy sCheduling Technique for wIreless sensOr Networks S. Begum, S. Wang, B.
1 An Adaptive Energy-Efficient and Low-Latency MAC for Data Gathering in Wireless Sensor Network Gang Lu, Bhaskar Krishnamachari, and Cauligi Raghavendra.
A Power Saving MAC Protocol for Wireless Networks Technical Report July 2002 Eun-Sun Jung Texas A&M University, College Station Nitin H. Vaidya University.
1 Virtual Patrol : A New Power Conservation Design for Surveillance Using Sensor Networks Prasant Mohapatra, Chao Gui Computer Science Dept. Univ. California,
Presenter: Abhishek Gupta Dept. of Electrical and Computer Engineering
An Energy-Aware Periodical Data Gathering Protocol Using Deterministic Clustering in Wireless Sensor Networks (WSN) Mohammad Rajiullah & Shigeru Shimamoto.
Minimizing Energy Consumption in Sensor Networks Using a Wakeup Radio Matthew J. Miller and Nitin H. Vaidya IEEE WCNC March 25, 2004.
An Energy-Efficient Voting-Based Clustering Algorithm for Sensor Networks Min Qin and Roger Zimmermann Computer Science Department, Integrated Media Systems.
An Energy-Efficient MAC Protocol for Wireless Sensor Networks Qingchun Ren and Qilian Liang Department of Electrical Engineering, University of Texas at.
A SURVEY OF MAC PROTOCOLS FOR WIRELESS SENSOR NETWORKS
An Adaptive Energy-Efficient and Low- Latency MAC for Data Gathering in Wireless Sensor Networks Gang Lu, Bhaskar Krishnamachari, and Cauligi S. Raghavendra.
By Naeem Amjad 1.  Challenges  Introduction  Motivation  First Order Radio Model  Proposed Scheme  Simulations And Results  Conclusion 2.
Self Organization and Energy Efficient TDMA MAC Protocol by Wake Up for Wireless Sensor Networks Zhihui Chen and Ashfaq Khokhar ECE Department, University.
Hybrid Indirect Transmissions (HIT) for Data Gathering in Wireless Micro Sensor Networks with Biomedical Applications Jack Culpepper(NASA), Lan Dung, Melody.
An Energy Efficient MAC Protocol for Wireless LANs, E.-S. Jung and N.H. Vaidya, INFOCOM 2002, June 2002 吳豐州.
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.
SEA-MAC: A Simple Energy Aware MAC Protocol for Wireless Sensor Networks for Environmental Monitoring Applications By: Miguel A. Erazo and Yi Qian International.
An Energy-Efficient MAC Protocol for Wireless Sensor Networks Speaker: hsiwei Wei Ye, John Heidemann and Deborah Estrin. IEEE INFOCOM 2002 Page
Energy-Aware Data-Centric Routing in Microsensor Networks Azzedine Boukerche SITE, University of Ottawa, Canada Xiuzhen Cheng, Joseph Linus Dept. of Computer.
Cross-Layer Scheduling for Power Efficiency in Wireless Sensor Networks Mihail L. Sichitiu Department of Electrical and Computer Engineering North Carolina.
Performance Evaluation of IEEE
Link Layer Support for Unified Radio Power Management in Wireless Sensor Networks IPSN 2007 Kevin Klues, Guoliang Xing and Chenyang Lu Database Lab.
Simulation of DeReClus Yingyue Xu September 6, 2003.
CS541 Advanced Networking 1 Contention-based MAC Protocol for Wireless Sensor Networks Neil Tang 4/20/2009.
Energy-Efficient, Application-Aware Medium Access for Sensor Networks Venkatesh Rajenfran, J. J. Garcia-Luna-Aceves, and Katia Obraczka Computer Engineering.
A Bit-Map-Assisted Energy- Efficient MAC Scheme for Wireless Sensor Networks Jing Li and Georgios Y. Lazarou Department of Electrical and Computer 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)
AUTO-ADAPTIVE MAC FOR ENERGY-EFfiCIENT BURST TRANSMISSIONS IN WIRELESS SENSOR NETWORKS Romain Kuntz, Antoine Gallais and Thomas No¨el IEEE WCNC 2011 Speaker.
Oregon Graduate Institute1 Sensor and energy-efficient networking CSE 525: Advanced Networking Computer Science and Engineering Department Winter 2004.
AN ADAPTIVE MAC PROTOCOL FOR WIRELESS SENSOR NETWORKS Wen-Hwa Liao, Hsiao-Hsien Wang, and Wan-Chi Wu PIMRC ’ 07.
Wireless Access and Networking Technology (WANT) Lab. An Efficient Data Aggregation Approach for Large Scale Wireless Sensor Networks Globecom 2010 Lutful.
Energy-Efficient Communication Protocol for Wireless Microsensor Networks by Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan Presented.
AN EFFICIENT TDMA SCHEME WITH DYNAMIC SLOT ASSIGNMENT IN CLUSTERED WIRELESS SENSOR NETWORKS Shafiq U. Hashmi, Jahangir H. Sarker, Hussein T. Mouftah and.
MAC Protocols for Sensor Networks
MAC Protocols for Sensor Networks
Protocols for Wireless Sensor Networks
Threshold sensitive Energy Efficient sensor Network (TEEN)
Wireless Sensor Networks 5. Routing
Energy-Efficient Communication Protocol for Wireless Microsensor Networks by Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan Presented.
Net 435: Wireless sensor network (WSN)
Investigating Mac Power Consumption in Wireless Sensor Network
Presentation transcript:

ELECTIONEL ECTI ON ELECTION: Energy-efficient and Low- latEncy sCheduling Technique for wIreless sensOr Networks Shamim Begum, Shao-Cheng Wang, Bhaskar Krishnamachari, and Ahmed Helmy Department of Electrical Engineering-Systems, University of Southern California IEEE Local Computer Networks (LCN’04)

Outline Introduction Proposed Protocol Simulation Results Conclusion and Future Work

Introduction Research challenge Energy efficiency Energy efficient protocols MAC, topology control, data aggregation, etc Main concern Design of sleep scheduling scheme

Introduction Performance metrics Energy efficiency Latency Responsiveness The difference between reported data value and the data threshold Focuses in different scenarios Normal operation: energy efficiency Abnormalities happed: low latency or high responsiveness

Introduction Motivation Dynamic requirements of different metrics Main idea Spatial-temporal correlation Spatial: At any point of time, all sensors in a small area in the sensor field measure the same phenomenon Temporal: When some abnormal reaction causes the phenomenon, all sensors read this increasing phenomenon and perceive the increase

Protocol --- Network Model and Assumptions Sensor field Reaction area assumption both communication radio and the sensor can be turned off independently to save energy threshold tolerance is specified model

Protocol --- Timing Diagram Phase 0: Synchronization --- using existing synchronization schemes Phase 1: Periodic sleep and monitor Phase 2: CH formation, data aggregation, and report

Protocol --- State Transition Diagram

Protocol --- Phase 2 M A E B D C Z X Y W 42 Initial ( D th ) = 30)

Protocol --- Phase 2 M A E B D C Z X Y W 42 Neighborhood advertisement message exchange

Protocol --- Phase 2 Cluster head election M A E B D C Z X Y W 42

Protocol --- Phase 2 M A E B D C Z X Y W 42 Cluster head advertisement message broadcast

Protocol --- Phase 2 M A E B D C Z X Y W 42 Cluster membership message reply Message from node X has higher signal strength

Protocol --- Phase 2 Cluster formation M A E B D C Z X Y W 42

Protocol --- Phase 2 TDMA schedule creation in cluster heads M A E B D C Z X Y W 42

Protocol --- Phase 2 M A E B D C Z X Y W 42 TDMA schedule announcement

Protocol --- Phase 2 M A E B D C Z X Y W 42 Data aggregation and data transmission ? Does the cluster always directly transmit data packets to its nearby base station ?

Sleep Cycle Adaptation ELECTION vs. other protocols ELECTION turns sensors off during sleep Sleep cycle reduction function F sr is a function of current sleep cycle and gradient of the environment s(t): sleep cycle duration at time t g(t) : gradient at time t s(t+1)=F sr (s(t), g(t))

Exponential F sr Good for latency Aggressive sleep cycle reduction causes small sleep cycle  energy expensive

Geared F sr

Simulation Results --- Compared Approaches TEEN [12] Nodes sleep periodically instead of staying awake During sleep Nodes turn their communication radios off leaving the sensors on Nodes sense the environment continuously and wake up only when the event threshold is detected Hybrid Mix of TEEN and ELECTION Fixed sleep cycle, on-demand cluster formation

Simulation Results --- Parameters and Phenomenon Parameters Phenomena P1: Changes 100 times during the entire simulation P2: Changes 20 times during the entire simulation

Simulation Results --- Remaining Energy (P1) Major energy costs are sensing and cluster formation Save energy of cluster formation, but waste energy for continuous sensing

Simulation Results --- Remaining Energy (P2) Sleep duration become large (Change slower than P1),  significant energy saving Fixed sleep duration  no significant energy saving

Simulation Results --- Number of Alive Nodes

Simulation Results --- Delay Hybrid/TEEN: fixed sleep cycle (delay  25 sec) ELECTION: depends on the sensing phenomenon

Simulation Results --- Responsiveness

Conclusion and Future Work Proposed ELECTION scheme Consider the spatial-temporal correlation of underlying physical phenomenon Three phases Perform well in comparison with TEEN and hybrid protocol Future work Hierarchical organization of cluster heads Load balance