Jayant Gupchup Phoenix, EWSN 2010 Phoenix: An Epidemic Approach to Time Reconstruction Jayant Gupchup †, Douglas Carlson †, Răzvan Musăloiu-E. †,*, Alex.

Slides:



Advertisements
Similar presentations
Ranveer Chandra Ramasubramanian Venugopalan Ken Birman
Advertisements

Christoph Lenzen Philipp Sommer Philipp Sommer Roger Wattenhofer Roger Wattenhofer Optimal Clock Synchronization in Networks.
Is There Light at the Ends of the Tunnel? Wireless Sensor Networks for Adaptive Lighting in Road Tunnels IPSN 2011 Sean.
SoNIC: Classifying Interference in Sensor Networks Frederik Hermans et al. Uppsala University, Sweden IPSN 2013 Presenter: Jeffrey.
1 S4: Small State and Small Stretch Routing for Large Wireless Sensor Networks Yun Mao 2, Feng Wang 1, Lili Qiu 1, Simon S. Lam 1, Jonathan M. Smith 2.
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.
Introduction to Wireless Sensor Networks
1 An Approach to Real-Time Support in Ad Hoc Wireless Networks Mark Gleeson Distributed Systems Group Dept.
Jayant Gupchup Graduate student, Johns Hopkins University Representative Slides.
Ultra-Low Power Time Synchronization Using Passive Radio Receivers Yin Chen † Qiang Wang * Marcus Chang † Andreas Terzis † † Computer Science Department.
Time Synchronization for Wireless Sensor Networks
Probabilistic Aggregation in Distributed Networks Ling Huang, Ben Zhao, Anthony Joseph and John Kubiatowicz {hling, ravenben, adj,
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.
More routing protocols Alec Woo June 18 th, 2002.
Microsoft E-Science Data Storage Model for Environmental Monitoring Wireless Sensor Networks Jayant Gupchup †, R.
1 University of Freiburg Computer Networks and Telematics Prof. Christian Schindelhauer Wireless Sensor Networks 15th Lecture Christian Schindelhauer.
Time Synchronization Murat Demirbas SUNY Buffalo.
LPT for Data Aggregation in Wireless Sensor networks Marc Lee and Vincent W.S Wong Department of Electrical and Computer Engineering, University of British.
1 4 th Workshop COST289 – April 11 th 2007 D-STAR MAC Protocol: a Cross Layer Solution for Wireless Sensor Networks Endowed with Directive Antennas Gianfranco.
Achieving Long-Term Surveillance in VigilNet Pascal A. Vicaire Department of Computer Science University of Virginia Charlottesville, USA.
End-to-End Delay Analysis for Fixed Priority Scheduling in WirelessHART Networks Abusayeed Saifullah, You Xu, Chenyang Lu, Yixin Chen.
FlockLab: A Testbed for Distributed, Synchronized Tracing and Profiling of Wireless Embedded Systems IPSN 2013 NSLab study group 2013/04/08 Presented by:
1 Chalermek Intanagonwiwat (USC/ISI) Ramesh Govindan (USC/ISI) Deborah Estrin (USC/ISI and UCLA) DARPA Sponsored SCADDS project Directed Diffusion
Accuracy-Aware Aquatic Diffusion Process Profiling Using Robotic Sensor Networks Yu Wang, Rui Tan, Guoliang Xing, Jianxun Wang, Xiaobo Tan Michigan State.
CS450 Network Embedded Sensing Systems Week 11: Time Synchronization and Reconstruction Jayant Gupchup.
IN23A-1072: Life Under Your Feet: A Wireless Soil Ecology Sensor Network K. Szlavecz 1, A. Terzis 1, R. Musaloiu 1, A. Szalay 1, J. Gupchup 1, C.-J. Liang.
IDIES Temporal Integrity Challenges in Long-term Environmental Monitoring Sensor Networks. Jayant Gupchup † Alex.
Building and End-to-end System for Long Term Soil Monitoring Katalin Szlávecz, Alex Szalay, Andreas Terzis, Razvan Musaloiu-E., Sam Small, Josh Cogan,
Data Management in Environmental Monitoring Sensor Networks Jayant Gupchup Dissertation Committee: Alex Szalay Andreas Terzis Carey Priebe.
Adaptive Control-Based Clock Synchronization in Wireless Sensor Networks Kasım Sinan YILDIRIM *, Ruggero CARLI +, Luca SCHENATO + * Department of Computer.
Towards a Model-Based Data Collection Framework for Environmental Monitoring Networks Research Proposal Jayant Gupchup Department of Computer Science,
Clock Synchronization in Sensor Networks for Civil Security Farnaz Moradi Asrin Javaheri.
1 EnviroStore: A Cooperative Storage System for Disconnected Operation in Sensor Networks Liqian Luo, Chengdu Huang, Tarek Abdelzaher John Stankovic INFOCOM.
Machine Learning Approach to Report Prioritization with an Application to Travel Time Dissemination Piotr Szczurek Bo Xu Jie Lin Ouri Wolfson.
Microsoft E-Science Data Storage Model for Environmental Monitoring Wireless Sensor Networks Jayant Gupchup †, R.
Growth Codes: Maximizing Sensor Network Data Persistence abhinav Kamra, Vishal Misra, Jon Feldman, Dan Rubenstein Columbia University, Google Inc. (SIGSOMM’06)
College of Engineering Grid-based Coordinated Routing in Wireless Sensor Networks Uttara Sawant Major Advisor : Dr. Robert Akl Department of Computer Science.
1 Clock Synchronization for Wireless Sensor Networks: A Survey Bharath Sundararaman, Ugo Buy, and Ajay D. Kshemkalyani Department of Computer Science University.
A New Hybrid Wireless Sensor Network Localization System Ahmed A. Ahmed, Hongchi Shi, and Yi Shang Department of Computer Science University of Missouri-Columbia.
Gap-filling and Fault-detection for the life under your feet dataset.
A Passive Approach to Sensor Network Localization Rahul Biswas and Sebastian Thrun International Conference on Intelligent Robots and Systems 2004 Presented.
Differential Ad Hoc Positioning Systems Presented By: Ramesh Tumati Feb 18, 2004.
Ad Hoc Positioning System (APS)
Time synchronization for UWSN. Outline Time synchronization knowledge Typical time sync protocol Time sync in UWSN Discussion.
DISSense: An Adaptive Ultralow-power Communication Protocol for Wireless Sensor Networks Ugo Maria Colesanti*, Silvia Santini°, Andrea Vitaletti* * Dipartimento.
APL: Autonomous Passive Localization for Wireless Sensors Deployed in Road Networks IEEE INFOCOM 2008, Phoenix, AZ, USA Jaehoon Jeong, Shuo Guo, Tian He.
EM-MAC: A Dynamic Multichannel Energy-Efficient MAC Protocol for Wireless Sensor Networks Bonhyun Koo Lei Tang*, Yanjun Sun †, Omer Gurewitz.
Computer Science 1 TinySeRSync: Secure and Resilient Time Synchronization in Wireless Sensor Networks Speaker: Sangwon Hyun Acknowledgement: Slides were.
Time This powerpoint presentation has been adapted from: 1) sApr20.ppt.
Networking Algorithms Mani Srivastava UCLA [Project: Dynamic Sensor Nets (ISI-East)]
Low Power, Low Delay: Opportunistic Routing meets Duty Cycling Olaf Landsiedel 1, Euhanna Ghadimi 2, Simon Duquennoy 3, Mikael Johansson 2 1 Chalmers University.
Yu Gu and Tian He Minnesota Embedded Sensor System (MESS) Department of Computer Science & Engineering This work is supported by.
1 Order Reconstruction and Data Integrity Testing of Sensor Network Data Matthias Keller, ETH Zürich MICS Workshop,
Sharp Hybrid Adaptive Routing Protocol for Mobile Ad Hoc Networks
Cross-Layer Scheduling for Power Efficiency in Wireless Sensor Networks Mihail L. Sichitiu Department of Electrical and Computer Engineering North Carolina.
Delivery ratio-maximized wakeup scheduling for ultra-low duty-cycled WSNs under real-time constraints Fei Yang, Isabelle Augé-Blum National Institute of.
Brazil Timestamp Reconstruction. Problem: Reboots Too many Reboots!
Model Based Event Detection in Sensor Networks Jayant Gupchup, Andreas Terzis, Randal Burns, Alex Szalay.
Cross-Layer Scheduling for Power Efficiency in Wireless Sensor Networks Mihail L. Sichitiu Department of Electrical and Computer Engineering North Carolina.
Sniper Detection Using Wireless Sensor Networks
EM-MAC: A Dynamic Multichannel Energy-Efficient MAC Protocol for Wireless Sensor Networks ACM MobiHoc 2011 (Best Paper Award) Lei Tang 1, Yanjun Sun 2,
Experiences and Challenges in Campaign Style Deployments using Wireless Sensor Networks Jayant Gupchup †, Scott Pitz *, Douglas Carlson †, Chih-Han Chang.
Zijian Wang, Eyuphan Bulut, and Boleslaw K. Szymanski Center for Pervasive Computing and Networking and Department of Computer Science Rensselaer Polytechnic.
Cooperative Adaptive Partner Selection for Real-Time Services in IEEE j Multihop Relay Networks Cheng-Kuan Hsieh, Jyh-Cheng Chen, Jeng-Feng Weng.
How Dirty is your Data : The Duality between detecting Events and Faults J. Gupchup A. Terzis R. Burns A. Szalay Department of Computer Science Johns Hopkins.
Towards Optimal Sleep Scheduling in Sensor Networks for Rare-Event Detection Qing Cao, Tarek Abdelzaher, Tian He, John Stankovic Department of Computer.
IHP: Innovation for High Performance Microelectronics
Introduction to Wireless Sensor Networks
Introduction to Wireless Sensor Networks
Presentation transcript:

Jayant Gupchup Phoenix, EWSN 2010 Phoenix: An Epidemic Approach to Time Reconstruction Jayant Gupchup †, Douglas Carlson †, Răzvan Musăloiu-E. †,*, Alex Szalay ±, Andreas Terzis † Department of Computer Science, Johns Hopkins University † Department of Physics and Astronomy, Johns Hopkins University ± Google *

Jayant Gupchup Phoenix, EWSN 2010 where: Environmental Monitoring

Jayant Gupchup Phoenix, EWSN 2010 Design Goals and Targets  Target Lifetime : 1 year o Duty-cycle (~ 5%)  Accuracy Requirements o Milliseconds (ms) - Seconds (s) o Online Synchronization not needed  Delay-tolerant networks o Basestation collects data opportunistically o NOT “sample-and-send”  All measurements require timestamps o Not just events

Jayant Gupchup Phoenix, EWSN 2010 Naïve Time Reconstruction  Measurements are timestamped using motes local clock  Basestation collects data  Time reconstruction algorithm: Assigns measurements a global timestamp

Jayant Gupchup Phoenix, EWSN 2010 Reconstruction is NOT Synchronization  Asynchronous operation o Each mote has its own operation schedule o No attempt to match schedules  Motes o Agnostic of network time / global time o Do not process time information o Do not have an onboard Real-Time Clock (RTC) (E.g. Telos, Mica2, MicaZ, IRIS)

Jayant Gupchup Phoenix, EWSN 2010 Phoenix Performance  Accuracy o Order of seconds, ~ 6 PPM (ignoring temperature effects)  Yield : Fraction of measurements assigned timestamps o ≥ 99%  Overheads: o Duty-Cycle: 0.2% o Space: 4%  Yield performance maintained: o Presence of random, frequent mote reboots o Absence of global clock source for months

Jayant Gupchup Phoenix, EWSN 2010 Background and Related Work

Jayant Gupchup Phoenix, EWSN 2010 Reboots and Basestation ? ? ?

Jayant Gupchup Phoenix, EWSN 2010 Cub Hill – Year long deployment

Jayant Gupchup Phoenix, EWSN 2010 Cub Hill : Time Reconstruction Nodes Stuck (Data Loss) Watchdog Fix Basestation Down Reboot Problems

Jayant Gupchup Phoenix, EWSN 2010 Rate of Reboots

Jayant Gupchup Phoenix, EWSN 2010 Reconstruction Challenges  Motes reboot at random o Downtime is non-deterministic  Dependence on basestation  Temporary network partitions  Mote clock o Varies per mote o Skew changes over time

Jayant Gupchup Phoenix, EWSN 2010 Related Work  Linear Regression for Time Rectification o Fidelity and Yield in a Volcano Monitoring Sensor Network, Werner-Allen et al., OSDI 2006  Reboot Problems o Lessons from the Hogthrob Deployments, Chang et al., WiDeploy 2008 o Trio: Enabling sustainable and scalable outdoor wireless sensor network deployments, Dutta et al., SPOTS 2006  State preservation after reboots o Surviving sensor network software faults, Chen et al., SIGOPS 2009  Data-driven Temporal Integrity o Recovering temporal integrity with data driven time synchronization, Lukac et al., IPSN 2009 o Sundial: Using sunlight to reconstruct global timestamps, Gupchup et al., EWSN 2009

Jayant Gupchup Phoenix, EWSN 2010 Phoenix

Jayant Gupchup Phoenix, EWSN 2010 Big Picture Base Station

Jayant Gupchup Phoenix, EWSN 2010 Terminology  Segment: S tate defined by a monotonically increasing local clock (LC) o Comprises  Anchor: o : Time-references between 2 segments o : Time-references between a segment and global time  Fit : Mapping between one time frame to another o Defined over : Neighbor Fit o Defined over : Global fit  Fit Parameters o Alpha (α) : Skew o Beta (β) : Offset  Goodness of Fit : Metric that estimates the quality of the fit o E.g. : Variance of the residuals

Jayant Gupchup Phoenix, EWSN Phase  Phase-I : Data Collection (In-network)  Phase-II : Timestamp Assignment (Database)

Jayant Gupchup Phoenix, EWSN 2010 Architecture Summary  Motes  Global Clock Source  Basestation

Jayant Gupchup Phoenix, EWSN 2010 Anchor Collection – I : Beaconing Each Mote: Beacons time-state periodically Beacon interval~ 30s Duty-cycle overhead: 0.075%

Jayant Gupchup Phoenix, EWSN 2010 Anchor Collection – II : Storage Each Mote: Stays up (30s) after reboot Listens for announcements Wakes up periodically (~ 6 hrs) Stays up (30s) Listens for announcements Stores anchors Duty-Cycle : 0.14%

Jayant Gupchup Phoenix, EWSN 2010 Anchor Collection – III : Global References G-Mote: Connected to a global clock source Beacon its time-state (30s) Store Global References (6 hrs) Global clock source (GPS, Basestation etc) ,

Jayant Gupchup Phoenix, EWSN (B) 43-5 (B) 97-7 (A) 97-7 (A) 28-4 (G) 28-4 (G) 97-7 (A) 97-7 (A) 43-5 (B) 43-5 (B) 28-4 (G) 28-4 (G) Time Reconstruction (outside the network) χ = 2 χ = 2.5 χ = 7 Segment Graph

Jayant Gupchup Phoenix, EWSN 2010 Evaluation: Simulation & Experiments

Jayant Gupchup Phoenix, EWSN 2010 Evaluation Metrics  Yield: Fraction of samples assigned timestamps (%)  Average PPM Error: PPM Error per measurement:  Duty Cycle Overhead: Fraction of time radio was on (%)  Space Overhead: Fraction of space used to store anchors (%)

Jayant Gupchup Phoenix, EWSN 2010 Simulation: Missing Global Clock Source Simulation Period : 1 Year

Jayant Gupchup Phoenix, EWSN 2010 Simulation: Wake Up Interval Anchor collection rate should be significantly faster than the rate of reboots

Jayant Gupchup Phoenix, EWSN 2010 Simulation: Segments to anchor with

Jayant Gupchup Phoenix, EWSN 2010 Olin Deployment - 19 Motes - 21 Day Deployment - 62 segments - One Global clock mote

Jayant Gupchup Phoenix, EWSN 2010 Deployment Accuracy

Jayant Gupchup Phoenix, EWSN 2010 Naïve Yield Vs Phoenix Yield Phoenix Yield: 99.5%

Jayant Gupchup Phoenix, EWSN 2010 Conclusion  Phoenix timestamps: o > 99% of the collected measurements o With accuracy in order of seconds  Phoenix is Robust to: o Basestation failures for days-months o Random mote reboots  Paying a price of: o 0.2% increase in duty cycle o 4% space overhead

Jayant Gupchup Phoenix, EWSN 2010 Questions ?

Jayant Gupchup Phoenix, EWSN 2010 Extra:

Jayant Gupchup Phoenix, EWSN 2010 Discussion / Future Work  Choosing the right link metric o Factor number of anchor points o Temporal separation of anchors o Combining the metrics along a “fit” path  Adaptive anchor collection o If rate of reboots is unknown  Compare with online timestamping (FTSP)

Jayant Gupchup Phoenix, EWSN 2010 Simulation Parameters ParameterTypeDefault Value Clock SkewUniform Distribution~ U (40 70) [ppm] Segment ModelNon-Parametric (Cub Hill)median : 4 days TopologyCub Hill (53 nodes) Communication Delay (end-to-end) Uniform Distribution~ U (5 15) [ms] Packet Reception RatioLog-Normal Path LossPr(2.0) = η = 2.04 σ = 6.28 Constant Constant NUM_SEGMENTSConstant4 Sampling Frequency (measurements) Constant10 mi

Jayant Gupchup Phoenix, EWSN 2010 Reboots: Long downtimes

Jayant Gupchup Phoenix, EWSN 2010 Clock Skews

Jayant Gupchup Phoenix, EWSN 2010 Temperature dependence Source: