Accurate and Robust Time Reconstruction for Deployed Sensor Networks

The notion of global time is of great importance for many sensor network applications. Time reconstruction methods aim to reconstruct the global time with respect to a reference clock. To achieve microsecond accuracy, MAC-layer timestamping is required for recording packet transmission and reception times. The timestamps, however, can be invalid due to multiple reasons, such as imperfect system designs, wireless corruptions, or timing attacks, etc. In this paper, we propose ART, an accurate and robust time reconstruction approach to detecting invalid timestamps and recovering the needed information. ART is much more accurate and robust than threshold-based approach, especially in dynamic networks with inherently varying propagation delays. We evaluate our approach in both testbed and a real-world deployment. Results show that: 1) ART achieves a high detection accuracy with low false-positive rate and low false-negative rate; 2) ART achieves a high recovery accuracy of less than 2 ms on average, much more accurate than previously reported results.

[1]  Saurabh Ganeriwal,et al.  Timing-sync protocol for sensor networks , 2003, SenSys '03.

[2]  Yunhao Liu,et al.  Exploiting Constructive Interference for Scalable Flooding in Wireless Networks , 2013, IEEE/ACM Transactions on Networking.

[3]  Steve Goddard,et al.  Cross-Layer Analysis of the End-to-End Delay Distribution in Wireless Sensor Networks , 2009, 2009 30th IEEE Real-Time Systems Symposium.

[4]  Shaojie Tang,et al.  Canopy closure estimates with GreenOrbs: sustainable sensing in the forest , 2009, SenSys '09.

[5]  François Ingelrest,et al.  SensorScope: Out-of-the-Box Environmental Monitoring , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[6]  Christoph Lenzen,et al.  PulseSync: An Efficient and Scalable Clock Synchronization Protocol , 2015, IEEE/ACM Transactions on Networking.

[7]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[8]  Lothar Thiele,et al.  Efficient network flooding and time synchronization with Glossy , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[9]  Jan Beutel,et al.  Wireless Sensor Networks in Permafrost Research – Concept, Requirements, Implementation and Challenges , 2008 .

[10]  Yunhao Liu,et al.  Measurement and Analysis on the Packet Delivery Performance in a Large-Scale Sensor Network , 2014, IEEE/ACM Transactions on Networking.

[11]  Alexander S. Szalay,et al.  Phoenix: An Epidemic Approach to Time Reconstruction , 2010, EWSN.

[12]  Amy L. Murphy,et al.  Is there light at the ends of the tunnel? Wireless sensor networks for adaptive lighting in road tunnels , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[13]  Yunhao Liu,et al.  Does Wireless Sensor Network Scale? A Measurement Study on GreenOrbs , 2011, IEEE Transactions on Parallel and Distributed Systems.

[14]  Kang G. Shin,et al.  Attack-Tolerant Time-Synchronization in Wireless Sensor Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[15]  Gyula Simon,et al.  The flooding time synchronization protocol , 2004, SenSys '04.

[16]  Lothar Thiele,et al.  Low-power wireless bus , 2012, SenSys '12.

[17]  Amir Pnueli,et al.  Permutation Graphs and Transitive Graphs , 1972, JACM.

[18]  Alexander S. Szalay,et al.  Sundial: Using Sunlight to Reconstruct Global Timestamps , 2009, EWSN.

[19]  David E. Culler,et al.  Elapsed time on arrival: a simple and versatile primitive for canonical time synchronisation services , 2006, Int. J. Ad Hoc Ubiquitous Comput..

[20]  Deborah Estrin,et al.  Recovering temporal integrity with Data Driven Time Synchronization , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[21]  Yunhao Liu,et al.  On the Delay Performance Analysis in a Large-Scale Wireless Sensor Network , 2012, 2012 IEEE 33rd Real-Time Systems Symposium.

[22]  Philip Levis,et al.  Experiences from a Decade of TinyOS Development , 2012, OSDI.

[23]  Roger Wattenhofer,et al.  Gradient clock synchronization in wireless sensor networks , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[24]  Jiming Chen,et al.  Secure Time Synchronization in WirelessSensor Networks: A MaximumConsensus-Based Approach , 2014, IEEE Transactions on Parallel and Distributed Systems.

[25]  Guoliang Xing,et al.  WizSync: Exploiting Wi-Fi Infrastructure for Clock Synchronization in Wireless Sensor Networks , 2011, 2011 IEEE 32nd Real-Time Systems Symposium.

[26]  Lothar Thiele,et al.  Reconstruction of the correct temporal order of sensor network data , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[27]  Matt Welsh,et al.  Fidelity and yield in a volcano monitoring sensor network , 2006, OSDI '06.

[28]  Xenofon D. Koutsoukos,et al.  Time Synchronization in Heterogeneous Sensor Networks , 2008, DCOSS.

[29]  Philip Levis,et al.  Collection tree protocol , 2009, SenSys '09.

[30]  Mani B. Srivastava,et al.  A case against routing-integrated time synchronization , 2010, SenSys '10.

[31]  Lothar Thiele,et al.  How was your journey?: uncovering routing dynamics in deployed sensor networks with multi-hop network tomography , 2012, SenSys '12.