skip to main content
research-article

Distributed Data-Centric Adaptive Sampling for Cyber-Physical Systems

Published:14 January 2015Publication History
Skip Abstract Section

Abstract

A data-centric joint adaptive sampling and sleep scheduling solution, SILENCE, for autonomic sensor-based systems that monitor and reconstruct physical or environmental phenomena is proposed. Adaptive sampling and sleep scheduling can help realize the much needed resource efficiency by minimizing the communication and processing overhead in densely deployed autonomic sensor-based systems. The proposed solution exploits the spatiotemporal correlation in sensed data and eliminates redundancy in transmitted data through selective representation without compromising on accuracy of reconstruction of the monitored phenomenon at a remote monitor node. Differently from existing adaptive sampling solutions, SILENCE employs temporal causality analysis to not only track the variation in the underlying phenomenon but also its cause and direction of propagation in the field. The causality analysis and the same correlations are then leveraged for adaptive sleep scheduling aimed at saving energy in wireless sensor networks (WSNs). SILENCE outperforms traditional adaptive sampling solutions as well as the recently proposed compressive sampling techniques. Real experiments were performed on a WSN testbed monitoring temperature and humidity distribution in a rack of servers, and the simulations were performed on TOSSIM, the TinyOS simulator.

References

  1. Z. Abbasi, G. Varsamopoulos, and S. K. S. Gupta. 2010. Thermal aware server provisioning and workload distribution for Internet data centers. In Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (HPDC’10). 130--131. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. C. Aggarwal, A. Bar-Noy, and S. Shamoun. 2011. On sensor selection in linked information networks. In Proceedings of the 2011 International Conference on Distributed Computing in Sensor Systems (DCOSS’11). 1--8.Google ScholarGoogle Scholar
  3. H. Akaike. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 6, 716--723.Google ScholarGoogle ScholarCross RefCross Ref
  4. S. Bandyopadhyay and E. Coyle. 2003. An energy-efficient hierarchical clustering algorithm for wireless sensor networks. In Proceedings of the 22nd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’03). 1713--1723.Google ScholarGoogle Scholar
  5. S. Bandyopadhyay and E. J. Coyle. 2004. Minimizing communication costs in hierarchically clustered networks of wireless sensors. Computer Networks 44, 1, 1--16. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. Banerjee, T. Mukherjee, G. Varsamopoulos, and S. K. S. Gupta. 2010. Cooling-aware and thermal-aware workload placement for green HPC data centers. In Proceedings of the 2010 International Green Computing Conference (IGCC’10). 245--256. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Bhardwaj and A. P. Chandrakasan. 2002. Bounding the lifetime of sensor networks via optimal role assignments. In Proceedings of the 21st Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM’02). 1587--1596.Google ScholarGoogle Scholar
  8. S. Chachra and M. Marefat. 2006. Distributed algorithms for sleep scheduling in wireless sensor networks. In Proceedings of the 2006 IEEE International Conference on Robotics and Automation (ICRA’06). 3101--3107.Google ScholarGoogle Scholar
  9. Z. Chen, S. Yang, L. Li, and Z. Xie. 2010. A clustering approximation mechanism based on data spatial correlation in wireless sensor networks. In Proceedings of the Wireless Telecommunications Symposium (WTS’10). 1--7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. T. Cui, L. Chen, T. Ho, S. H. Low, and L. L. H. Andrew. 2007. Opportunistic source coding for data gathering in wireless sensor networks. In Proceedings of the International Conference on Mobile Adhoc and Sensor Systems (MASS’07). 1--11.Google ScholarGoogle Scholar
  11. D. L. Donoho. 2006. Compressed sensing. IEEE Transactions on Information Theory 52, 4, 1289--1306. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. J. Geweke. 1982. Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77, 378, 304--313.Google ScholarGoogle Scholar
  13. R. W. Ha, P. Ho, X. S. Shen, and J. Zhang. 2006. Sleep scheduling for wireless sensor networks via network flow model. Computer Communications 29, 13--14, 2469--2481. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. J. Haupt, W. U. Bajwa, M. Rabbat, and R. Nowak. 2010. Compressed sensing and network monitoring. Next Wave 18, 3, 16--25.Google ScholarGoogle Scholar
  15. W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan. 2000. Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Science (HICSS’00). 8020. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. T. Huang, N. Kandasamy, and H. Sethu. 2012. Evaluating compressive sampling strategies for performance monitoring of data centers. In Proceedings of the 2012 IEEE Network Operations and Management Symposium (NOMS’12). 655--658.Google ScholarGoogle Scholar
  17. H. Jiang, S. Jin, and C. Wang. 2011. Prediction or not? An energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems 22, 6, 1064--1071. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. A. Keshavarzian, H. Lee, and L. Venkatraman. 2006. Wakeup scheduling in wireless sensor networks. In Proceedings of the 7th International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc’06). 322--333. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. B. Krishnamachari, D. Estrin, and S. Wicker. 2002. The impact of data aggregation in wireless sensor networks. In Proceedings of the 22nd International Conference on Distributed Computing Systems. 575--578. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. Kusuma, L. Doherty, and K. Ramchandran. 2001. Distributed compression for sensor networks. In Proceedings of the 2001 International Conference on Image Processing (ICIP’01). 82--85.Google ScholarGoogle Scholar
  21. E. K. Lee, I. Kulkarni, D. Pompili, and M. Parashar. 2012a. Proactive thermal management in green datacenter. Journal of Supercomputing 60, 2, 165--195. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. E. K. Lee, H. Viswanathan, and D. Pompili. 2011. SILENCE: Distributed adaptive sampling for sensor-based autonomic systems. In Proceedings of the 8th ACM International Conference on Autonomic Computing (ICAC’11). 61--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. E. K. Lee, H. Viswanathan, and D. Pompili. 2012b. VMAP: Proactive thermal-aware virtual machine allocation in HPC cloud datacenters. In Proceedings of the 19th International Conference on High Performance Computing (HiPC’12). 1--10.Google ScholarGoogle Scholar
  24. X. Li, X. Xu, S. Wang, S. Tang, G. Dai, J. Zhao, and Y. Qi. 2009. Efficient data aggregation in multi-hop wireless sensor networks under physical interference model. In Proceedings of the IEEE 6th International Conference on Mobile Adhoc and Sensor Systems (MASS’09). 353--362.Google ScholarGoogle Scholar
  25. C. Liu, K. Wu, and J. Pei. 2007. An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Transactions on Parallel Distributed Systems 18, 7, 1010--1023. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. T. Melodia, D. Pompili, and I. F. Akyildiz. 2006. A communication architecture for mobile wireless sensor and actor networks. In Proceedings of the 2006 3rd Annual IEEE Conference on Sensor and Ad Hoc Communications and Networks (SECON’06). 109--118.Google ScholarGoogle Scholar
  27. V. Mhatre and C. Rosenberg. 2004. Design guidelines for wireless sensor networks: Communication, clustering, and aggregation. Ad Hoc Networks Journal 2, 1, 45--63.Google ScholarGoogle ScholarCross RefCross Ref
  28. J. Moore, J. S. Chase, and P. Ranganathan. 2006. Weatherman: Automated, online and predictive thermal mapping and management for data centers. In Proceedings of the IEEE International Conference on Autonomic Computing (ICAC’06). 155--164. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. P. Ogren, E. Fiorelli, and N. E. Leonard. 2004. Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment. IEEE Transactions on Automatic Control 49, 8, 1292--1302.Google ScholarGoogle ScholarCross RefCross Ref
  30. S. S. Pradhan, J. Kusuma, and K. Ramchandran. 2002. Distributed compression in a dense microsensor network. IEEE Signal Processing Magazine 19, 2, 51--60.Google ScholarGoogle ScholarCross RefCross Ref
  31. S. S. Pradhan and K. Ramchandran. 2000. Distributed source coding: Symmetric rates and applications to sensor network. In Proceedings of the Data Compression Conference (DCC’00). 363--372. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. G. Quer, R. Masiero, D. Munaretto, M. Rossi, J. Widmer, and M. Zorzi. 2009. On the interplay between routing and signal representation for compressive sensing in wireless sensor networks. In Proceedings of the Information Theory and Applications Workshop (ITA’09). 206--215.Google ScholarGoogle Scholar
  33. A. Scaglione. 2003. Routing and data compression in sensor networks: Stochastic models for sensor data that guarantee scalability. In Proceedings of the IEEE International Symposium on Information Theory (ISIT’03).Google ScholarGoogle ScholarCross RefCross Ref
  34. A. Scaglione and S. D. Servetto. 2002. On the interdependence of routing and data compression in multi-hop sensor networks. In Proceedings of the 8th Annual International Conference on Mobile Computing and Networking (MobiCom’02). 140--147. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. G. Schwarz. 1978. Estimating the dimension of a model. Annals of Statistics 6, 2, 461--464.Google ScholarGoogle ScholarCross RefCross Ref
  36. A. K. Seth. 2010. A MATLAB toolbox for Granger causal connectivity analysis. Journal of Neuroscience Methods 186, 2, 22--26.Google ScholarGoogle ScholarCross RefCross Ref
  37. K. Sohrabi, J. Gao, V. Ailawadhi, and G. J. Pottie. 2000. Protocols for self-organization of a wireless sensor network. IEEE Personal Communications 7, 1, 16--27.Google ScholarGoogle ScholarCross RefCross Ref
  38. J. A. Stankovic, T. F. Abdelzaher, C. Lu, L. Sha, and J. Hou. 2003. Real-time communication and coordination in embedded sensor networks. Proceedings of the IEEE 91, 7, 1002--1022.Google ScholarGoogle ScholarCross RefCross Ref
  39. M. C. Vuran, O. B. Akan, and I. F. Akyildiz. 2004. Spatio-temporal correlation: Theory and applications for wireless sensor networks. Computer Networks 45, 3, 245--259. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. R. Willett, A. Martin, and R. Nowak. 2004. Backcasting: Adaptive sampling for sensor networks. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN’04). 124--133. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. X. Xu, Y. Hu, W. Liu, and J. Bi. 2008. Data-coverage sleep scheduling in wireless sensor networks. In Proceedings of the 7th International Conference on Grid and Cooperative Computing (GCC’08). 342--348. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. S. Yoon and C. Shahabi. 2007. The clustered aggregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks. ACM Transactions on Sensor Networks 3, 1, Article No. 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. O. Younis and S. Fahmy. 2004. Distributed clustering in ad-hoc sensor networks: A hybrid, energy-efficient approach. In Proceedings of the 23rd Conference of the IEEE Communications Society (INFOCOM’04).Google ScholarGoogle Scholar
  44. M. R. Zoghi and M. H. Kahaei. 2009. Efficient sensor selection based on spatial correlation in wireless sensor networks. In Proceedings of the 14th International CSI Computer Conference (CSICC’09). 627--632.Google ScholarGoogle Scholar
  45. M. Zuniga. 2010. Building a Network Topology for TOSSIM. Retrieved October 30, 2014, from http://www.tinyos.net/tinyos-2.x/doc/html/tutorial/usc-topologies.html.Google ScholarGoogle Scholar

Index Terms

  1. Distributed Data-Centric Adaptive Sampling for Cyber-Physical Systems

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader
          About Cookies On This Site

          We use cookies to ensure that we give you the best experience on our website.

          Learn more

          Got it!