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Wireless Sensor Networks In-Network Relational Databases

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  1. Wireless Sensor NetworksIn-Network Relational Databases Jocelyn Botello

  2. Overview • Introduction • Sensor Database System • Projects • TinyDB • Cougar • Maximum Performance • Efficiency • Optimization EEL 6897: Prof. Boloni

  3. Introduction • Minimization Goal: • Network Traffic • Amount of Transmitted Data • Maximization Goal: • Computing Capacity • Power • Acquire Data for Unlimited Time EEL 6897: Prof. Boloni

  4. Sensor Database System • Access data with no previous knowledge • Three-Layer Reference Model • Relational Model • Sensor Data: Time Series • Stored Data: Relations EEL 6897: Prof. Boloni

  5. TinyDB from Berkley • Query Processor • Multiple Query Concurrency • Tree Routing EEL 6897: Prof. Boloni

  6. TinyDB from Berkley • Event- Based Queries • Actuation Queries • Lifetime- Based Queries • Monitoring Queries • Network Health Queries • Exploratory Queries • Aggregation Queries EEL 6897: Prof. Boloni

  7. Cougar from Cornell • Sensors • Abstract Data Type Functions • In-Network Processing • Gateway Node • Query Proxy • Small Database Component EEL 6897: Prof. Boloni

  8. Efficiency • Communication Failure • Reliable Data • Uncertainty of Data • Security of Data • Network’s Power Life EEL 6897: Prof. Boloni

  9. Communication Failure • Sensors Physically Dependable • Outside Factors • Keep Data Alive • Back-Up • Accessibility, Availability EEL 6897: Prof. Boloni

  10. Reliable Data: Uncertainty • Level of Accuracy Vs Cost of Computation • Desired Accuracy • Probabilistic Threshold Query EEL 6897: Prof. Boloni

  11. Reliable Data: Security • Network Specific • Level of Security • Access Points/Rights • Affects of Aggregation • Dynamic • Level of Security Vs Access Time EEL 6897: Prof. Boloni

  12. Optimization • Data Space Management • Queries • Aggregation EEL 6897: Prof. Boloni

  13. Data Space Management • Storage Nodes • Minimize Traffic & Retrieve Time • Switch Roles • Busy Region • Power Life EEL 6897: Prof. Boloni

  14. Queries • Independent, Dynamic • Irrelevant Factors • Power Management • Time Synchronization • Data Processing • Data Collection • Maintaining Power Life • Multiple, Nested Queries EEL 6897: Prof. Boloni

  15. Aggregation • Partial/Total Aggregation • Selective Data • Spatial Aggregation • Spatial Moving Average • Voroni Diagram • Triangular Irregular Network EEL 6897: Prof. Boloni

  16. Conclusion • Maximum Performance • Efficiency • Reliable Data Vs Communication Failure • Optimization • Queries • Aggregation • Minimize Network Traffic • Conservation of Power EEL 6897: Prof. Boloni

  17. Future Work • Power Management • Data Management • Data Collection • Data Processing • Query Processing • Network Design EEL 6897: Prof. Boloni

  18. References [10] S. M. Michael J. Franklin, Joseph M. Hellerstein, “Thinking big about tiny databases,” Bulletin of IEEE Computer Society Technical Committee on Data Engineering, September 2007. [11] Q. Ren and Q. Liang, “Query processing optimization through sample size and monitoring coverage controlling in wireless sensor networks,” IEEE CNF, vol. 3, pp. 830–834, September 2006. [12] Q. Ren and Q. Lian, “A quality-guaranteed and energy-efficient query processing algorithm for sensor networks,” in Proc. IEEE Wireless Communications and Networking Conference 2006 (WCNC2006), April 2006, pp. 47–62. [13] L. Q. Zhuang, J. B. Zhang, D. H. Zhang, and Y. Z. Zhao, “Data management for wireless sensor networks: Research issues and challenges,” in Proc. IEEE 2006 International Conference on Wireless Communication, Networking and Mobile Computing, September 2005, pp. 1–5. [14] G. K. J. B. Jeffrey Considine, Feifei Li, “Approximate aggregation techniques for sensor databases,” in Proc. IEEE 20th International Conference on Data Engineering (ICDE’04), April 2004, pp. 449–460. [15] P. Flajolet and G. N. Martin, “Probablistic counting algorithms for data base applications,” Journal of Computer and System Sciences. [16] M. Sharifzadeh and C. Shababi, “Supporting spatial aggregation in sensor network databases,” in Proc. 12th Annual ACM international workshop on Geographic Information Systems, 2004, pp.166– 175. [1] P. S. Philippe Bonnet, Johannes Gehrke, “Towards sensor database systems,” ACM, vol. 1987, pp. 3–14, 2001. [2] Y. Yao and J. Gehrke, “The cougar approach to in-network query processing in sensor networks,” ACM SIGMOD Record, vol. 31, no. 3, pp. 9–18, September 2002. [3] Q. Luo and H. Wu, “System design issues in sensor databases,” in Proc. ACM SIGMOD International Conference on Management of Data, June 2007, pp. 1182–1185. [4] Zechinelli-Martini, Jose-Luis, and I. Elias-Morales, “Modelling and querying sensor databases,” in Proc. IEEE 8th Mexican International Conference on Current Trends in Computer Science, September 2007, pp. 138–148. [5] S. R. Madden, M. J. Franklin, J. M. Hellerstein, and W. Hong, “Tinydb: An acquisitional query procesing system for sensor networks,” ACM Transactions on Database System, vol. 30, no. 1, pp. 122–173, March 2006. [6] T. Apaydin, S. Vural, and P. Sinha, “On improving data accessibility in storage based sensor networks,” in Proc. IEEE International Conference on Mobile Adhoc and Sensor System(MASS ’07), October 2007, pp. 1–9. [7] R. Cheng and S. Prabhakar, “Managing uncertainty in sensor databases,” SIGMOD Record, vol. 32, no. 4, pp. 41–46, 2003. [8] B. Thuraisingham, “Secure sensor information management and mining,” IEEE Signal Processing Magazine, vol. 3, pp. 14–19, May 2004. [9] R. Tamishetty, L. H. Ngoh, and P. H. Keng, “Query-based wireless sensor storage management for real-time applications,” in Proc. IEEE International Conference on Industrial Informatics 2006, August 2006, pp. 166–170. EEL 6897: Prof. Boloni