Comparative Analysis of Push-Pull Query Strategies for Wireless Sensor Networks
We present a comparative mathematical analysis of two important distinct approaches to hybrid push-pull querying in wireless sensor networks: structured hash-based data-centric storage (DCS) and the unstructured comb-needle (CN) rendezvous mechanism. Our analysis yields several interesting insights. For ALL-type queries pertaining to information about all events corresponding to a given attribute, we examine the conditions under which the two approaches outperform each other in terms of the average query and event rates. For the case of ANY-type queries where it is sufficient to obtain information from any one of the desired events for a given attribute, we propose and analyze a modified sequential comb-needle technique (SCN) to compare with DCS. We find that DCS generally performs better than CN/SCN for high query rates and low event rates, while CN/SCN perform better for high event rates. Surprisingly, for the cases of ALL-type aggregated queries and ANY-type queries, we find that there exist “magic number” event rate thresholds, independent of network size or query probability, which dictate the choice of querying protocol. While our analysis is based on a single-sink square-grid deployment, we believe the insights can be generalized to random deployments.
KeywordsSensor Network Wireless Sensor Network Magic Number Event Information Query Rate
Unable to display preview. Download preview PDF.
- 1.Govindan, R.: Data-Centric Storage in Sensor Networks. In: Znati, T., Sivalingam, K., Raghavendra, C.S. (eds.) Wireless Sensor Networks. Kluwer Publishers, Dordrecht (2003)Google Scholar
- 2.Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks. In: Proceedings of the Sixth Annual International Conference on Mobile Computing and Networks (MobiCOM) (August 2000)Google Scholar
- 3.Madden, S., Franklin, M.J., Hellerstein, J.M., Hong, W.: TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks. In: 5th Symposium on Operating System Design and Implementation (OSDI 2002) (December 2002)Google Scholar
- 4.Braginsky, D., Estrin, D.: Rumor Routing Algorithm For Sensor Networks. In: The First Workshop on Sensor Networks and Applications (WSNA 2002) (October 2002)Google Scholar
- 5.Shenker, S., Ratnasamy, S., Karp, B., Govindan, R., Estrin, D.: Data-Centric Storage in Sensornets. ACM SIGCOMM, Computer Communications Review 33(1) (January 2003)Google Scholar
- 6.Shakkottai, S.: Asymptotics of Query Strategies over a Sensor Network. In: INFOCOM 2004 (March 2004)Google Scholar
- 7.Krishnamachari, B., Heidemann, J.: Application-Specific Modelling of Information Routing in Wireless Sensor Networks. In: Workshop on Multihop Wireless Networks (MWN 2004) held in conjunction with the IEEE International Performance Computing and Communications Conference (IPCCC) (April 2004)Google Scholar
- 8.Trigoni, N., Yao, Y., Demers, A., Gehrke, J., Rajaraman, R.: Hybrid Push-Pull Query Processing for Sensor Networks. In: Proceedings of the Workshop on Sensor Networks as part of the GI-Conference Informatik 2004, Berlin, Germany (September 2004)Google Scholar
- 9.Liu, X., Huang, Q., Zhang, Y.: Combs, Needles, Haystacks: Balancing Push and Pull for Discovery in Large-Scale Sensor Networks. In: ACM Sensys (November 2004)Google Scholar
- 11.Chang, N., Liu, M.: Revisiting the TTL-based Controlled Flooding Search: Optimality and Randomization. In: Proceedings of the Tenth Annual International Conference on Mobile Computing and Networks (ACM MobiCom), Philadelphia, PA (September 2004)Google Scholar
- 12.Krishnamachari, B., Ahn, J.: Optimizing Data Replication for Expanding Ring-based Queries in Wireless Sensor Networks, USC Computer Engineering Technical Report CENG-05-14 (October 2005)Google Scholar