1. Top-k Queries in Wireless Sensor Networks Amber Faucett, afaucett@islander.tamucc.edu Dr. Longzhuang Li, Longzhuang.Li@tamucc.edu In today’s world, wireless sensor networks are being used to monitor everything from the environment to industrial areas. Monitoring top-k queries is an important aspect needed for wireless sensor networks especially with environmental monitoring. From top-k queries, information can be gathered about the highest or lowest readings within the sensor network and from which sensors this data is being logged. By observing the positive effects that this type of software could bring, we have decided to look further into finding the most efficient algorithms to monitor top-k queries. Because sensor nodes are limited by a small battery life, battery consumption must be considered when designing the query algorithm(s) to identify the top-k readings. By thoroughly researching currently proposed solutions for top-k queries, we will compare the strengths and weaknesses and hopefully find what is currently the best algorithm. For future plans, we will look into constructing a simulation of a wireless sensor network in order to observe and compare the numerous top-k monitoring algorithms against one another as well as our own proposal. The end goal is to create energy efficient algorithm to monitor top-k queries in wireless sensor networks that outperforms any currently proposed solution. In order to test the algorithms, we will be using TinyOS which is an open-source operating system that can be used to test networking devices specifically those that require very low power usage. On top of the TinyOS, we will be using TOSSIM which is a TinyOS simulator that compiles directly from TinyOS code. TOSSIM provides the same tools as would be provided in real-world networking environment. Along with TOSSIM, we will be looking at using TinyViz which is a GUI tool of TOSSIM. TinyViz allows you to interact with a running simulation of a wireless sensor network. With TinyViz, we will be able to visually observe the execution of the TinyOS applications. Abstract References Approach Acknowledgments [1] D. Zeinalipour-Yazti, Z. Vagena, D. Gunopulos and V. Kalogeraki, V. Tsotras, M. Vlachos, N. Koudas, D. Srivastava, “Finding the K Highest- Ranked Answers in a Distributed Network”, Computer Networks, 25 June 2009. [2] M. Wu, J. Xu, X. Tang, and W.C. Lee, “Top-k Monitoring in Wireless Sensor Networks,” IEEE Trans Knowledge and Data Eng., July 2007. [3]P. Andeou, D. Zeinalipour-Yazti, M. Vassiliadou, P.K. Chrysanthis, G. Samaras, “KSpot: Effectively Monitoring the K Most Important Events in a Wireless Sensor Network”, In Proceedings of the 25 th International Conference on Data Engineering (ICDE), April 2009. [4]S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong, “TAG: A Tiny Aggregation Service for Ad Hoc Sensor Networks,” Proc. Usenix Fifth Symp. Operating Systems Design and Implementations, Dec. 2002. Presentation of this poster was supported in part by NSF Grant CNS-0837556. I would also like to acknowledge the National Science Foundation - Louis Stokes Alliance for Minority Participation (grant #0703290) for its support. Thanks also to Dr. Li for his help and guidance through this research. Background Future Work Once familiar with TinyOS and TOSSIM, the plan is to begin plugging in the discussed algorithms and viewing their capabilities. From here, we will choose one of the tested algorithms and look at ways of improving on its weaknesses. Eventually, we would like to get to the point of building our own algorithm that will outperform any currently proposed algorithms for top-k queries. With wireless sensor networks, sometimes it is desired to find the k objects with the highest/lowest overall values. Algorithms that attempt to solve this request have become known as top-k queries. In order for an algorithm to be considered successful, it must have low latency and low battery consumption. There are already several algorithms out that have been proposed and tested throughout the world. We will review over two examples to give a better explanation of what algorithms consist of for top-k query monitoring. The first algorithm is what is known as the Tiny AGgregation or TAG approach. In the TAG approach, the sensors use a routing tree rooted at the base station to transfer the data back to the requesting user. As the data flows back up through the sensors, it is collected and divided along the way to the base station according to the specifications of the query. A visual representation of the TAG approach can be seen in Fig a. At each sampling instance, each node sends its current reading to the next node which then compares the received data to its own. With the given example, the TAG approach takes a total of nine messages to find the top-k result. A second approach to top-k query monitoring is called FILA, which is a filter-based monitoring approach. Each node is given a filter range so that only data that is above (or below) that range is returned to the base station. The base station also keeps a copy of the filters placed on all of the nodes. The purpose of applying a filter to the sensors is to reduce the amount of unnecessary traffic within the network. A visual representation of FILA can be seen in Fig b. With this example, FILA takes a total of four messages to return the top-k result to the base station.