smartcq: Answering and Evaluating Bounded Continuous Search Queries within the WWW and Sensor Networks

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1 smartcq: Answering and Evaluating Bounded Continuous Search Queries within the WWW and Sensor Networks Nils Hoeller, Christoph Reinke, Dirk Kukulenz, Volker Linnemann Institute of Information Systems University of Luebeck Ratzeburger Allee 160, Luebeck, Germany {hoeller, reinke, kukulenz, Abstract Continuous Queries (CQ) can be used to keep track of relevant information in the World Wide Web and Sensor Networks over a period of time. However, result sets may become unbounded and notifications can be delayed. A special form of CQ are Bounded Continuous Search Queries (BCSQ), where results are processed immediately and a bounding condition for the number of user notifications may be defined to limit the result set. Based on a theoretical background for answering BCSQ we present smartcq, a web application for processing BCSQ and evaluating the query results. Based on a transparent search engine a user may execute and evaluate new strategies and methods for continuous search queries. Furthermore because of the positive results of BCSQ we encourage its use for querying Wireless Sensor Networks to reduce the communication demand and hence reduce the energy consumption to enhance the lifetime of a sensor network. 1. Introduction Searching the World Wide Web may be tedious if a search has to be repeated frequently. A repeated search execution is necessary in particular if it is not known when and how long information will be available on the Web. A periodical search has to be done in order not to miss relevant information. Practical solutions are given by alert services, as e.g. Google alerts or Microsoft Live alerts, that repeat a user query automatically and present search results on a regular basis. These continuous query systems help a user to scan the Web periodically but still have disadvantages. First, alert notifications may become 'unbounded', if the number of notifications becomes too high for a user to process the information. The user still has to search within the notification results, which might take the same effort as a regular web search. An optimal continuous query system would therefore allow the user to determine the number of processed results within the period of the search. Second, by using common alert services, notifications about new web information will be presented every determined period ('in-batch'). By the time a user gets a notification, the web information may not be relevant anymore, as e.g. a notification about a good product offer that has already been sold. An optimal continuous query system would therefore notify the user immediately after new web information is retrieved. As a result, Bounded Continuous Search Queries (BCSQ) [6] have been introduced to realize optimal continuous search queries. This includes immediate information processing and the usage of a bounding condition for user notifications. While previous work concentrated on methods for optimizing the results of BCSQ [5, 6], in the first part of this paper we present an architecture for processing BCSQ. Furthermore we suggest an evaluation system, that lets researchers and developers evaluate new BCSQ approaches. The architecture has been implemented and named smartcq [3]. Another important kind of information networks are wireless sensor networks that consist of hundreds or thousands of tiny microcontrollers with sensors to monitor and analyze the environment. Recent sensor deployments have been reported [12] each including the extensive use of continuous queries to extract sensor data continuously. A special kind of sensor networks like the ALERT deployment [1] require immediate (real-time) notification to recognize emergency situations. To fulfill this task, existing sensor network query engines like TinyDB [9] or Cougar [11] require sending each new sensor data to a central point immediately, as e.g. a gateway, where sensor data is evaluated and alerts may eventually be

2 triggered. While supporting real-time notifications, this message driven approach is very energy consuming. In the second part of the paper we show that BCSQ can be an important form of queries for wireless sensor networks, that support alert and event triggered queries. By reducing the number of results and hence messages they can optimize the energy consumption of the network while still allowing immediate notification in case of important sensor values. That makes BCSQ applicable to event and alert based queries within wireless sensor networks. In summary, our contributions in this paper are: In Section 2, we introduce the BCSQ web application smartcq and its architecture. We explain how BCSQ in the WWW can be processed. In Section 3, we motivate the usage of BCSQ within wireless sensor networks to enhance the network lifetime. We give an overview of related work in the area of BCSQ, continuous queries in the WWW and query processing in wireless sensor networks in Section 4. In Section 5, we conclude our paper and present future work directions. 2. smartcq System Design In this section we outline the architecture of the BCSQ system smartcq and the interfaces for the usage and evaluation of BCSQ. 2.1 Architecture Existing search engines in the WWW do not support Bounded continuous search queries (BCSQ). BCSQ require additional system functionalities compared to regular search engines, as e.g. a profile management and a decision logic. We have developed and implemented a new web application smartcq that integrates these further functionalities. In Figure 1 the system architecture of smartcq is shown, including three parts: (1) a user interface, (2) the BCSQ services and (3) a regular web search engine. A main development goal for smartcq and BCSQ systems in general is transparency. Therefore, the system's web search engine is based on Nutch[2], a transparent open source web search framework. The main contributions of smartcq are the BCSQ services and the user and evaluation interface, which will be described in the next subsection. The BCSQ services include profile management, evaluation methods and the BCSQ logic, which decides whether the user has to be notified about a search result or not. Figure 1. smartcq system architecture BCSQ profiles contain a query string, a time period, a bounding condition for user notifications, the starting time and a basket (set) of websites that should be considered. smartcq extends this query profile by letting the user select a method for answering BCSQ. There are two kinds of methods available. The first method is the kssp approach [6], which is based on the famous secretary selection problem and works without any knowledge of the distribution of ranking scores. The second kind of methods are based on the estimation of a ranking score distribution to find an optimized filtering threshold [5]. Once there are active BCSQ profiles in the system, the BCSQ logic applies the query profiles to the search engine results every time the index has been updated and new web information has arrived. Any query hit will be passed to the decision logic which applies the BCSQ answering method that is determined by the BCSQ profile. A positive decision results in the acceptance of the web document and a user notification. 2.2 The User and Evaluation Interface SmartCQ allows the user to issue BCSQ over the web interface. Notifications about relevant documents will be delivered to him over the web interface or by . Once the BCSQ duration is over, a summary report is presented to the user. Figure 2 shows a typical summary report, which increases the transparency of the system. The summary report shows the scores of relevant documents on the query +olympic +games

3 over 45 days, where the 7 best documents are searched. The actual best documents with the highest ranking score are marked and compared to the documents selected by smartcq. In this example 5 of the actual 7 best documents have been found by the kssp approach [6]. Furthermore, evaluation scores for the query results and the applied selection method are shown. and reaches a maximum if about 3% of the incoming data are used for distribution learning. Figure 3. smartcq BestRatio method evaluation 3. BCSQ in Wireless Sensor Networks Figure 2. BCSQ summary report Another goal for the smartcq system is to support further research on methods for answering BCSQ. For this purpose smartcq can be used in a separate evaluation mode. In this mode BCSQ are applied to a previously built web archive including daily web site versions over 13 months. This batch processing mode can be applied to test the performance of new filtering methods. Besides, XML may be used to create a large set of BCSQ efficiently. The system provides a statistical evaluation in order to quantify the performance of new BCSQ strategies with respect to the evaluation measures in [5]. In addition, we apply a Best Ratio measure, which is defined as the percentage of the actual k best documents (kbest) in the selected document set (A). Figure 3 shows the evaluation results for previously presented BSCQ strategies based on the Best Ratio measure. The x-axis marks different selection strategies as e.g. a secretary strategy ('kssp', [6]) and distribution based strategies ('static' and percentage values, [5]). The percentage values denote the percentage of data used for distribution learning [5]. As in [5] in the example in Figure 3 the distribution optimized BCSQ strategy performs better than the kssp approach by selecting a higher percentage of actual best documents The previous sections discussed BCSQ within the WWW. smartcq has been introduced as a transparent BCSQ architecture and web application for evaluating BCSQ over the WWW. In this paper we emphasize the importance of BCSQ within the field of other information systems like wireless sensor networks, that is motivated by the previous results in Section 2.2. In wireless sensor network deployments the user is often interested in continuous information, e.g. measured data, over a long period. Therefore, in [9, 11] queries are mostly continuous. For real-time sensor network deployments like the ALERT deployment [1] and other forecast scenarios it is especially important that measured data is checked immediately to trigger events in case of an emergency and sensitive situations repectively. An approach to support alert and event sensor networks is an external storage or so called dumb data collection sensor network [10]. By using an existing sensor network query engine each measured value is simply transmitted to the base station and evaluated immediately. Although this could be a reliable approach, its main disadvantage is the potentially high energy consumption when data is sampled with a high frequency or resolution. Hence, we argue that data should be filtered within the sensor network. BCSQ are a new means towards energy efficient evaluation within wireless sensor networks especially for alert and event based queries. Analogous to the requirements of an optimal continuous query system in the WWW, alert and event based queries in wireless sensor networks have two basic requirements: 1. A notification about important sensor data has to be immediate (real-time notification) 2. The limited energy capacity of wireless sensor networks requires only to send messages if

4 something important happens, e.g. the best sensor value to a given criteria. While the first requirement is the same for either BCSQ in the WWW as in sensor networks, the second requirement has also similarities to the search in the WWW. While we first stated that a result set should not become unbounded to avoid user message filtering, in sensor networks we want to avoid sending unimportant messages to save energy and hence extend the networks lifetime. To point out the possibilities of BCSQ in wireless sensor networks we will refer to the following example query during this section: forcon trigger=1timeunit start=now end=100 estimated greatest 1 in //sensors/temp This example query, that is given in a BCSQ extension of the XPath [13] query language with forcon signalizing new BCSQ, has the purpose to extract the maximum temperature. It has a duration of 100 timeunits (i.e. hours), which correspond to 100 temperature sensor values. For simplification we only want to find the maximum temperature in this period. Besides, it is important that sensor nodes process any measured data immediately. An extension to find the greatest k>1 temperature values immediately has been discussed in our previous work [5, 6]. As described before, the standard approach to evaluate this query can be to send each temperature value to the base station to calculate the maximum temperature after 100 timeunits (retrospective analysis). Furthermore we can estimate the maximum temperature randomly before. This can even be done on the sensor node. We will refer to this approach as random. In Section 2.2 we showed that our BCSQ strategies are able to find a high percentage of maximum ranking scores, which motivates its usage in a sensor network scenario where the task is to find the maximum temperature. While the kssp approach [6] works without any further knowledge of a distribution of the sensor values, the distribution based methods [5] require this knowledge. However in typical scenarios like [12] historical data is often present (static distribution) or the query duration is long and the distribution learning strategy (dynamic distribution) is applicable. To give an overview on how we can benefit from the smartcq results we evaluated the BSCQ strategies for the given example query with respect to a representative sensor node platform pacemate, that is used in our institute. The pacemate sensor node platform [8] is based on a Phillips LPC 2136 processor with a 868 MHz DP1205 radio transceiver. Based on the previous BestRatio results we first calculated how many messages are required to reach an expectancy value of 1 for the probability of finding the maximum temperature value. In Figure 4 the results of this evaluation are shown. E-Value_Max 1,2 1 0,8 0,6 0,4 0,2 kssp random distribution Messages Figure 4. BCSQ for Sensor Network Results In summary, the distribution based approaches [5] require sending two messages (x-axis) to reach the demanded expectancy value (y-axis). The kssp method [6] that requires no further knowledge about the distribution of the sensor values requires three messages. The standard random approach does not reach the demanded expectancy value until all 100 messages have been send and a retrospective analysis can be done. These results influence the lifetime of the wireless sensor network, since sending messages is the most energy consuming task. We evaluated the impact on a typical deployment scenario with a diameter of 100 sensor nodes and the pacemate sensor node technical specifications. The goal was to find the expected network lifetime for a varying demanded expectancy value. For the battery capacity of 2500mAh, a power consumption of 62mA at maximum transmission power, a data rate up to 152.3kbit/s and a message size of 100byte we get the results shown in Figure 5. While the random selection strategy has a very high energy demand and hence lowers the expected lifetime of the sensor network significantly, we can summarize that the BCSQ methods [5, 6] perform much better. Accordingly, the work on smartcq and BCSQ can have innovative benefits on interdiciplinary research areas like wireless sensor networks. 4. Related Work Continuous web search systems and architectures have been introduced by the main web search vendors

5 (e.g. Google and MSN). Unlike these systems, our approach limits the entire result set of a continuous web J kssp random distribution 0 0,01 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 Expectancy Value Figure 5. Sensor Network Energy Demand search by letting the user determine a number of allowed notifications he is willing to accept. Therefore bounded continuous search queries demand further filtering and decision logic compared to previous approaches. Bounded Continuous Search Query Engines implement the publish-subscribe paradigm. Many existing publish-subscribe systems have been introduced [4] but do not address filtering documents by bounding conditions for user notifications. Two well-known query approaches have been introduced for wireless sensor networks. While TinyDB [9] and Cougar[11] support regular continuous queries and focus on the aggregation of preliminary results to save energy, with smartcq we motivate the evaluation of sensor values deep in the sensor network to limit the communication demand and hence extend the lifetime of the network. 5. Conclusion & Future Work We presented a web application for issuing BCSQ. The architecture allows the evaluation of new methods to answer BCSQ. While providing a helpful search tool for regular users, developers can benefit from smartcq to improve further strategies for BCSQ. In Section 3 we showed how the work on BCSQ can influence and optimize interdisciplinary research areas like wireless sensor networks. Based on the motivating evaluation results from smartcq we discussed the usage of BCSQ within wireless sensor networks to optimize communication demand, which extends the expected lifetime of a wireless sensor network. Future work on the smartcq system is concentrated on building an interface to directly insert new BCSQ answering methods to the system, defined by the user, in order to evaluate them. This will help to find new strategies for answering BCSQ. Using BCSQ within wireless sensor networks can be simplified by using a query management system like smartcq. Based on our previous work on integrating wireless sensor networks in the WWW [7], we will extend smartcq to allow posing BCSQ to wireless sensor networks directly through its web interface. 10. References [1] [2] [3] The smartcq project [4] P. T. Eugster, P. A. Felber, R. Guerraoui and A. Kermarrec, The many faces of publish/subscribe. ACM Comput. Surv. 35, 2 (Jun. 2003), [5] D. Kukulenz, N. Hoeller, S. Groppe, and V. Linnemann, Optimization of Bounded Continuous Search Queries based on Ranking Distributions. In Proc. of WISE 2007, Nancy, France, pages Springer, [6] D. Kukulenz and A.. Ntoulas Answering Bounded Continuous Search Queries In The World Wide Web. In Proc. Of WWW-07,WorldWideWeb Conf., ACM Press, Banff, Canada, [7] N. Hoeller, C. Reinke, S. Groppe and V. Linnemann Xobe Sensor Networks: Integrating XML in Sensor Network Programming In Proc. of INSS 2008, IEEE, Kanazawa, Japan, 2008 [8] M. Lipphardt, H. Hellbrück, D. Pfisterer, S. Ransom and S. Fischer: Practical experiences on mobile inter-body-areanetworking. In Proceedings of BodyNets 07, 2007 [9] S. R. Madden, M. J. Franklin, J. M. Hellerstein and W. Hong, TinyDB: an acquisitional query processing system for sensor networks. ACM Trans. Database Syst. 30, 1 (Mar. 2005), [10] J. Paradiso, G. Borriello and G.-C. Roman, Applications: Beyond dumb data collection (panel). EmsNets, 2006 [11] Y. Yao and J. Gehrke The cougar approach to innetwork query processing in sensor networks. SIGMOD Rec. 31, 3 (Sep. 2002), [12] Ning Xu, A Survey of Sensor Network Applications, University of Southern California, [13] XML Path Language (XPath) 2.0 Recommendation, W3C,