Impact of multi-query optimization in sensor networks

Xiang, Shili; Lim, Hyuk; Tan, Kian-Lee

doi:10.1145/1315903.1315906

Cited by 15 publications

(14 citation statements)

References 14 publications

(9 reference statements)

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“…CEP and data base systems [30,6,26] typically allow reuse by finding completely overlapping sub-sets of operators that process the same input events. However, this is not suitable for overlapping interests in sensor data.…”

Section: Related Workmentioning

confidence: 99%

Recep

Ottenwälder

Koldehofe

Rothermel

et al. 2014

Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

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An appealing use case of complex event processing (CEP) systems is for mobile users to react in real-time to events in their environment, e.g., to the occurrence of a dangerous situation such as an accident. Maintaining mobile CEP systems is highly resource intensive since in many cases events need to be detected in a consumer-centric manner to ensure low latency event detection and high quality of results. In this paper we propose the RECEP system to increase the scalability of mobile CEP systems. In the presence of mobile users with partially overlapping interest, the RECEP system offers methods to efficiently reuse computations and this way reduces the resource requirements of mobile CEP. Since reuse of computations happens with respect to well defined quality metrics, RECEP can be easily tailored to specific mobile applications and maximize the resource savings for their desired quality in terms of precision and recall of the processed events from the user's environment.

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Section: Related Workmentioning

confidence: 99%

Recep

Ottenwälder

Koldehofe

Rothermel

et al. 2014

Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

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“…They are often designed independently of the used physical network layer and geared towards predicting the residual lifetime of the sensor network. The cost model presented in [51] was designed to compute the benefits of a (partial) merge of queries (in context of multi-query optimization for sensor networks). The authors also considered the selectivity of predicates to estimate the power consumption for a single query.…”

Section: Related Workmentioning

confidence: 99%

Stream engines meet wireless sensor networks: cost-based planning and processing of complex queries in AnduIN

Klan

Karnstedt

Hose

et al. 2010

Distrib Parallel Databases

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Wireless sensor networks are powerful, distributed, self-organizing systems used for event and environmental monitoring. In-network query processors like TinyDB offer a user friendly SQL-like application development. Due to the sensor nodes' resource limitations, monolithic approaches often support only a restricted number of operators. For this reason, complex processing is typically outsourced to the base station. Nevertheless, previous work has shown that complete or partial innetwork processing can be more efficient than the base station approach. In this paper, we introduce AnduIN, a system for developing, deploying, and running complex innetwork processing tasks. In particular, we present the query planning and execution strategies used in AnduIN, a system combining sensor-local in-network processing and a data stream engine. Query planning employs a multi-dimensional cost model taking energy consumption into account and decides autonomously which query parts will be processed within the sensor network and which parts will be processed at the central instance.

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“…from the set of queries that are either merged queries or the original queries and merge them first. This strategy is the same as the strategy used by Xiang et al [19] except that they consider only the pairs that satisfy…”

Section: The Algorithmmentioning

confidence: 99%

“…In the second set of experiments, we compare the performance merit of our progressive approach with the iterative approach proposed by Xiang et al [19]. We measure Weighted Sum while varying parameters explained above.…”

Section: Performance Evaluation 51 Experimental Data and Environmentsmentioning

confidence: 99%

“…The method proposed in this paper is based on the technique of systematically controlling the trade-off between the energy cost and the storage cost through controlled merging of queries with similar ranges. There are existing methods proposed to reduce the energy cost by merging queries to avoid duplicate transmission of query results [10,19,20].…”

Section: Introductionmentioning

confidence: 99%

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Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks

Lee

Whang

Lim

et al. 2010

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Recently, as the size of sensor networks increases due to the growth of ubiquitous computing environments and wireless networks, building wireless sensor networks in a hierarchical configuration is put forth as a practical approach. Contrasted with the traditional approach of building networks in a "flat" structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher-capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by Xiang et al. as the basis. In addition, the method considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing "false alarms"). We then present how to build a hierarchical sensor network that is optimal with respect to the weighted sum of the two costs. This allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 1.002 − 3.210 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by Xiang et al. key words: hierarchical sensor network, progressive processing, continuous range queries

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Impact of multi-query optimization in sensor networks

Cited by 15 publications

References 14 publications

Recep

Recep

Stream engines meet wireless sensor networks: cost-based planning and processing of complex queries in AnduIN

Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks

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