A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks

Silberstein, Adam; Braynard, R.; Ellis, Carla Schlatter; Munagala, Kamesh; Yang, Jun

doi:10.1109/icde.2006.10

Cited by 141 publications

(114 citation statements)

References 14 publications

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“…Energy efficiency for sensor networks has been an intense area of research in recent years [7], [8], [9], [10], [5], [11], [12], [13], [14], [15], [16], [17], [18]. Among these techniques, various data collection approaches have been proposed such as event detection [19], in-network aggregation [5], [6], distributed compression [13], model-based apporaches [17], [8], and data sampling [10], [18].…”

Section: Related Workmentioning

confidence: 99%

Region Sampling: Continuous Adaptive Sampling on Sensor Networks

Lin

Arai

Gunopulos

et al. 2008

2008 IEEE 24th International Conference on Data Engineering

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Abstract-Satisfying energy constraints while meeting performance requirements is a primary concern when a sensor network is being deployed. Many recent proposed techniques offer error bounding solutions for aggregate approximation but cannot guarantee energy spending. Inversely, our goal is to bound the energy consumption while minimizing the approximation error. In this paper, we propose an online algorithm, Region Sampling, for computing approximate aggregates while satisfying a pre-defined energy budget. Our algorithm is distinguished by segmenting a sensor network into partitions of non-overlapping regions and performing sampling and local aggregation for each region. The sampling energy cost rate and sampling statistics are collected and analyzed to predict the optimal sampling plan. Comprehensive experiments on real-world data sets indicate that our approach is at a minimum of 10% more accurate compared with the previously proposed solutions.

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Section: Related Workmentioning

confidence: 99%

Region Sampling: Continuous Adaptive Sampling on Sensor Networks

Lin

Arai

Gunopulos

et al. 2008

2008 IEEE 24th International Conference on Data Engineering

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“…Fortunately, in many applications one is interested in aggregated data, , e.g., what is the average temperature over a given area, what is the maximum (or minimum) pressure exercised on a bridge or what are the top-k more polluted spots in a city. For each one of those types of queries several efficient algorithms have been proposed, and a variety of techniques have been utilized, e.g., based pruning techniques to avoid unnecessary updates [21,29] or model-based optimization techniques [11,28]. Nonetheless, most approaches are in some way based on the concept of in-network query processing proposed initially in [20].…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

Continuous Probabilistic Sum Queries in Wireless Sensor Networks with Ranges

Hubig

Züfle

Emrich

et al. 2012

Lecture Notes in Computer Science

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Abstract. Data measured in wireless sensor networks are inherently imprecise, due to a number of reasons, and aggregate queries are often used to analyze the collected data in order to alleviate the impact of such imprecision. In this paper we will deal with the imprecision in the measured values explicitly by employing a probabilistic approach and we focus on one particular type of aggregate query, namely the SUM query. We consider that sensors in the network may, operate (all collectively at the same time) in two different modes: (1) returning a finite set of discrete values with a probability attached to each value, or (2) a continuous probabilistic density function over a possibly infinite set of possible values. Our foremost concern is to present the first algorithms to efficiently compute the probabilistic SUM according to the possible world semantics, i.e., without any loss of information. Furthermore, we show how this query can be efficiently updated in dynamic environments where sensor values change often and we show techniques to distribute computation over all network nodes. Our experimental results show that processing queries in-network and incrementally as opposed to collecting the measured values from all nodes at the base station and computing the answer centrally, can reduce the total number of messages sent by at least 50%, thus saving energy and extending the network's lifetime, a chief concern regarding wireless sensor networks.

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“…Assume a data set [13], [16], [17] and arrival timestamp (used in slide window queries), respectively. Let a user-defined query function be…”

Section: A the Problemmentioning

confidence: 99%

Continuous multi-dimensional top-k query processing in sensor networks

Jiang

Cheng

Wang

et al. 2011

2011 Proceedings IEEE INFOCOM

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Abstract-Top-k query has long been an important topic in many fields of computer science. Efficient implementation of the top-k queries is the key for information searching. With the new frontier such as the cyber-physical systems, where there can be a large number of users searching information directly into the physical world, many new challenges arise for top-k query processing. From the client's perspective, different users may request different set of information, with different priorities and at different times. Thus, the top-k search not only should be multi-dimensional, but also across time domain. From the system's perspective, the data collection is usually carried out by small sensing devices. Unlike the data centers used for searching in the cyber-space, these devices are often extremely resourceconstrained and system efficiency is of paramount importance.In this paper, we develop a framework that can effectively satisfy the two ends. The sensor network maintains an efficient dominant graph data structure for data readings. A simple topk extraction algorithm is used for the user query processing and two schemes are proposed to further reduce communication cost. Our proposed methods can be used for top-k query with any linear convex query function. To the best of our knowledge, this is the first work for continuous multi-dimensional top-k query processing in sensor networks; and our simulation results show that our schemes can reduce the total communication cost by up to 90%, compared with the centralized scheme or a straightforward extension from previous top-k algorithm on one-dimensional sensor data.

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A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks

Cited by 141 publications

References 14 publications

Region Sampling: Continuous Adaptive Sampling on Sensor Networks

Region Sampling: Continuous Adaptive Sampling on Sensor Networks

Continuous Probabilistic Sum Queries in Wireless Sensor Networks with Ranges

Continuous multi-dimensional top-k query processing in sensor networks

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