Pot

Cho, Yong Hyun; Son, Jihoon; Chung, Yon Dohn

doi:10.1145/1402050.1402053

Cited by 17 publications

(5 citation statements)

References 16 publications

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“…Its basic idea is to gather data according to priority; that is to say, the higher readings are collected earlier. Cho et al propose POT algorithm (partial ordered tree) which considers the space correlation to maintain k sensor nodes with highest sensing values [14]. Michel et al propose a framework KLEE to process top- k query [15] which allows for trade-off efficiency against result quality and bandwidth saving against communications.…”

Section: Related Workmentioning

confidence: 99%

Prediction-Based Filter Updating Policies for Top-k Monitoring Queries in Wireless Sensor Networks

Zheng

Zhang

Song

et al. 2014

International Journal of Distributed Sensor Networks

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Processing top-query in an energy-efficient manner is an important topic in wireless sensor networks. Redundant data transmitting between base station and sink node is avoided by installing filters on sensor nodes; thus, communication overhead between base station and sensor nodes is decreased. However, existing algorithms such as FILA, and DAFM consume much energy when updating the filter window. In this paper, we propose a new top-algorithm named PreFU which is based on prediction models to update window parameters of filters. PreFU can predict the next step sensor values based on time series predicting models which can be built by historical data. By estimating the cost of updating window parameters based on predicted sensor values, updates of filter window parameters can be reduced. Thus, the cost of updating window parameters is decreased. Experimental results show that our PreFU algorithm is more energy-efficient than existing algorithms while guaranteeing the accuracy of top-query results.

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Section: Related Workmentioning

confidence: 99%

Prediction-Based Filter Updating Policies for Top-k Monitoring Queries in Wireless Sensor Networks

Zheng

Zhang

Song

et al. 2014

International Journal of Distributed Sensor Networks

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“…We have studied many querying algorithms in WSNs, but we have not found a Continuous Top-k Contour Regions Querying algorithm as we proposed. There are many top-k querying algorithms [3–8] and continuous contour regions querying schemes for WSNs [9–13]. We have selected part of them to analyze their core mechanisms, characteristics, advantages, and disadvantages.…”

Section: Related Workmentioning

confidence: 99%

“…In POT [3] protocol, they classify sensor nodes into a number of Partial Ordered Trees (POT), and the sink maintains the global ranking list (GR). POT protocol is useful for the occasion that top-k results are spatially correlated.…”

Section: Related Workmentioning

confidence: 99%

“…There are many energy-efficient queries in WSNs, for example, top-k querying [3–8], contour regions querying [9–15], aggregate querying [16], event querying [17, 18], and so forth. The traditional continuous top-k querying in WSNs can return the list of k sensor nodes with the highest (or lowest) readings at every sampling period.…”

Section: Introductionmentioning

confidence: 99%

“…A contour map of an attribute (e.g., temperature) displays the distribution of the attribute value over the topographic regions. Figure 1 shows the contour regions of temperatures in a real volcanic area, Kawah Ijen crater lake [3]. The sink can detect and analyze the environmental events using the contour regions.…”

Section: Introductionmentioning

confidence: 99%

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Continuous Top-k Contour Regions Querying in Sensor Networks

Chen

et al. 2013

International Journal of Distributed Sensor Networks

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Wireless sensor networks (WSNs) are important parts of Internet of Things or Cyber-Physical Systems (CPS). WSNs can be seen as a new type of distributed database systems. The data query processing is very important for WSNs. In this paper, we proposed a Continuous Top-k Contour Regions Querying algorithm (CTCRQ) which can continuously obtain the top-k contour regions and does not lose the rate of precision (accuracy). We take full advantage of the kth value of top-k result in current round as the threshold to suppress the nodes whose readings do not belong to the top-k result in next round. Extensive experiments are conducted to evaluate the performance of the proposed CTCRQ approach by using a synthetic data set. The results provide a number of insightful observations and show that CTCRQ substantially outperforms Centralized algorithm, Centralized Optimized algorithm, and CCM algorithm in terms of data transmitted.

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