Data Stream Based Algorithms For Wireless Sensor Network Applications

Aquino, André L. L.; Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Buriol, Luciana S.; Loureiro, Antônio A. F.; Fernandes, A.O.; Coelho, Carlyle Mendes

doi:10.1109/aina.2007.49

Cited by 18 publications

(14 citation statements)

References 18 publications

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“…• Data stream processing: The ψ reduction, performed by CHj, is defined by two data stream techniques [2]: sampling and sketch.…”

Section: Behaviors Are Shownmentioning

confidence: 99%

“…However, this organization may not be appropriate in specifics scenarios, and its solutions can seldom be applied. For that reason, we enhanced the sensor stream reduction algorithms developed for non-clustered networks [2], in order to apply them in clustered networks. Clustered networks offer major advantages over their non-clustered counterparts in these situations, so the question we address is Are the sensor stream algorithms, developed for non-clustered networks, efficient in clustered networks?…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the work [2], proposes and evaluates two algorithms based on data stream that use sampling and sketching to reduce data traffic in non-clustered WSNs and, therefore, to decrease the delay and energy consumption. The sampling scheme uses log n items to represent the original n elements data, preserving information.…”

Section: Introductionmentioning

confidence: 99%

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Sensor stream reduction for clustered wireless sensor networks

Aquino

Figueiredo²,

Nakamura³

et al. 2008

Proceedings of the 2008 ACM Symposium on Applied Computing

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This work presents the use of sensor stream reduction algorithms in clustered wireless sensor networks (WSNs), where the cluster head node is responsible to reduce the amount of data generated by the nodes in its cluster. Moreover, a formal description to sensor stream reduction problem is proposed and an analytical model is adapted to show that when our solutions are adopted in cluster based organization, they have a superior performance to that observed when they are used in a non-clustered organization.

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“…• Data stream processing: The ψ reduction, performed by CHj, is defined by two data stream techniques [2]: sampling and sketch.…”

Section: Behaviors Are Shownmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensor stream reduction for clustered wireless sensor networks

Aquino

Figueiredo²,

Nakamura³

et al. 2008

Proceedings of the 2008 ACM Symposium on Applied Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The data stream generated from WSN is continuous in nature, and as a result many WSN applications [13], [14] operate on an unbounded sliding window containing the previous N samples of the observed data.…”

Section: Related Workmentioning

confidence: 99%

Identification and Validation of Spatio-Temporal Associations in Wireless Sensor Networks

Ali

Pissinou

Makki

2009

2009 Third International Conference on Sensor Technologies and Applications

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Wireless sensor networks (WSN) consist of large numbers of sensor nodes embedded in physical space for continuously sensing/monitoring a specified physical phenomenon. Typically, WSN requires spatially dense sensor deployment to satisfactorily respond to the requirements of the application. The spatial proximity of nodes and the temporal behaviour of the sensing operation causes the observations sensed to be associated spatio-temporally. However, due to unfavourable environmental conditions and low energy resources available in WSN, there are instances when the nodes lie with in the same spatial space and measure the same attribute yet the observations sensed by them are not related. No mechanism is present that identifies the degree of association or disassociation between them.In this paper we propose a methodology that uses concepts from computational geometry to identify and isolate relevant spatially related subsets from a given universal set of nodes in WSN, and validate the existence of spatio-temporal associations among the observations sensed by them. The node identification process is achieved by employing classical object intersection solutions from computational geometry and the validation process is achieved by using similarity verification methods. By means of simulations on real time data we demonstrate the procedure for identification and validation by using Jaccard's coefficient, Cosine similarity and Pearson product correlation coefficient.

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“…Some related works consider univariate data reduction and they use techniques such as data aggregation [10], adaptive sampling [7], or sensor stream reduction [2]. Considering multivariate data reduction, there are some proposals that consider discrete wavelet transformation, hierarchical clustering, sampling and singular value decomposition techniques [8].…”

Section: Introductionmentioning

confidence: 99%