Sensor stream reduction for clustered wireless sensor networks

Aquino, André L. L.; Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Frery, Alejandro C.; Loureiro, Antônio A. F.; Fernandes, A.O.

doi:10.1145/1363686.1364183

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…As presented in Aquino et al . [ 21 ], and Frery et al . [ 22 ], a WSN can be conveniently described as sampling/reconstruction processes within the signal processing framework.…”

Section: The Modelsmentioning

confidence: 99%

Data Driven Performance Evaluation of Wireless Sensor Networks

Frery

Ramos

Alencar-Neto

et al. 2010

Sensors

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Wireless Sensor Networks are presented as devices for signal sampling and reconstruction. Within this framework, the qualitative and quantitative influence of (i) signal granularity, (ii) spatial distribution of sensors, (iii) sensors clustering, and (iv) signal reconstruction procedure are assessed. This is done by defining an error metric and performing a Monte Carlo experiment. It is shown that all these factors have significant impact on the quality of the reconstructed signal. The extent of such impact is quantitatively assessed.

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“…As presented in Aquino et al . [ 21 ], and Frery et al . [ 22 ], a WSN can be conveniently described as sampling/reconstruction processes within the signal processing framework.…”

Section: The Modelsmentioning

confidence: 99%

Data Driven Performance Evaluation of Wireless Sensor Networks

Frery

Ramos

Alencar-Neto

et al. 2010

Sensors

Self Cite

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“…[3], a WSN system can be represented by the diagram shown in Figure 1. In this diagram, N denotes the environment and the process to be measured, P is the phenomena of interest, with V * their space-temporal domain.…”

Section: Problem Descriptionmentioning

confidence: 99%

A wavelet-based sampling algorithm for wireless sensor networks applications

Aquino

Oliveira

Wanner

2010

Proceedings of the 2010 ACM Symposium on Applied Computing

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This work proposes and evaluates a sampling algorithm based on wavelet transforms with Coiflets basis to reduce the data sensed in wireless sensor networks applications. The Coiflets basis is more computationally efficient when data are smooth, which means that, data are well approximated by a polynomial function. As expected, this algorithm reduces the data traffic in wireless sensor network and, consequently, decreases the energy consumption and the delay to delivery the sensed information. The main contribution of this algorithm is the capability to detect some event by adjusting the sampling dynamically. In order to evaluate the algorithm, we compare it with a static sampling strategy considering a real sensing data where an external event is simulated. The results reveal the efficiency of the proposed method by reducing the data without loosing its representativeness, including when some event occurs. This algorithm can be very useful to design energy-efficient and time-constrained sensor networks when it is necessary to detect some event.

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“…Let us consider a WSN monitoring physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. Such a system is represented by the diagram [ 37 ]:…”

Section: Problem Statementmentioning

confidence: 99%

Data Centric Sensor Stream Reduction for Real-Time Applications in Wireless Sensor Networks

Aquino

Nakamura²

2009

Sensors

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This work presents a data-centric strategy to meet deadlines in soft real-time applications in wireless sensor networks. This strategy considers three main aspects: (i) The design of real-time application to obtain the minimum deadlines; (ii) An analytic model to estimate the ideal sample size used by data-reduction algorithms; and (iii) Two data-centric stream-based sampling algorithms to perform data reduction whenever necessary. Simulation results show that our data-centric strategies meet deadlines without loosing data representativeness.

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Sensor stream reduction for clustered wireless sensor networks

Cited by 4 publications

References 12 publications

Data Driven Performance Evaluation of Wireless Sensor Networks

Data Driven Performance Evaluation of Wireless Sensor Networks

A wavelet-based sampling algorithm for wireless sensor networks applications

Data Centric Sensor Stream Reduction for Real-Time Applications in Wireless Sensor Networks

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