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Cost Based Optimal Data Sampling Rate in Wireless Sensor Network

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Abstract

Abstract

En 中文
Data sampling rate is an important performance metric in sensor-based environmental monitoring and real-time applications. Optimal and reliable monitoring in sensor-based applications requires frequent data sampling. However, when data is transferred through wireless channels, as is the case in Wireless Sensor Networks (WSNs), increasing this rate may limit battery lifespan. This is because Radio Data Transmissions (RDTs) are the most important source of energy consumption in WSN nodes. In this paper, we compare the performance of several RDT reduction algorithms such as Redundant Data, K-Means, Autoregressive data reduction (AR), Autoregressive Integrated Data Gathering (ARIMA) and Adaptive Distributed Data Gathering (ADiDaG), varying the data sampling rate, and determine the best sampling rate that minimizes a cost function, another performance metric. We perform the simulations using the readings from 20,000 real smart meters from the City of Moncton's Water and Utility Department. Simulation results demonstrate that the ARIMA approach achieves the best sampling rate that minimizes the cost function.
Keywords:
Wireless Sensor Network
Smart meter
Energy saving
Data Reduction
Green communication
ARIMA
Radio Transmission
Water Leakage

Journal

I
International Wireless Communications and Mobile Computing Conference
IF:
0
Papers:
10
Citations:
0

Organization

No organization information available