Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks
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RESEARCH
Open Access
Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks Guorui Li1* and Ying Wang2
Abstract Data aggregation is a very important method to conserve energy by eliminating the inherent redundancy of raw data in wireless sensor networks (WSNs). In this article, we developed an automatic auto regressive-integrated moving averagemodeling-based data aggregation scheme in WSNs. The main idea behind this scheme is to decrease the number of transmitted data values between sensor nodes and aggregators by utilizing time series prediction model. The proposed scheme can effectively save the precious battery energy of wireless sensor nodes while keeping the predicted data values of aggregators within application-defined error threshold. We show through experiments with real data that the predicted data values of our proposed scheme fit the real sensed data values very well and fewer messages are transmitted between sensor nodes and aggregators than the native data aggregation scheme. Furthermore, the characteristics of the proposed data aggregation scheme are also discussed in this article. Keywords: Wireless sensor networks, Data aggregation, Time series analysis, ARIMA model, Pediction
1. Introduction Wireless sensor networks(WSNs) are made up of a mass of spatially distributed autonomous sensor nodes, to jointly monitor physical or environmental conditions, such as temperature, humidity, vibration, pressure, sound, motion, or pollutants [1]. These sensors could be scattered randomly in harsh environments such as battlefields or deterministically placed at specified locations to collect information from the environment. The typical application fields of WSNs include industrial process control, security and surveillance, traffic control, home automation, environmental sensing, structural health monitoring, etc. [2]. In WSNs, the communication cost of sensor node is often several orders of magnitude higher than that of computation. For instance, the transmission and reception energy costs for one bit of MICAz node [3] and TelosB node [4] are 600, 670, and 720, 810 nJ, respectively. However, the computation energy costs for 1 bit of them are only 3.5 and 1.2 nJ, respectively [5]. Therefore, data aggregation scheme is often adopted as an effective way to * Correspondence: [email protected] 1 School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China Full list of author information is available at the end of the article
save the precious battery energy of wireless sensor nodes by eliminating the inherent redundancy in the raw data and avoiding unnecessary data transmission. Moreover, data aggregation scheme is also useful to extract applicationspecified general information from the raw data which are collected from the sensor nodes [6]. Hence, it is critical for WSNs to support data aggregation schemes. There have been plenty of researches in the recent past on data aggregation schemes in WSNs. Typically, the whole sensor netwo
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