Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks

Runze Wan,Qinghui Hu,Naixue Xiong,Jun Shang,Haijun Wang

doi:10.1186/s13638-019-1374-8

Abstract

For resource-constrained IoT systems, data collection is one of the fundamental operations to reduce the energy dissipation of sensor nodes and improve the network lifetime. However, an anomaly or deviation will exert a great influence on the quality of data collected, especially for a data aggregation scheme. By taking into account data-aware clustering and detection of anomalous events, a similarity-aware data aggregation using a fuzzy c-means approach for wireless sensor networks is proposed. Firstly, by using a fuzzy c-means approach, the clustering process can be performed to organize sensors into clusters based on data similarity. Next, an effective support degree function is defined for further outlier diagnosis. Afterwards, the appropriate weight of valid data can be obtained by taking advantage of the probability distribution characteristics of normal samples within a certain period. Finally, the aggregation result in the cluster can be estimated. Practical database-based simulations have confirmed that the proposed data aggregation method can achieve better performance than traditional methods in terms of data outlier detection accuracy and relative recovery error.

Highlights

Wireless sensor networks (WSNs) are typically composed of many small and low-cost sensor nodes with resource constraints, such as low memory capacity, less computational complexity, low communication bandwidth, and limited power
We cope with data-aware clustering and detection of anomalous events, and we use fuzzy c-means approach to organize sensors into clusters based on data similarity
We propose a novel similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks

Summary

Introduction

Wireless sensor networks (WSNs) are typically composed of many small and low-cost sensor nodes with resource constraints, such as low memory capacity, less computational complexity, low communication bandwidth, and limited power. This new type of network demonstrates the characteristics of low cost, wide distribution, small volume, and flexible self-organizing [1]. The basic idea is to aggregate the samples of multi-sensors with a certain degree of redundancy rather than transmit raw data. It means that some nodes will act as aggregator to eliminate redundant data received from other sensor nodes and achieve desirable results for data accuracy. We cope with data-aware clustering and detection of anomalous events, and we use fuzzy c-means approach to organize sensors into clusters based on data similarity

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Methods

Conclusion