Multivariate Spatial Condition Mapping Using Subtractive Fuzzy Cluster Means

Sensors

卷:14

Adnan Al-Anbuky¹  Hakilo Sabit¹ 

[1] Electrical and Electronic Engineering, Auckland University of Technology, 24 St Paul Street, Auckland 1010, New Zealand;

关键词: data stream mining;    sensor cloud;    fuzzy clustering;    wireless sensor network;   

DOI  :  10.3390/s141018960

来源: DOAJ

【 摘 要 】

Wireless sensor networks are usually deployed for monitoring given physical phenomena taking place in a specific space and over a specific duration of time. The spatio-temporal distribution of these phenomena often correlates to certain physical events. To appropriately characterise these events-phenomena relationships over a given space for a given time frame, we require continuous monitoring of the conditions. WSNs are perfectly suited for these tasks, due to their inherent robustness. This paper presents a subtractive fuzzy cluster means algorithm and its application in data stream mining for wireless sensor systems over a cloud-computing-like architecture, which we call sensor cloud data stream mining. Benchmarking on standard mining algorithms, the k-means and the FCM algorithms, we have demonstrated that the subtractive fuzzy cluster means model can perform high quality distributed data stream mining tasks comparable to centralised data stream mining.

【 授权许可】

Unknown