Enhancing Visibility of Network Performance in Large-Scale Sensor Networks

Being embedded in the physical world, wireless sensor networks (WSNs) present a wide range of failures, due to environment conditions, hardware limitations and software uncertainties, and so on. Once deployed, the interactivity of a WSN greatly decreases, which leads to limited visibility of network performance for managers to investigate sensor behaviors. Existing evidence-based approaches aim to explain particular network symptoms based on expert knowledge and heuristic experiences, which degrade diagnosis accuracy and perform unreliably. These diagnosis models define a limited group of network failures, emphasizing on expert knowledge too much, and thus fail to be adopted to different applications. In this work, we propose VN2, a novel tool to enhance the visibility of network performance. VN2 quantifies a node's state in terms of variation of 43 metrics, and trains a representative matrix of network exceptions with Non-negative Matrix Factorization (NMF) model. With this matrix, when a new network state coming up, VN2 automatically attributes abnormal symptoms to one or more root causes. We implement VN2 on test bed and real system traces. Experimental results show that VN2 models network exceptions involving small subsets of root causes, and the interpretation of root causes help us understand network behaviors in details.