Directional Diagnosis for Wireless Sensor Networks

Abstract

Network diagnosis is crucial in managing a wireless sensor network (WSN) since many network-related faults, such as node and link failures, can easily happen. Diagnosis tools usually consist of two key components, information collection and root-cause deduction, while in most cases information collection process is independent with root-cause deduction. This results in either redundant information which might pose high communication burden on WSNs, or incomplete information for root-cause inference that leads false judgments. To address the issue, we propose DID, a directional diagnosis approach, in which the diagnosis information acquirement is guided by the fault inference process. Through several rounds of incremental information probing and fault reasoning, root causes of the network abnormalities with high credibility are deduced. We employ a node tracing scheme to reconstruct the topical topology of faulty regions and build the inference model accordingly. We implement the DID approach in our forest monitoring sensor network system, GreenOrbs. Experimental results validate the scalability and effectiveness of this design.