An evolving risk management framework for wireless sensor networks

Abstract

Individual units in a wireless sensor network (WSN) are exposed to multiple risks, either during or after their deployment. The identification of the risk sources and their watchful monitoring in dynamic, unpredictable environments is pivotal to ensure a smooth, long-term functioning of the WSN. We introduce an evolving risk management framework for WSNs that captures multiple risk features and provides both a visual depiction of the corporate network threats at any time and a numerical assessment of any sensor's overall risk. The visualization module is embodied through an evolving clustering architecture which heavily relies on shadowed sets. The risk assessment module embraces fuzzy and shadowed evaluations of the risk sources and incorporates a simple adaptive learning process that weights the risk sources proportionally to their observed impact on failed sensors. A distinctive trait of the proposed framework is its highly automated yet still human-centric nature. Experiments utilizing different sensor models and deployment scenarios confirm the feasibility of the risk management platform under consideration.