Multi-Modal Evidence Filtering in Wireless Sensor Networks

Abstract

A novel framework named Dempster-Shafer Information Filtering for in- formation processing in Distributed Sensor Networks (DSNs) is presented. More- over, distributed algorithms to implement spatio-temporal ltering applications in grid sensor networks are presented within the context of the framework. The framework facilitates processing multi-modality sensor data with a high noise level. Moreover, we compare intuitively appealing results against Dempster- Shafer fusion to grant further credence to the proposed framework. The concept of the proposed framework is based on the belief notions in Dempster-Shafer (DS) evidence theory. It enables one to directly process tem- porally and spatially distributed multi-modality sensor data to extract meaning buried in the noise clutter. Certain facts on lter parameter's selection impose several challenges in the design of the Information Filter. This is analysed using a re propagation scenario when high noise is present in the sensed data. Infor- mation bandwidth and the sluggishness of the lter are traded-o to minimise the e ect of the noise in the output evidence signal. From the application point of view, we address a Wireless Sensor Network (WSN) deployed in a multi-stoery building which can be e ectively used to convey information to relevant parties ( re ghters in their rescue operations) during an emergency situation. Therefore, a re propagation scenario is simulated to illustrate the applications and justify the proposed framework.