Semantically Enriched Data for Effective Sensor Data Fusion

Geeth Ranmal De Mel, Tien Pham, Thyagaraju Damarla, Wamberto Weber Vasconcelos, Timothy J Norman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Data fusion plays a major role in assisting decision makers by providing them with an improved situational awareness so that informed decisions could be made about the events that occur in the field. This involves combining a multitude of sensor modalities such that the resulting output is better (i.e., more accurate, complete, dependable etc.) than what it would have been if the data streams (hereinafter referred to as ‘feeds’) from the resources are taken individually. However, these feeds lack any context-related information (e.g., detected event, event classification, relationships to other events, etc.). This hinders the fusion process and may result in creating an incorrect picture about the situation. Thus, results in false alarms, waste valuable time/resources.

In this paper, we propose an approach that enriches feeds with semantic attributes so that these feeds have proper meaning. This will assist underlying applications to present analysts with correct feeds for a particular event for fusion. We argue annotated stored feeds will assist in easy retrieval of historical data that may be related to the current fusion. We use a subset of Web Ontology Language (OWL),1 OWL-DL to present a lightweight and efficient knowledge layer for feeds annotation and use rules to capture crucial domain concepts. We discuss a solution architecture and provide a proof-of-concept tool to evaluate the proposed approach. We discuss the importance of such an approach with a set of user cases and show how a tool like the one proposed could assist analysts, planners to make better informed decisions.
Original languageEnglish
Title of host publicationSPIE Defense, Security, and Sensing (DSS 2011)
PublisherSPIE
Number of pages10
Publication statusPublished - May 2011
Event2011 Defense Security and Sensing - Orlando, United Kingdom
Duration: 25 Apr 201129 Apr 2011

Conference

Conference2011 Defense Security and Sensing
CountryUnited Kingdom
CityOrlando
Period25/04/1129/04/11

Fingerprint

Sensor data fusion
Ontology
Data fusion
Semantics
Sensors

Cite this

De Mel, G. R., Pham, T., Damarla, T., Vasconcelos, W. W., & Norman, T. J. (2011). Semantically Enriched Data for Effective Sensor Data Fusion. In SPIE Defense, Security, and Sensing (DSS 2011) SPIE.

Semantically Enriched Data for Effective Sensor Data Fusion. / De Mel, Geeth Ranmal; Pham, Tien; Damarla, Thyagaraju; Vasconcelos, Wamberto Weber; Norman, Timothy J.

SPIE Defense, Security, and Sensing (DSS 2011). SPIE, 2011.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

De Mel, GR, Pham, T, Damarla, T, Vasconcelos, WW & Norman, TJ 2011, Semantically Enriched Data for Effective Sensor Data Fusion. in SPIE Defense, Security, and Sensing (DSS 2011). SPIE, 2011 Defense Security and Sensing, Orlando, United Kingdom, 25/04/11.
De Mel GR, Pham T, Damarla T, Vasconcelos WW, Norman TJ. Semantically Enriched Data for Effective Sensor Data Fusion. In SPIE Defense, Security, and Sensing (DSS 2011). SPIE. 2011
De Mel, Geeth Ranmal ; Pham, Tien ; Damarla, Thyagaraju ; Vasconcelos, Wamberto Weber ; Norman, Timothy J. / Semantically Enriched Data for Effective Sensor Data Fusion. SPIE Defense, Security, and Sensing (DSS 2011). SPIE, 2011.
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