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Article

Associations Rankings Model for Cellular Surveillance Analysis

1School of Computing and Informatics, University of Nairobi, Nairobi, Kenya


Journal of Computer Sciences and Applications. 2020, Vol. 8 No. 2, 40-45
DOI: 10.12691/jcsa-8-2-1
Copyright © 2020 Science and Education Publishing

Cite this paper:
Michael M Kangethe, Robert Oboko. Associations Rankings Model for Cellular Surveillance Analysis. Journal of Computer Sciences and Applications. 2020; 8(2):40-45. doi: 10.12691/jcsa-8-2-1.

Correspondence to: Michael  M Kangethe, School of Computing and Informatics, University of Nairobi, Nairobi, Kenya. Email: mich01mk@gmail.com

Abstract

This is the study and implementation of an association surveillance technology framework model for GSM mobile networks. This enables the efficient and automated identification of entity associations and potential relationships between several entities and events based on a hierarchy of interactions. The approach to this problem is to develop a weighted graph network G=(V(W),E) where V={w(SID1),w(SID2),…,w(SIDn)} w represents the association sore between the ShadowID represented as a node SID and the Person of interest(POI) represented as the root node. This model and algorithm are developed as an automated surveillance system framework that enables the tracking of individual entities ' relationships with others based on their interaction and by their physical proximity to the entity of interest. As the future of automated surveillance will not just include the collection of geographic and visual data but also intelligence on the particular entity's interaction log information from activity patterns which can be mapped in an easy to present format to the interested parties.

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