An Approach for Situation Aware IOT Service based on SOA
Journal: International Journal of Science and Research (IJSR) (Vol.6, No. 5)Publication Date: 2017-05-05
Authors : Trupti P. Mokal; Poonam Gupta;
Page : 1229-1233
Keywords : K-means clustering; HMM Hidden Marcov Model; Fuzzy logic; Pattern Identification; Gaussian distribution;
Abstract
Since late eighties of the last century Situation Awareness (SA) has started gaining the interest of several researchers from different scientific fields. Significance of Situation awareness (SA) is well established in different sectors such as aviation, armed forces and air traffic. In this paper, a weather department data is considered where weather department person keep entering the daily routine data into situation aware system to a web application on the basis of some attributes like temperature, humidity, wind speed, etc. An automatic Situation aware system validates the weather data in set time for any kind of disaster prediction using following four steps Preprocessing, k-means clustering, HMM (Hidden Marcov Model) and Fuzzy logic. An existing system is based on Hardware based device like sensor and communicators. There is no backup plan for failure of these devices whereas in this system, event generation is been triggered by well applied data mining techniques. This paper introduces situation awareness due to data entered into situation aware system to a web application. The different patterns based on the effects of event is been identified based on pre-defined protocols using frequent dataset mining at distributed network server. The Publish/Subscribe broker module broadcast event risks to all subscribers using Gaussian distribution model via SMS or email. This paper proposes an automatic situation aware system, alert generator software, due to which an alert arise. This is what an Event-driven is.
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