Author : NEVEEN IBRAHIM MOHAMED GHALI
CoAuthors : Ahmed. A. A. Gad-Elrab , Shereen A. El-aal , Afaf A. S. Zaghrout
Source : International Journal of Advanced Trends in Computer Science and Engineering
Date of Publication : 12/2019
Abstract :
Internet of Things (IoTs) enables entities every day to
communicate and collaborate with each other for providing
information, data and services to inhabitants and users. IoTs
consists of a large number of smart devices that can generate
immense amount of data with different types. These sensors
raw data needs to be modeled in a certain structure before
filtering and processing to provision context information.
This process is called context modeling. Context modeling
provides definition of how context data are structured and
maintained through context aware system. However,
employing model for every context type through context
aware application is static and is specified by the application
developer. The main problem in IoTs is that the structure of
context data changes overtime, therefore static modeling
cannot be adaptable for modeling these changes. In this
paper, a new dynamic approach for context modeling based
on genetic algorithm and satisfaction factor is proposed.
Firstly, the proposed approach uses genetic algorithm to find
the best matching between a set of contexts and a set of
available context models. Secondly, it uses a satisfaction
factor to calculate the satisfaction degree for each context
with each available context model and select the context
model with high satisfaction degree as the structure model
of this context, dynamically. In addition, flexibility
indicator property and context based are defined to measure
the performance of the proposed approach. The results of
conducted simulations show that the proposed approach
achieves higher performance than static approach for context
modeling.
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