Modified Genetic Algorithm for Resource Selection on Internet of Things

Abstract

With the epidemic progression in resources on IoT, discovery emerges as an eminent challenge due to requirement of their self-automation. The traditional resource discovery approaches do not provide efficient methodologies due to continuously changing IoT search metrics such as syntax, access, architecture, etc. To address the gap, the paper proposes an optimized technique, namely, Modified Genetic Algorithm for Resource Selection (MGA-RS) that intends to discover optimum data (resources) is short period of time by considering the bit strings of chromosomes. It is evaluated on datasets of Ionosphere from machine learning repository of university college, London. The best and mean fitness are selected in a way that they should be close to each other at the time when MGA-RS reaches termination condition and to minimize classification error from kNN. It is found that MGA-RS outperforms well with kNN based fitness function and is approximately 14% and 15% better than simple and rastrigin fitnesses, respectively, for selecting the optimal resources in IoT.