A New Method for Intelligent Message Network Management in Ubiquitous Sensor Networks

Abstract

Ubiquitous Sensor Network (USN) computing is a useful technology for autonomic integrating in different environments which can be available anywhere. Managing USN plays an important role on the availability of nodes and paths. In order to manage nodes there is a cyclic route starts from manager, passing nodes, and come back to manager as feedback. In this paper, a new, self-optimizing method presented for finding this cyclic path by combining epsilon greedy and genetic algorithm and then it is compared with other well-known methods in terms of cost of the route they find and the power consumption. The results show that the route that is found by our new method costs at least 53% less than other methods. However in some cases, it uses 32% more energy for finding the route which can be compensate in traversing the shorter route. The overall simulation results in prototype data show the effectiveness of the proposed method.