Improved sequence-based localization applied in coal mine

Abstract

Complex geographical environment brings tremendous challenges to get information of localization in underground coal mines. Sequence-based localization is a simple method; without calculating distance during the positioning stage in real time, this method uses the received signal strength indication matched degree between unknown node and regions to locate. However, sequence-based localization has a great issue on poor marginal nodes localization. Sequence–centroid localization contributes to improving this issue, but the location error on the boundary of whole area is unsatisfactory as well. This article proposes an improved sequence-based localization method which is integrated with quantum-behaved particle swarm optimization, as quantum-behaved particle swarm optimization makes good use of the search performance of global optimal solution. In our simulation, we consider that ZigBee devices can be used to construct wireless sensor networks and locate personnel location. The results prove that the improved sequence-based localization algorithm provides comparable accuracy than sequence-based localization.