A dynamic state transition algorithm with application to sensor network localization

TL;DR

This paper introduces a novel dynamic state transition algorithm with a risk-based adjustment strategy and gradient refinement to effectively solve the NP-hard sensor network localization problem, outperforming traditional methods.

Contribution

It proposes a new dynamic state transition algorithm with risk and restoration in probability for sensor network localization, without relying on problem-specific assumptions.

Findings

The algorithm effectively localizes sensors with high accuracy.

Experimental results demonstrate superior performance over existing methods.

The approach is robust to different network configurations.

Abstract

The sensor network localization (SNL) problem is to reconstruct the positions of all the sensors in a network with the given distance between pairs of sensors and within the radio range between them. It is proved that the computational complexity of the SNL problem is NP-hard, and semi-definite programming or second-order cone programming relaxation methods are only able to solve some special problems of this kind. In this study, a stochastic global optimization method called the state transition algorithm is introduced to solve the SNL problem without additional assumptions and conditions of the problem structure. To transcend local optimality, a novel dynamic adjustment strategy called "risk and restoration in probability" is incorporated into the state transition algorithm. An empirical study is investigated to appropriately choose the "risk probability" and "restoration probability", yielding the dynamic state transition algorithm, which is further improved by gradient-based refinement. The dynamic state transition algorithm with refinement is applied to the SNL problem, and satisfactory experimental results have testified the effectiveness of the proposed approach.