Node location is one of the most important problems to be solved in practical application of WSN. As a typical location algorithm without ranging, DV-Hop is widely used in node localization of wireless sensor networks. However, in the third phase of DV-Hop, a least square method is used to solve the nonlinear equations. Using this method to locate the unknown nodes will produce large coordinate errors, poor stability of positioning accuracy, low location coverage, and high energy consumption. An improved localization algorithm based on hybrid chaotic strategy (MGDV-Hop) is proposed in this paper. Firstly, a glowworm swarm optimization of hybrid chaotic strategy based on chaotic mutation and chaotic inertial weight updating (MC-GSO) is proposed. The MC-GSO algorithm is used to control the moving distance of each firefly by chaos mutation and chaotic inertial weight when the firefly falls into a local optimum. The experimental results show that MC-GSO has better convergence and higher accuracy and avoids the premature convergence. Then, MC-GSO is used to replace the least square method in estimating node coordinates to solve the problem that the localization accuracy of the DV-Hop algorithm is not high. By establishing the error fitness function, the linear solution of coordinates is transformed into a two-dimensional combinatorial optimization problem. The simulation results and analysis confirm that the improved algorithm (MGDV-Hop) reduces the average location error, increases the location coverage, and decreases and balances the energy consumption as compared to DV-Hop and the location algorithm based on classical GSO (GSDV-Hop).
The Improvement of Wireless Sensor Networks Node Location Algorithm in Extreme Environment
