Research on range-free location algorithm for wireless sensor network based on particle swarm optimization

Abstract

Location technology is the key support technology of wireless sensor network (WSN). The hop number and hop distance information obtained by traditional distance vector hop (DV-Hop) location algorithm can only be acquired by solving the nonlinear equations, and the solution of the equation determines the accuracy of node location. Although the least squares method has better estimation performance, the solution results are sensitive to the average hop distance, which will lead to the large error in the solution of the equation. In order to solve the problem of location error caused by initial value sensitivity of least squares method in the coordinate calculation stage of unknown nodes and beacon nodes, a range-free location algorithm based on particle swarm optimization (PSO) is proposed in this paper. The proposed approach solves the problem of location error caused by initial value sensitivity of least squares method, obtains relatively accurate solution, and improves the accuracy of location algorithm. The experimental results show that the PSO algorithm has faster convergence speed and higher location accuracy than the non-optimization algorithm.

CWDV-Hop: A Hybrid Localization Algorithm With Distance-Weight DV-Hop and CSO for Wireless Sensor Networks

“…The same with Step1 of DV-Hop in Fig. 3 Calculate the distance-weighted hop distance using (23) Estimate coordinates of unknown node using two-dimensional hyperbolic algorithm with (29)- (36) Compute the distance between anchor and unknown node using (2) Initialize CSO and calculate the fitness values of all chickens with (37)- (42) Update the location of roosters, hens and chicks iteratively according to (13), (15) Step 2…”

“…End WDV-Hop The pseudo code of CWDV-Hop is described in Table II. ( , ) xy by two-dimensional hyperbolic localization algorithm using (29)- (36); 28: Initialize the related parameters of CSO where the estimated coordinates ( , ) xy  of unknown node is assigned initially by (42); 29: Evaluate the chicken's fitness values using (41); 30: Update the location ( , ) xy iteratively in accordance with (13), (15) and (18); 31: Stop the iteration until the ending requirement is satisfied; 32: Output: the best coordinates ( , ) xy of unknown node. To verify the performance of the proposed scheme, three groups of simulations have been conducted in Matlab2016a, and implemented on Intel core i5-3320m CPU and RAM of 4 GB.…”

“…In order to describe the above three aspects for short, we use I, II, and III to represent them in Table I. [34], [35], [36], [37] III [38], [ In [28], [29], [30], Genetic Algorithm (GA) is applied to increase the localization accuracy of DV-Hop by building the objective function and fitness function. [28] utilizes GA to optimize the estimated coordinates of unknown nodes, which are calculated by the improved DV-Hop algorithm.…”

“…[33] uses PSO as the learning mechanism of quantum neural network (QNN) and proposes a PSO-QNN model which is applied to optimize the average hop distance of DV-HOP. In [34], [35], [36], [37], PSO tries to optimize the estimated coordinate of unknown nodes obtained by DV-Hop. Singh and Sharma [34] firstly use hyperbola method instead of Least Square (LS) approach to obtain the estimated location of unknown node and then optimize that location with the help of PSO.…”

“…Shi et al [35] modify the average hop distance of relevant nodes by a path matching algorithm and optimize the initial location of each unknown node with a modified PSO algorithm. In [36], an improved PSO is applied to settle the matter of high localization deviation aroused by initial value sensitivity of LS approach. PSO in [37] is used twice that the first time is introduced to build the dynamic set for selecting appropriate anchors by the binary particle swarm optimization (BPSO) algorithm and the second is used to optimize the coordinates of unknown nodes with the continuous PSO method.…”