# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 2 Related Works and Background  
In this work, our focus lies on approaches in the range-free category, and in particularly on the advancement of localization techniques based on the DV-Hop technique. One notable issue is that computing the average hop size value during the DV-Hop localization process can result in inaccurate estimates of the distance between nodes. Improving the accuracy when calculating the distance between sensor nodes could lead to a lower localization error. Various DV-Hop enhancements to tackle these concerns have been claimed in the state-of-the-art. Many researchers have recently made major attempts to encourage the use of weighted schemes and bio-inspired techniques to optimize and improve localization accuracy in both anisotropic and isotropic network topologies. In [29], the authors presented a new scheme based on DV-Hop that uses weighted redundancy and optimized beacons. Their paper presented a weighted iterative technique for determining the best number of iterations and average hop size while integrating the weighted mean square error criterion. The hop error between anchors was utilized to assign weights to the signals, while the distance computation employed the optimal average hop size achieved in the previous phase. As a result, the positions of unknown nodes were derived using only a selection of beacon nodes with lower localization errors. Simulations revealed that the proposed variants outperformed previous localization methods, including the original DV Hop. In [30], the authors proposed a modified hop count technique using mobile anchor nodes to reduce the localization error. The proposed technique was assessed based on communication range and the number of anchor nodes. The authors studied static wireless sensor networks with different numbers of mobile anchor nodes, and used their technique to design a similar solution with a static anchor node. Regarding the localization error, their results demonstrated that the suggested scheme surpasses the original DV-Hop. In [31], the authors proposed an alternative modification to the DV-Hop technique to increase localization accuracy in anisotropic networks. The DV-Hop approach was improved in three steps, while the PSO and simulated annealing (SA) methods were combined to handle nonlinear equations and increase the localization accuracy of the sensor nodes. The results demonstrated the efficiency of their proposed DV-Hop compared to competitive methods in the literature. However, in this approach, communication overload among the sensor nodes in the network increases during multi-hop information forwarding. In [32], the authors discussed an enhanced localization algorithm called DEIDV-Hop. This approach combines enhanced DV-Hop and DE techniques. The major aim of DEIDV-Hop is to correct any inaccuracies caused by the distance of the average hop size. To increase population diversity, the authors included a random mutation mechanism that impacts random individuals. This phase seeks to prevent the DE algorithm from experiencing premature convergence and search stagnation. Moreover, during the crossover process, they incorporated a segment of social learning from the PSO based on the newly created individual. Simulation results revealed that this DEIDV-Hop approach had a lower localization error and better stability compared to the current techniques in four distinct network settings. In [33], the authors introduced an improved algorithm called LSDV-Hop. This novel approach us es the least squares theory to improve the precision of node localization. LSDV-Hop computes a transformation vector using the least-squares method in order to align the true and estimated positions of the anchor nodes. The simulation results showed that this LSDV-Hop approach surpasses the original DV-Hop. In [34], the authors presented an online sequential strategy for DV-Hop-based localization. This approach is divided into three main stages. In the first phase, a unique technique is introduced for computing the average hop size between anchors.