A Node Localization Algorithm for Wireless Sensor Networks Based on Virtual Partition and Distance Correction

The coordinates of nodes are very important in the application of wireless sensor networks (WSN). The range-free localization algorithm is the best method to obtain the coordinates of sensor nodes at present. Range-free localization algorithm can be divided into two stages: distance estimation and coordinate calculation. For reduce the error in the distance estimation stage, a node localization algorithm for WSN based on virtual partition and distance correction (VP-DC) is proposed in this paper. In the distance estimation stage, firstly, the distance of each hop on the shortest communication path between the unknown node and the beacon node is calculated with the employment of virtual partition algorithm; then, the length of the shortest communication path is obtained by summing the distance of each hop; finally, the unknown distance between nodes is obtained according to the optimal path search algorithm and the distance correction formula. This paper innovative proposes the virtual partition algorithm and the optimal path search algorithm, which effectively avoids the distance estimation error caused by hop number and hop distance, and improves the localization accuracy of unknown nodes.

A Novel Hybrid Range-free Approach to locate Sensor Nodes in 3D WSN using GWO-FA Algorithm

The precise node location of the sensor nodes is an essential requirement in Wireless Sensor Networks (WSNs) to determine the place or event occurring at a particular instant of time. In WSN, existing localization schemes considers two-dimensional (2D) space, while in actual life, sensor nodes are placed in three-dimensional (3D) space. In 3D localization, there are many research challenges, such as higher computational complexity, poor location prediction, lesser coverage, and depending only on fewer anchor nodes. To address various research issues in 3D environment we propose a range-free technique applied in an anisotropic scenario having Degree of Irregularity (DOI) as 0.01 using the concepts of fuzzy logic system (FLS). Anisotropic properties of nodes are considered to determine the efficiency of Grey wolf with Firefly algorithm. In our proposed scenario, the received signal strength (RSS) information is necessary among the target nodes and their corresponding anchor nodes for determining the location of target nodes using the information based on edge weights. These edge weights are further modelled using Hybrid GWO-FA to estimate the location of target nodes. The proposed algorithms is energy efficient as a single location aware node is used for localization. Further, the concept of virtual anchors is introduced that helps the algorithm to determine 3D positions.

A Kernel-Based Node Localization in Anisotropic Wireless Sensor Network

Wireless sensors localization is still the main problem concerning wireless sensor networks (WSN). Unfortunately, range-free node localization of WSN results in a fatal weakness–, low accuracy. In this paper, we introduce kernel regression to node localization of anisotropic WSN, which transfers the problem of localization to the problem of kernel regression. Radial basis kernel-based G-LSVR and polynomial-kernel-based P-LSVR proposed are compared with classical DV-Hop in both isotropic WSN and anisotropic WSN under different proportion beacons, network scales, and disturbances of communication range. G-LSVR presents the best localization accuracy and stability from the simulation results.