Abstract
Accurate localization is critical in the internet of things (IOT), especially for wireless sensor networks (WSNs). Location estimation can be affected by factors such as node density, topological diversity, and sensor coverage. As such, we propose a hybrid approach using multistage collaborative calibration for wireless sensor network localization, specifically in 3D environments. This technique integrates a Modified version of Light Gradient Boosting Model (MLGB), which is based on a regression scheme, a cooperative methodology, and a fine calibration model for collaborative fusion. These techniques were combined with quadrilateral shrunk centroid (QSC) and distance vector hop algorithms, using a multi-communication radius and an improved frog-leaping algorithm (DVMFL). In the first step, MLGB was used to correct for inhomogeneous localization estimation errors and RSSI data sparsity. As a result, the model is capable of adapting to high topological diversity (i.e., C-shape, H-shape, S-shape, and O-shape).Successive steps further improved prediction accuracy by using a screening cooperative anchor node strategy to increase node density and enhance the QSC-DVMFL fusion framework for fine position estimation. The proposed methodology was assessed in a series of validation, comparing it to other techniques. The results demonstrated a clear effectiveness and adaptability across a variety of factors that typically affect WSN localization.
Highlights
Wireless sensor networks (WSNs) have attracted increased attention in recent years, due to unprecedented technological progress in multiple engineering disciplines
COLLABORATIVE FUSION FINE CALIBRATION In this stage, a collaborative fusion fine calibration framework is proposed, which consists of quadrilateral shrunk centroid (QSC) and distance vector algorithms based on a multi-communication radius and improved frog leaping (DVMFL) models, to produce rough location estimates
The average location estimation error was high when the percent of missed values was high, increasing significantly as the percentage rose from 50% to 60%
Summary
Wireless sensor networks (WSNs) have attracted increased attention in recent years, due to unprecedented technological progress in multiple engineering disciplines. L. Xu et al.: Hybrid Approach Using Multistage Collaborative Calibration for WSN Localization in 3D Environments typically more accurate as they estimate the distances between unknown and known (anchor) nodes. Known (anchor) nodes are often distributed non-uniformly, causing positioning errors to be lower in the center and higher along the edges This type of irregular network topology is the most significant factor affecting estimation precision (see FIGURE 3). A new machine learning technique is required, to address the problems caused by non-uniform estimation errors and irregular network topologies in WSN localization. Rough localization is performed using a modified light gradient boosting model (MLGB) with a novel loss function, designed to address non-uniform position estimation errors, RSSI data sparsity, and irregular network topology.
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