Optimized Deep Learning for Congestion-Aware Continuous Target Tracking and Boundary Detection in IoT-Assisted WSN

Abstract

Target tracking (TT) is an important application of Internet of Things (IoT)-assisted wireless sensor networks (WSNs) in which sensor nodes monitor and report the positions of continuously moving objects. However, continuous tracking of multiple objects causes network congestion. To solve this challenge and provide congestion-aware continuous TT and boundary detection, we propose a novel energy-aware radial clustering based piecewise regressive multiobjective golden eagle optimized deep convolutive learning (EARC-ODCL) algorithm. In particular, EARC-ODCL incorporates a multiobjective stochastic sampled golden eagle optimization to select the optimal cluster heads (CHs) for sending the target information. Also, a fully connected deep convolutive neural learning is used to identify the boundary of sensor nodes. Furthermore, a piecewise linear regression approach is used for network congestion prediction and minimize data loss. Thereby, the proposed EARC-ODCL is able to perform continuous TT and boundary detection with higher accuracy in IoT-assisted WSN. Extensive simulations are conducted to assess the performance of EARC-ODCL in terms of various metrics, such as TT accuracy and time, boundary detection accuracy (BDA), and data loss.