Treffer: EffiResNet-SENet: An Optimization-assisted Deep Learning Approach for Range-based Wireless Sensor Network Localization.

Advanced methods are increasingly needed in the field of wireless sensor network (WSN) localization to address the difficulties of unknown node positions, especially in dangerous environments. In this study, a novel localization model called EffiResNet-SENet is introduced. It combines range-based localization with a technique that uses optimization. The proposed localization model consists of two primary phases: the training phase and the localization phase. The localization phase utilizes the powerful deep learning model of EfficientNet, ResNeSt, and optimized SENet, named EffiResNet-SENet, to predict the node locations. EffiResNet-SENet leverages the efficiency and accuracy of EfficientNet, the enhanced feature representation of ResNeSt, and the channel-wise dependencies recalibration of SENet. The two main stages the suggested model goes through are the training and localization phases. To improve localization accuracy, EffiResNet-SENet is trained using measured distance-based features like Angle of Arrival (AoA), Time of Arrival (ToA), Time Difference of Arrival (TDoA), Angle of Departure (AoD), Time of Flight (ToF), and Received Signal Strength Indicator (RSSI). The activation function of SENet Blocks is optimized using a HawkCos Optimisation Algorithm (HCOA), which combines the sine-cosine algorithm (SCA) and Harris' hawk optimization (HHO), to significantly increase localization accuracy. PYTHON is used to carry out the proposed model. [ABSTRACT FROM AUTHOR]

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