IoT-based hybrid optimized fuzzy threshold ELM model for localization of elderly persons

作者：

Highlights：

• The proposition of the fuzzy logic system (FLS) applied over the centroid and ELM for node localization to handle both the low and the high-density scenarios, respectively.

• Designed the control parameter(kα) for PSGWO for boosting the decline speed of convergence factor so that local search can be improved and optimization time can be minimized.

• Optimized FLS and ELM using PSGWO with a free vector for adjusting approximation precision nearer to the moving node’s actual position.

• Proposed a novel population and multi-criteria based soft computing algorithm called hybrid optimized fuzzy threshold extreme learning machine (HOFTELM).

摘要

•The proposition of the fuzzy logic system (FLS) applied over the centroid and ELM for node localization to handle both the low and the high-density scenarios, respectively.•Designed the control parameter(kα) for PSGWO for boosting the decline speed of convergence factor so that local search can be improved and optimization time can be minimized.•Optimized FLS and ELM using PSGWO with a free vector for adjusting approximation precision nearer to the moving node’s actual position.•Proposed a novel population and multi-criteria based soft computing algorithm called hybrid optimized fuzzy threshold extreme learning machine (HOFTELM).

论文关键词：Elderly persons,Smart city,Internet of things,Fuzzy logic system,Weighted centroid,Extreme learning machine

论文评审过程：Received 4 August 2020, Revised 7 June 2021, Accepted 25 June 2021, Available online 1 July 2021, Version of Record 5 July 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115500