Adaptive extreme learning machine ‐ Fuzzy system framework for energy optimization of IOTs in wireless sensor networks. Issue 1 (19th January 2021) - Subramaniam Nachimuthu, Deepa; I, Jasmine Selvakumari Jeya; Baladhandapani, Aruna Devi
Abstract : In this paper, a new adaptive extreme learning machine (ELM) neural network‐Fuzzy system framework is developed for effective and efficient network energy optimization of internet‐of‐things (IoTs) sensor nodes in wireless sensor networks (WSNs). Each sensor nodes in a WSN communicates with one another in varied methods to transfer the data from IoT cloud to the other virtual modes. Optimization of IoTs in sensor networks tends to handle the network energy and accuracy with the help of extremely complex clustering techniques. Henceforth, this paper has developed a novel adaptive ELM‐Fuzzy system framework to handle the network energy level in an optimum way and thus achieving better data accuracy and network throughput by reducing the overhead of the network to the maximum possible. Numerical simulations carried out proves the consistency and efficacy of the modeled novel adaptive ELM‐ Fuzzy system framework for IoT based sensor network on comparing with traditional techniques from previous literatures. Abstract : Development of novel machine learning model for performing effective energy optimization of cloud assisted Internet‐of‐Things in wireless sensor networks. The newly modeled extreme learning machine with fuzzy system framework guarantees the increase of network throughput and ensures the accuracy is significantly better for the considered IOT based network model.