INTERPRETABLE DEEP LEARNING FOR IIOT: AN LSTM–IFWT DRIVEN SENSOR DATA ANALYTICS FRAMEWORK FOR E-WASTE MANAGEMENT AND SUSTAINABLE MONITORING

Contextually, the Industrial Internet of Things (IIoT) contributes significantly to the Industry 4.0 to deliver intelligent monitoring and predictive maintenance systems. Nevertheless, the IoT sensor data, particularly in alpha-phase applications, are very volatile and intermittent with devices, which create significant obstacles to trustworthy data analysis. This paper introduces a mature and scalable framework to Long Short-Term Memory (LSTM) network and an innovative Intuitive Feature Wavelet Transformation (IFWT) mechanism that may be employed to successfully model and explain such time-varying time-series data. LSTM is a form of Recurrent Neural Network (RNN), and may be utilized to learn a temporal dependency between multivariate temperature measurements observed by sensors installed within as well as outside a controlled room atmosphere. The implementation using the architect of the Block chain layer achieved by the application of 5 layer and 13-layer functional transformations developed by using e-waste information security and threefold and multilabel categorization resolution in the advertisement of revolutionary IOT. Results of the experiment indicate that the suggested LSTM-based model is highly accurate in forecasting and detecting anomalies even in the presence of data instability. In order to make the models more interpretable, IFWT is proposed as a post-hoc approach to analysis that converts the weights of learned features into human-intuitive formats. The results of the Precision and performance in terms of F-1scores have been found to be more than 15% better in time series solution and more than 96% better in terms of classifications than SOA architectures. The proposed study proposes the vitality of IIOT ecosystems on block chain technology employed with augmented throughput imploring the many state-of-the-art methods and other deep learning structures to actualize the many solutions on IIOT.