Explicit Context Integrated Recurrent Neural Network for applications in smart environments Articles uri icon

publication date

  • December 2024

issue

  • 124752

volume

  • volume 255, Part D

International Standard Serial Number (ISSN)

  • 0957-4174

Electronic International Standard Serial Number (EISSN)

  • 1873-6793

abstract

  • The development and progress in sensor, communication, and computing technologies have led to smart environments. In such environments, data can easily be acquired not only from the monitored entities but also from the surroundings where the entity is operating. The data from the operating environment, which is not useful for learning models in isolation, is referred to as explicit context here. The performance of the predictive models can potentially improve when explicit contexts are taken into account. Typically, the data from various sensors are present in the form of time series. Recurrent Neural Networks (RNNs) are used for such data as they implicitly can deal with temporal contexts. However, while using RNNs for various smart environment applications, the available explicit contexts are often ignored or incorporated as primary (or ordinary) features into the model. Further, the conventional RNN models such as Elman RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) in their current form do not provide any mechanism to integrate explicit contexts. In this paper, we propose a Context Integrated RNN (CiRNN) that enables integrating explicit contexts represented in the form of contextual features. In CiRNN, the network weights are influenced by contextual features in such a way that the primary input features receive more weight if they are relevant in a given context. To show the efficacy of CiRNN, we selected three application domains, remaining useful life estimation for predictive maintenance, traffic forecasting for intelligent transportation systems, and appliance energy usage prediction for smart homes. These applications typically capture data from various sensors and provide scope for exploiting contextual information. For all three case studies, we used public datasets. The test results show that CiRNN performs better when compared to baseline models that incorporate contextual features as primary features, as well as in comparison to state-of-the-art models. The performance is measured in terms of RMSE, MAE, and score from an asymmetric scoring function. The latter measure is specific to the task of RUL estimation.

subjects

  • Telecommunications

keywords

  • context-dependent recurrent neural network; smart environments; remaining useful life estimation; sensor time series; traffic forecasting; appliance energy prediction