Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services (1810.04118v1)

Abstract: Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, Deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users' feedback for training purposes. In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes Variational Autoencoders (VAE) as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends deep reinforcement learning to the semi-supervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on BLE signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.

• The paper introduces a novel framework that combines DRL with VAEs to effectively use both labeled and unlabeled IoT data.
• It significantly enhances indoor localization accuracy by 23% and improves reward acquisition using BLE signal strengths.
• The scalable approach reduces dependency on labeled data, optimizing operational efficiency in diverse smart city applications.

Overview of Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services

The paper under discussion presents an innovative approach that extends Deep Reinforcement Learning (DRL) into a semi-supervised paradigm to improve smart city services and Internet of Things (IoT) applications. Given the challenges associated with acquiring large, labeled datasets in IoT environments, the proposed model efficiently leverages both labeled and unlabeled data to enhance learning outcomes through the integration of Variational Autoencoders (VAE). This methodology aligns particularly well with smart city infrastructures, specifically in applications that require substantial data for decision-making processes, such as smart building indoor localization systems.

Key Contributions:

• Novel Integration of VAE with DRL: The paper introduces a novel framework that synergizes the capabilities of deep generative models and semi-supervised learning paradigms within a DRL context. By incorporating VAEs, the framework capitalizes on the statistical representations of both labeled and unlabeled data, which is unprecedented in prior DRL implementations.
• Improvement in Indoor Localization: As a case paper, the model demonstrates significant improvements in estimating indoor positions via a DRL approach applied to Bluetooth Low Energy (BLE) signal strengths. The reported enhancements indicate a 23% increase in positioning accuracy and a twofold improvement in reward acquisition compared to existing supervised DRL models.
• Scalability and Practicality for IoT Applications: The proposed model addresses the inadequacies of traditional supervised learning, particularly in scenarios abundant with sensor-generated data that lacks comprehensive labeling. The semi-supervised approach markedly reduces the dependency on labeled data, thus optimizing application scalability across varied IoT ecosystems.

Strong Numerical Results:

The empirical evaluations conducted within a real-world academic library environment underscore the robustness of the proposed method. By integrating semi-supervised learning within the DRL framework, the paper reports a remarkable increment of 67% in reward optimization and notable reductions in positional errors during indoor localization trials.

Implications and Future Scope:

The incorporation of semi-supervised DRL models in IoT-driven smart city environments could redefine the efficacy of data-driven services, ensuring enhanced operational efficiency and intelligent decision-making capabilities. The demonstrated performance gains suggest potential applications across diverse IoT sectors, including energy management, intelligent transportation systems, and next-generation location-based services.

Looking ahead, further exploration into diverse IoT contexts could extend the robustness and versatility of this approach. Continuous refinement and adaptive enhancements to accommodate dynamic environmental changes and expanded datasets may pave the way for even more responsive and intelligent IoT systems. Potential future work could also explore hybridized methodologies integrating additional machine learning paradigms to further advance the state of smart city applications.