Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges (1810.04107v1)

Abstract: The development of smart cities and their fast-paced deployment is resulting in the generation of large quantities of data at unprecedented rates. Unfortunately, most of the generated data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards that benefit from the availability of such data. Moreover, the high dynamical nature of smart cities calls for new generation of machine learning approaches that are flexible and adaptable to cope with the dynamicity of data to perform analytics and learn from real-time data. In this article, we shed the light on the challenge of under utilizing the big data generated by smart cities from a machine learning perspective. Especially, we present the phenomenon of wasting unlabeled data. We argue that semi-supervision is a must for smart city to address this challenge. We also propose a three-level learning framework for smart cities that matches the hierarchical nature of big data generated by smart cities with a goal of providing different levels of knowledge abstractions. The proposed framework is scalable to meet the needs of smart city services. Fundamentally, the framework benefits from semi-supervised deep reinforcement learning where a small amount of data that has users' feedback serves as labeled data while a larger amount is without such users' feedback serves as unlabeled data. This paper also explores how deep reinforcement learning and its shift toward semi-supervision can handle the cognitive side of smart city services and improve their performance by providing several use cases spanning the different domains of smart cities. We also highlight several challenges as well as promising future research directions for incorporating machine learning and high-level intelligence into smart city services.

• The paper presents a scalable three-level semi-supervised deep reinforcement learning framework that leverages both labeled and unlabeled smart city data.
• It achieves significant performance improvements, with rewards increasing by 60% to 100% and localization accuracy improving by 6% to 23%.
• The framework enhances operational efficiencies in urban services and lays the groundwork for future decentralized, context-aware smart city applications.

Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges

The paper presents a detailed examination of using big data and machine learning to facilitate the development of cognitive smart cities. The authors identify the challenges associated with harnessing the voluminous data generated by smart cities, particularly emphasizing the phenomenon of data wastage due to the inadequacy of labeled data. They argue for the necessity of semi-supervised learning to effectively utilize both labeled and unlabeled data. This paradigm shift is aimed at addressing the underutilization of data generated within smart city infrastructures.

Proposed Framework

A pivotal contribution of this work is the three-level learning framework, designed to address the hierarchical nature of data in smart cities. This framework is scalable and leverages semi-supervised deep reinforcement learning (DRL). The integration of semi-supervised learning allows the model to utilize a mixed dataset comprising both labeled and unlabeled data. This approach enables the convergence towards superior control policies, optimizing the potential of smart services without disregarding valuable data.

Strong Numerical Results

The application of the proposed semi-supervised DRL model within a smart campus context, as part of the greater smart city ecosystem, yielded significant numerical outcomes. The model achieved a marked improvement in performance, realizing 60% to 100% more rewards compared to strictly supervised models. Furthermore, the localization accuracy improved between 6% to 23%, highlighting the efficacy of incorporating unlabeled data into the learning process.

Implications and Future Directions

Practically, this framework offers enhanced operational efficiencies across various sectors, including energy, water management, and public safety, by enabling timely and context-sensitive decision-making processes. Theoretically, it suggests the need to shift towards more adaptive and decentralized data processing models, overcoming the limitations of traditional static learning algorithms.

The paper outlines several future research avenues, emphasizing the potential for on-device intelligence through compression techniques for deep learning on resource-constrained devices. It also highlights the integration of semantic technologies for improved human-machine interactions in smart city environments. Additionally, the value of context-awareness algorithms is underscored, suggesting that this could further optimize decision-making processes by incorporating situational parameters into analytic models.

Challenges Highlighted

The authors acknowledge several challenges, including ensuring privacy and security in machine learning frameworks against false data injection attacks and managing context-awareness effectively across different application domains. They also recognize the need for innovative approaches that decentralize data analytics, facilitating real-time insights closer to the data source.

Conclusion

This paper offers a comprehensive exploration of the possibilities introduced by machine learning to smart cities, advocating for novel approaches such as semi-supervised learning to exploit big data's full potential. Its model sets a foundation for future research into creating more responsive, efficient, and intelligent urban environments capable of adapting to dynamic contexts and leveraging vast datasets for enhanced cognitive functionalities.