Fog Computing for Sustainable Smart Cities: A Survey (1703.07079v1)

Abstract: The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities.

Overview of "Fog Computing for Sustainable Smart Cities: A Survey"

The paper "Fog Computing for Sustainable Smart Cities: A Survey" systematically explores the paradigm of fog computing as a means to address the inefficiencies of centralized IoT architectures within smart cities. The authors C. Perera, Y. Qin, J.C. Estrella, S. Reiff-Marganiec, and A.V. Vasilakos argue that fog computing holds potential in achieving sustainability goals by offloading data processing tasks closer to the data-generating sources, thereby reducing latency, bandwidth usage, and cloud dependency.

Key Contributions

The paper delineates several contributions in the exploration of fog computing. It presents an extensive review of existing strategies within the fog computing domain and identifies essential characteristics needed for building sustainable smart cities. The paper includes a comparative analysis of over 30 research endeavors and uses four illustrative use case scenarios—smart agriculture, smart transportation, smart healthcare, and smart waste management—to demonstrate the practical applications of fog computing.

Essential Features of Fog Computing

The authors identify ten pivotal features integral to effective fog computing platforms:

1. Dynamic Discovery of Internet Objects (IO): Enables seamless integration and interaction with IoT devices, accommodating the diverse and evolving landscape of smart city infrastructures.
2. Dynamic Configuration and Device Management: Facilitates efficient network management by dynamically configuring devices to optimize energy usage and operational efficiency, critical to sustainability.
3. Multi-Protocol Support (Communication and Application Levels): Supports diverse communication standards, crucial for interoperability amid heterogeneous IoT devices.
4. Mobility: Enhances the ability to manage mobile devices autonomously, contributing to more versatile and adaptable applications, particularly in scenarios where mobility is key.
5. Context Discovery and Awareness: Empowers fog systems to leverage contextual information for more nuanced decision-making processes, thus optimizing resource allocation.
6. Semantic Annotation: Assists in enriching raw data with semantics to improve data processing and analytics reliability, an essential capability for extracting actionable insights.
7. Data Analytics: Facilitates real-time or near-real-time processing of data at the network's edge, reducing the need for data to traverse back to the cloud and enhancing responsiveness.
8. Security and Privacy: Ensures robust mechanisms for safeguarding data integrity and confidentiality, addressing critical concerns around data usage in smart cities.
9. Cloud Companion Support: Emphasizes the interplay between fog and cloud computing, recognizing that a hybrid approach is often necessary to maximize efficiency and sustainability.

Implications and Future Directions

The amalgamation of fog and cloud computing within IoT frameworks presents a promising avenue for developing sustainable smart cities. The authors argue that fog computing can significantly reduce the demand on cloud resources while maintaining or improving service quality. By distributing computational tasks closer to data sources, fog computing supports more responsive and context-aware systems.

There are noted challenges in achieving seamless fog-cloud integration including standardization, security, and data management complexities. The paper calls for further research into developing versatile, plug-compatible fog platforms that can adapt to varying application demands while maintaining operational efficiency and sustainability.

Moreover, the research acknowledges the need for better context-awareness algorithms, dynamic management tools, and novel data analytics frameworks to leverage the full potential of fog computing.

Conclusion

This survey underscores the importance of fog computing in advancing towards sustainable smart city solutions. As cities grow increasingly connected, the role of fog computing becomes vital in addressing the limitations of traditional IoT and cloud computing paradigms. By promoting the distributed processing of data, fog computing not only enhances service delivery but also paves the way for more sustainable urban environments.