Smart City Gnosys

Smart city article details

Title Toward Smart Urban Development Through Intelligent Edge Analytics
ID_Doc 57735
Authors Zaid M.A.; Faizal M.; Maheswar R.; Abdullaziz O.I.
Year 2020
Published EAI/Springer Innovations in Communication and Computing
DOI http://dx.doi.org/10.1007/978-3-030-38516-3_8
Abstract The rise of successful cutting-edge Internet of Things (IoT) applications for urban development has inspired the industry and research community. Although the industry verticals such as automotive, robotics, e-health, and entertainment applications create new business opportunities for the service providers, they pose challenges in terms of deployment cost, reliability, and latency requirements. For example, self-driving vehicles require ultralow latency and reliable data processing to make split-second decisions. Unfortunately, cloud-based solutions for such IoT applications are not suitable due to the end-to-end latency and connection reliability. To meet these challenging requirements, edge computing paradigm has emerged as a solution where computational resources are brought to the proximity of the end users. Now, advanced data analytics, machine learning, and cognitive techniques can also be deployed at the edge of the network. Together, edge computing, data analytics, and machine learning empower service providers with true and intelligently automated infrastructure for IoT applications. In this chapter, we investigate the relevant use cases of edge-enabled IoT applications for smart urban development. To this end, we also provide a comprehensive study of the recent trends and the state of the art in accommodating all these emerging technologies in the acceleration of smart urban development. © Springer Nature Switzerland AG 2020.
Author Keywords Green wireless big data; Hierarchical hidden Markov model (HHMM); Machine learning; Smart cities


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