Proactive Edge Computing for Smart City: A Novel Case for ML-Powered IoT

Abstract

Purpose: In response to the challenges posed by traditional cloud-centric IoT architectures, this research explores the integration of Proactive Edge Computing (PEC) in context of smart cities. The purpose addresses privacy concerns, enhance system capabilities, and introduce machine learning powered anticipation to revolutionize urban city management.

Methodology: The research employs a comprehensive methodology that includes a thorough review of existing literature on use of IoT devices, edge computing and machine learning in context of smart cities. It introduces the concept of PEC to advocate for a shift from cloud-centric to on-chip computing. The methodology is based on several case studies in various domains of smart city management focusing on the improvement of public life.

Findings: This research reveal that the integration of PEC in various smart city domains leads to a significant improvement. Real time data analysis, and machine learning predictions contributes to reduced congestion, enhance public safety, sustainable energy practices, efficient waste management, and personalized healthcare.

Unique Contribution to Theory, Policy and Practice: The research makes a unique contribution to the field of theory, policy and practice by proposing a paradigm shift associated with IoT for smart cities. The suggested shift not only ensures data security but also offers a more efficient and proactive approach to urban challenges. The case studies provide actionable insights for policymakers and practitioners, fostering a holistic understanding of the complexities associated with deploying IoT devices in smart cities. The research lays the foundation for a more secure, efficient, and anticipatory ecosystem, aligning technological advancements with societal needs in the dynamic landscape of smart cities.