Real-Time Edge Analytics for IoT Networks: Optimizing Data Processing and Decision-Making in Smart Cities

As urban cities become smarter, the increasing number of Internet of Things (IoT) devices leads to the generation and collection of large amounts of data. Real-time processing and analysis of this data is extremely important in many applications. However, the bottleneck in conventional cloud processing stems from latency, bandwidth constraints and privacy concerns. The future framework proposed in this research will cater towards enabling real-time edge analytics in IoT networks while optimizing edge-based data processing decisions to improve smart-city benefits. Such benefits include making intelligent decisions regarding energy usage and network congestion through advanced predictive analytics on traffic management. The methods for enabling distributed data processing, optimized resource management, and deploying predictive models at specific edge nodes will be explored within this study. Similarly, it aims to propose approaches for maintaining the data security and privacy of IoT users while ensuring minimal latency and high accuracy in predictive analytics. The efficacy of the proposed framework will be demonstrated through case studies in smart city settings involving automated traffic management, energy optimizations and real-time monitoring of environmental parameters.