Edge computing framework for enabling situation awareness in IoT based smart city

The Internet of Things (IoT) offers a lot of benefits for building smart cities. Such cities will be able to utilize a huge number of heterogeneous IoT devices that can generate a sheer volume of data. So, considering this heterogeneity, one of the major challenges in smart cities is how to process this data and identify different situations for decision-makers on the basis of this data. The traditional cloud computing approach can provide enormous computing and storage facilities to support data processing. However, it requires all the data to be moved to the cloud from the edge devices of the user endpoint, thus introducing a high latency. In this paper, we used the edge computing approach to minimize such latency. Besides, as major portion of data is generated from the user endpoint, processing this data in the edge can significantly improve the performance. Our experiment shows that processing raw IoT data at the edge devices is effective in terms of latency and provides situational awareness for the decision makers of smart city in a seamless fashion.