In this project, we will find a hybrid approach which means that both Fog and cloud enabled processing should work together to build effective IoT data analytics in order to overcome their respective resource constraints and weaknesses.

paper template is attached.Theme. Fog and Cloud Computing paradigm is regarded as capable of satisfying the resource requirements for the emerging IoT platforms. One important aspect of great importance is the continuously increasing data processing, communicating, and decision-making capabilities required by the emerging applications of IoT, while surviving with severe resource constraints, and privacy and security concerns. A large number of IoT devices are assumed to produce much more data than the number of human beings in the world all producing data. The data are collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. In many cases, an IoT device collects much more than the device can deliver to the upstream layer, such from Fog to Cloud. However, ideally the data needs to be analyzed locally to provide local decisions, reduce redundant data, increase privacy, and give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyze the data in the fog devices before finally sending to the Cloud. When, how much data, and how to send the data to Fog and then Cloud are also needed to be addressed properly? In this project, we will find a hybrid approach which means that both Fog and cloud enabled processing should work together to build effective IoT data analytics in order to overcome their respective resource constraints and weaknesses.