Optimize cloud computations using edge computing

The IoT devices captures data and sends it to Cloud for computation but data transfer process from IoT device to Cloud can take lot of time if volume of data is large. Therefore, it makes sense to process captured data locally at IoT edge node to avoid latency. In Edge Computing, the Gateway stores data and perform computations along with traffic aggregation and routing. While Edge analytics allows pre-processing and filtering of the data closer to where it's being created but the data which falls within normal range can be stored in low cost IoT storage and abnormal readings will be sent to Data Lake or in-memory database. Edge Computing will boost traditional Cloud computing model with service nodes placed at the network edges. It will help traditional data center cloud models by reducing latency and increased bandwidth. In future computation and data processing power will slowly shift towards edge devices like sensors, drones, driverless cars etc. Playing augmented reality, 3D video games, content-based video analysis is a challenge on mobile phone due to limited processing power and battery life. Realtime analysis of massive sensor data is needed in industries like manufacturing, mining, transportation to detect anomalies and send alerts. Therefore, Edge Computing and Cloud computing are likely to follow more of a hybrid approach and complement each other. This paper talks about Edge computing architecture, computational offloading approaches, Edge Computing challenges and benefits etc.