Performance Modelling and Analysis of IoT based Edge computing policies

In today’s era, the acceptance of IoT-based edge devices is growing exponentially, which creates challenges of data acquisition, processing, and communication. In the edge computing paradigm, intelligence is shifted from the center to the edge by performing specific processing and prediction locally. A strategy based on reducing communication resources between sensors and edge devices is the prime focus of this investigation. It uses a predictive model-based policy at edge devices for the reconstruction of not delivered context vector. A new hybrid Averaged Exponential Smoothening (AES) policy proposed is based on the current context vectors as well as a smoothing vector to reduce reconstruction error and improve the percentage of communication. It is observed that if we send data only when there is a marginal change in data then we can reduce communication overhead as well as keep reconstruction error low. This policy would be suitable for IoT-based edge computing applications for the smart city such as Smart Home, Healthcare, and Intelligent traffic to delivers the power of AI.