Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT Applications

الملخص EN

Due to the proliferation of global monitoring sensors, the Internet of Things (IoT) is widely used to build smart cities and smart homes.

5G HetNets play an important role in the IoT video stream.

This paper proposes an improved Call Session Control Function (CSCF) scheme.

The improved CSCF server contains additional modules to facilitate IoT traffic prediction and resource reservation.

We highlight traffic prediction in this work and develop a compressed sensing based linear predictor to catch the traffic patterns.

Experimental results justify that our proposed scheme can forecast the traffic load with high accuracy but low sampling overhead.