A Data-Driven Framework for Survivable Wireless Sensor Networks

The data-driven technique uses real-world readings or simulated dataset to draw inference about the behavior of communication network. The design of the network is further optimized to enhance the performability according to the inference drawn. The performability of the network is dependent on the performance parameters of the network such as packet delivery ratio, packets dropped, delay, throughput, and data rate. The data rate prediction is carried out using different machine learning techniques. Further, the performability of the network is directly associated with its survivability. Better is the network performability, more is the survivability of that particular network. This work proposes a framework for survivable Wireless Sensor Network which predicts the data rate of the network. The past experience serves as an optimized way to traverse data in the network with efficient data rate. A primary dataset designed with the help of simulations is used for this work. Also, the robustness of best predictive model is checked with the help of N-fold cross-validation technique.