A Machine Learning Based Connectivity Restoration Strategy for Industrial IoTs

Abstract

The connectivity restoration has significance for Industrial IoTs (IIoTs). If the connectivity is compromised, mobile data collectors can be deployed to restore the connectivity. The aggregation ratio, which is the proportion of data successfully delivered to the sink over all data, is considered as a crucial index. However, previous works only consider the travel distance, the load balance, the latency and the energy cost over the aggregation ratio. In this paper, a machine learning based connectivity restoration strategy CRrbf, that utilizes a Radial Basis Function Neural Network (RBFNN) along with an Unscented Kalman Filter (UKF), is proposed to maximize the aggregation ratio meanwhile reduce the energy cost. The theoretical analysis and simulation results indicate that CRrbf outperforms both distance based strategies and terrain based strategies in the aggregation ratio, the network latency and the network throughput. And the energy cost of CRrbf is less than that of distance based strategies.