A Grid-Based Clustering Algorithm via Load Analysis for Industrial Internet of Things

In the Industrial Internet of Things (IIoT), wireless sensor network (WSN) technology makes devices that communicate with each other. The information integrated from multiple data sources will be transformed into productivity. However, the clusters close to the base station take a considerable load over multi-hop transmission, and in this case, the lifetime of the industrial WSN is restricted. To solve this problem, a grid-based clustering algorithm via load analysis for IIoT is presented in this paper. First, the network load is quantitatively analyzed and then a load model is constructed. Furthermore, a set of expressions is deduced to indicate the network load distribution. It is concluded that the number of delivered packets in each level is related to the grid length at that level. The optimal grid length is obtained by solving polynomials to achieve the uniform energy consumption of nodes at each level. Finally, the network is partitioned into unequal grids according to the optimal cluster size and all the nodes of a grid are formed into a cluster. Results of the experiments show that compared with ACT, ER-HEED, and RUHEED, our algorithm balances energy depletion effectively and extends the whole network lifetime.