A Greedy Model With Small World For Improving The Robustness Of Heterogeneous Internet Of Things

Robustness is an important and challenging issue in the Internet of Things (IoTs). which contains multiple types of heterogeneous networks. Improving the robustness of topological structure, i.e., withstanding a certain amount of node failures, is of great significance especially for the energy-limited lightweight networks. Meanwhile, a high-performance topology is also necessary.The small world model in complex networks has been proven to be a feasible way to optimize the network topology. In this paper, we design a robust topology model for heterogeneous sensor networks of IoTs using the small world concept. Long-range links have been added as an attempt to implement shortcuts. Firstly, two theoretical models are studied, namely, Watts and Strogatz Small World Model and Random Addition Small World Model (RAM). Meanwhile, we show how to construct shortcuts in wireless networks and how to measure the importance of network nodes. Secondly, a Greedy Model with Small World properties (GMSW) is presented. GMSW is defined to use a greedy approach and implements a shortcut algorithm based on the local importance of nodes (SALI). This approach is particularly applicable when Regular Sensor Nodes (SSNs) and Super Sensor Nodes (SSNs) are randomly deployed in the monitoring area. And it works between the data link layer and the network layer. GMSW improves the efficiency of data transmission and reduces nodes'energy consumption, thereby increasing the lifetime of the network.Experimental results show that GMSW has greater performance by only adding a small number of shortcuts than the Random Addition Model (RAM) and the Directed Angulation towards the Sink Node Model (DASM). Furthermore, the robustness of GMSW is evaluated under general and specific failures. In both cases, the proposed model has been found to produce a greater reduction in network latency, and increased robustness.