A Probability-Based Scheme for Generating Robust Internet of Things

Jingchen Sun, Ning Chen, Songwei Zhang, Zhaolong Ning, Tie Qiu

Abstract: With the scale of Internet of Things (IoT) continually expanding, the topology is growing rapidly and the probability of cascading collapse due to node failures or malicious attacks is increasing. The decrease in the Quality of Service (QoS) of IoT could be mitigated by robust topology. Existing optimization strategies usually use heuristic algorithms to enhance topology robustness. However, when the scale of topology is large, these algorithms involve a significant amount of iterative searching for the optimal solution, which is time-consuming and prone to getting stuck into local optimum. To tackle this situation, this study introduces arithmetic encoding and proposes a novel probability-based robust topology generation model that can quickly generate IoT robust topology. We losslessly compress robust topologies using arithmetic encoding and extract their features. Based on the extracting features, we design a unique probability-based topology generation approach that avoids the time overhead of iterative calculations. Experimental results demonstrate that the proposed solution in this paper can construct robust topologies in less time for different network scales.