A Small World Phenomenon Based Greedy Network Model for Robust IoT Network

The ongoing evolution of the Internet of Things (IoT) empowers the Internet, which is progressively transforming the technology. The robustness plays a crucial role in the constrained lightweight IoT network. The key requirement includes an optimized model to ensure robustness and resilience towards the failure of a node as well as the complete sensor IoT network. The proposed technique uses two greedy criteria that improve the robustness of IoT networks. The proposed method utilizes the widely known Mahalanobis distance model to find out the shortcuts. This scheme also presents the concept of the regional influence of a node to optimize the topology of the network. Additionally, this scheme also presented algorithms for shortcut additions between high powered sensor nodes (HPSN) and the sink. Finally, this paper evaluates the performance of the proposed scheme in-terms of clustering coefficient, average path length, and the network latency, compared to existing techniques.