- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Robustness is an important and challenging issue in the Internet of Things (IoT), 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 has been proven to be a feasible way to optimize the network topology. In this paper, we propose a Greedy Model with Small World properties (GMSW) for heterogeneous sensor networks in IoT. We first present the two greedy criteria used in GMSW to distinguish the importance of different network nodes, based on which we define the concept of local importance of nodes. Then, we present our algorithm that transforms a network to possess small world properties by adding shortcuts between certain nodes according to their local importance. Our performance evaluations demonstrate that, by only adding a small number of shortcuts, GMSW can quickly enable a network to exhibit the small world properties. We also compare GMSW with a latest related work, the Directed Angulation toward the Sink Node Model (DASM), showing that GMSW outperforms DASM in terms of small world characteristics and network latency.
The diversity of nodes in IoT leads to a heterogeneous network, which can have the small world properties by in- troducing a low number of long-range links. In this paper,we proposed the GMSW model for heterogeneous sensor network of IoT. Firstly, we evaluated the small world properties of RAM, DASM and GMSW respectively. In all of those models, the addition of shortcuts is determined by a probability p. The analysis shows that, compared to RAM and DASM, and under the same probability, the average minimum path length of GMSW is reduced faster and meanwhile, its clustering coefficient decreases more slowly, which suggests that GMSW has better small world characteristics than the other two models. In addition, we evaluated the robustness of DASM and GMSW and the results indicate that, whether in the case of general failures or in the case of specific failures, the latency of GMSW is smaller than that of DASM. GMSW can perform well in the presence of a certain amount of node failures, but if the failure of SSNs is greater 986 than a threshold, the network may break down quickly. Thus, finding more factors which may improve the robust- 988 ness of network and taking these factors into account for a better solution will be the future work.