As is well known, multi-hop range-free localization algorithms demonstrate pretty good performance in isotropic networks in which sensor nodes distribute evenly and densely. However, these algorithms are easily affected by network topology, causing a significant decrease in positioning accuracy. To improve the localization performance in anisotropic networks, this paper presents a multi-hop range-free localization algorithm based on Least Square Regularized Regression (LSRR). By building a mapping relationship between hop counts and real distances, we can regard the process of localization as a regularized regression. Firstly, the proximity information of the given network is measured. Then, a mapping model between the geographical distances and the hop distances is constructed by LSRR. Finally, each sensor node finds its own position via this mapping. The Average Localization Error (ALE) metric is used to evaluate the proposed method in our experiments, and results show that, compared with similar methods, our approach can effectively decrease the effect of anisotropy, thus considerably improving the positioning accuracy.

In order to improve the frequency characteristics of the system with wind energy storage after short circuit fault, a coordinated control strategy of wind energy storage is proposed. Firstly, the influence of the wind storage power on the frequency change rate in the frequency rising stage and the frequency extreme value in the frequency recovery stage is analyzed. On this basis, the variable weight coefficient related to the frequency disturbance severity is introduced to establish the optimization model to solve the reference power of the wind storage. Then, the frequency regulation capability of the wind turbine during fault ride through and steady-state operation is evaluated, and the reference power of wind power generation is obtained by considering the rotor energy balance, which is compared with the reference power of wind energy storage to obtain the reference power of energy storage. Finally, the simulation is carried out in the four machine two zone system. The results show that the control strategy can reduce the frequency fluctuation range and accelerate the frequency recovery speed of the wind energy storage network. At the same time, it can make full use of the frequency modulation ability of wind power generation and reduce the impact of speed recovery link.

Most complex real systems are found to have multiple layers of connectivity and required to be modelled as multiplex networks. One of the extremely critical problems is to reduce the congestion and enhance the transfer capacity, especially in real communication networks with a big data environment. A novel and effective strategy to improve traffic and control congestion is proposed by adding edges according to their weights which are defined by the topology structural properties. Furthermore, which layer is more effective when our strategy is applied is discussed based on its topology structure. Adding edges between nodes whose product of multiplex network betweenness is the highest is confirmed to be more effective, particularly in the layer with stronger community structure. Simulation experiments on both computer‐generated and real‐world networks demonstrate that our strategy can enhance the transfer capacity of multiplex networks significantly, which is in good agreement with our analysis.