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Over the years, electric vehicles (EVs) have gained extensive attention and popularity worldwide. The ownership of EVs has been rapidly growing globally. The increasing EV charging (EVC) demand creates growth of electric energy demand and imposes additional stress on the power grid. To meet the required EVC demand, it is necessary to construct sufficient EV supply equipment (EVSE). The construction of EVSE is a key prerequisite for the wider deployment of EVs. Although EVC stations (CSs) have gained a default position for EV infrastructure, battery-swapping systems (BSSs) have also drawn considerable attention. In this article, we first introduce the system architectural design of BSSs. Then we present four kinds of BSS designs. The lessons learned from the practice of BSS implementation and operation in China are provided. BSS standardization is a determining issue in BSS development. State-of-the-art BSS standardization development is summarized, while key technical challenges and opportunities of BSSs are listed. [ABSTRACT FROM AUTHOR]

In this brief, we analyze the damage caused by malware-induced cyber attacks in Cyber-Physical Power Systems (CPPSs). Incubation period and detection probability of the malware are considered in our analytical framework. From attacker’s perspective, the trade-off between attack effectiveness and detection risk is studied to help choose the best attack timing and obtain the maximum payoff. Moreover, we conduct case studies in CPPSs formed by coupling IEEE 118-Bus System with scale-free communication networks. Simulation results reveal that the maximum expected payoff for the attacker is affected by the coupling pattern and the structure of communication network in CPPSs. [ABSTRACT FROM AUTHOR]

The dependency links in interdependent networks can lead to serious cascading failures when some of the network nodes are damaged. We find that the imbalance between nodes at both ends of the dependency links is one of the important reasons for a cascade of failures, which can be reduced as a whole to improve the robustness of the networks. Therefore, we propose a method to improve the robustness of interdependent networks by reducing key unbalanced dependency links. Firstly, we propose the dependency link imbalance index (DLI), which denotes the degree difference between nodes at both ends of a dependency link. And then we sort the dependency links in descending order according to the index value. The small number of the dependency links whose index values are in the forefront are called key unbalanced dependency links. By removing the key unbalanced dependency links, the imbalance of dependency links in interdependent networks is reduced in the mass, and the robustness of the networks is improved. Compared with the present random dependency links reduction strategy (RRS), key unbalanced dependency links reduction strategy (KURS) proposed in this brief can maintain the integrity and functionality of interdependent networks and improve more robustness while reducing fewer dependency links. Numerical simulations on three interdependent networks verify the effectiveness of key unbalanced dependency links reduction strategy proposed in this brief. [ABSTRACT FROM AUTHOR]

In modern power grids, a local failure or attack can trigger catastrophic cascading failures, which makes it challenging to assess the attack vulnerability of power grids. In this brief, we define the optimal $K$ -link attack problem and study the attack vulnerability of power grids in cascading failures. We propose a link centrality measure based on both topological and electrical properties for power grids. According to this centrality measure, we propose a greedy attack algorithm and an optimal attack algorithm. Simulation results based on standard IEEE bus test data show that the optimal attack is better than the greedy attack and the traditional PSO-based attack in fracturing power grids. Moreover, the greedy attack has an adequate attack efficiency with a smaller computational cost. This brief aids in understanding the vulnerability of power grids and provides clues for securing power grids. [ABSTRACT FROM AUTHOR]

Link prediction in networks has been a hot topic over the years, while its robustness has not been well discussed in the network literature. In this brief, we study the robustness of mainstream link prediction methods under various kinds of network attacks, including random attack (RDA), centrality based attack (CA), similarity based attack (SA), and simulated annealing based attack (SAA). In the variation of precision, a typical evaluation index of link prediction, we find that for the SA and SAA, a small fraction of link removals significantly degenerates the performance of link prediction. In general, SAA has the highest attack efficiency, followed by the SA and then the CA attack. Interestingly, the performance of some particular CA strategies, such as the betweenness based attack (BA), are even worse than the RDA attack. Furthermore, we discover that the link prediction method with high performance probably has low attack robustness, and vice versa. [ABSTRACT FROM AUTHOR]

Infrastructure networks, such as power grids, have been developing rapidly. There is a growing demand for predicting the future evolution process of power grids in the planning of infrastructure systems. As the structure of power grids evolves to be more complex, the grids can be studied under complex network theory by extracting structural properties. Accordingly, the prediction of the evolution process of power grids can be regarded as a link prediction problem. To correspond with the actual self-organization regulations of power grids, a novel link prediction model based on preferential attachment is proposed in this brief. After experimenting on several real world power grids, results show that our model outperforms six other popular models in various kinds of realistic networks. It is demonstrated as a possible methodology for solving the prediction problem of power grid evolution. [ABSTRACT FROM AUTHOR]

A SIN is a heterogeneous network that consists of various access nodes in different space mobile platforms, such as satellites, high altitude airships, and unmanned aerial vehicles. Due to their remarkable coverage performance, SINs play a key role in establishing a real-time and integrated communication framework for information acquisition and wideband signal transmission. This article presents the architecture of SINs and discusses three promising techniques for multiple-user transmission. Taking advantage of multiple-antenna systems, several transmission methods based on beamforming are investigated. We discuss various schemes for multiple- user access, with an emphasis on non-orthogonal approaches. Finally, cooperative transmission methods using different functional nodes are presented to enhance the performance of SINs. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrences of large-scale blackouts highlight the necessity of enhancing the robustness of power grids. In this brief, based on the cascading failure model which considers both the network topology and electrical characteristics running in power grids, we study how the topological metrics affect the robustness of power grids. The topological metrics considered include both the connectivity of the network and the distribution of generators. We compare the results in typical complex network models and an IEEE 118-bus network. Moreover, by using the simulated annealing method, we find the optimal network topology to achieve the best network robustness, and compare the topological metrics before and after the optimization to verify our findings. The results show that, in order to enhance the robustness of power grids, it is better to make the network sparsely connected, and place the generators as hubs and decentralize these generators. [ABSTRACT FROM AUTHOR]

Modern society relies heavily on complex infrastructures, such as power systems and communication systems, which makes it vulnerable to intentional attacks or unpredictable failures. Being able to locate critical components in such systems allows us to protect it from attacks or failures with minimal effort. From a network science perspective, two fundamental types of components in a system are its constituent nodes and edges. The importance of nodes recently attracts a lot of interests. However, edges are paid less attention to, although in the case of protection, edge targeted methods are less invasive and more flexible. In this brief, we address the issue of quantifying importance of edges. The importance of edges is defined as how their removal affects the connectivity of the network, since connectivity is the most important property that ensures the network’s function. The proposed importance measure, nearest-neighbor connectivity-based edge importance, can be used to quantify the importance of a single edge or a set of edges. The result on real-world network data shows that the proposed measure is more efficient than the most widely used measures. As another result, we show that the importance of a single edge is not necessarily positive correlated with the importance of incident nodes, which is widely assumed in literatures. [ABSTRACT FROM AUTHOR]

Real-world networks feature weights of interactions, where link weights often represent some physical attributes. In many situations, to recover the missing data or predict the network evolution, we need to predict link weights in a network. In this paper, we first proposed a series of new centrality indices for links in line graph. Then, utilizing these line graph indices, as well as a number of original graph indices, we designed three supervised learning methods to realize link weight prediction both in the networks of single layer and multiple layers, which perform much better than several recently proposed baseline methods. We found that the resource allocation index (RA) plays a more important role in the weight prediction than other topological properties, and the line graph indices are at least as important as the original graph indices in link weight prediction. In particular, the success application of our methods on Yelp layered network suggests that we can indeed predict the offline co-foraging behaviors of users just based on their online social interactions, which may open a new direction for link weight prediction algorithms, and meanwhile provide insights to design better restaurant recommendation systems. [ABSTRACT FROM AUTHOR]

In this brief, by considering realistic network operational settings, we propose a novel model to investigate the cascading failures in interdependent power grids and communication networks. We perform a critical node analysis on single networks to identify the vital nodes from the perspective of network robustness. Moreover, we assess the robustness of an interdependent system composed of an Internet AS-level network and the IEEE 118 Bus. Our simulation results show that assortative coupling of node destructiveness is more robust for densely coupled networks, whereas disassortative coupling of node robustness and node destructiveness performs better for sparsely coupled cases. [ABSTRACT FROM PUBLISHER]

In this brief, we consider a generic type of communication network consisting of two kinds of nodes, i.e., hosts and routers, and only the hosts can generate and receive packets. In this kind of network, the traffic performance is closely related to the traffic generation pattern, which strongly depends on the hosts' locations. We employ a simulated annealing algorithm to find a near-optimal configuration of the hosts, which effectively balances the traffic loads and improves the overall traffic performance. Our study suggests that the traffic generation pattern is an important factor for communication network design. [ABSTRACT FROM PUBLISHER]

With the Internet highly developed, complex communication networks are now facing the dilemma of how to use limited resources to achieve the best communication performances. In this brief, we study the resource allocation from a more comprehensive perspective where both allocating the capacities of networks and distributing the rates of traffic flows are considered. We build an optimization problem to optimize the resource allocation and maximize the network utility at the same time. Moreover, we design an iterative algorithm to find the optimal solution. The convergence of this algorithm is supported by theoretical proofs and simulation results. Our work may give insights to the plan of resource allocation in future communication networks. [ABSTRACT FROM PUBLISHER]

Braess's paradox shows a counterintuitive scenario where more resources may cause worse traffic performance when all users pursue their personal optimum. Previous study on this topic mainly focused on the specific traffic volume to show the existence of such a paradox. For real-world complex communication networks, on the contrary, the traffic volume changes from time to time. Therefore, it is important to study the traffic performance under this dynamic condition. We find that Braess's paradox does not happen when the traffic volume is sufficiently low or sufficiently high. Between these two extremes, the probability that Braess's paradox happens also changes with the traffic volume. This result is helpful for communication network planning and management. [ABSTRACT FROM AUTHOR]

The performance of the noncoherent differential chaos-shift-keying (DCSK) communication system over a multipath fading channel with delay spread is evaluated. Analytical expressions of the bit error rates are derived under the assumption of an independent Rayleigh fading two-ray channel model. Analytical and simulated results are presented and compared. The multipath performance of the DCSK system is also compared with that of the coherent CSK system as well as conventional generic waveform communication schemes. [ABSTRACT FROM AUTHOR]