一种无线传感器网络健壮性可调的能量均衡拓扑控制算法

无线传感器网络中, 应用环境的干扰导致节点间距不能被准确度量. 所以利用以节点间距作为权重的闭包图(EG)模型构建的拓扑没有考虑环境的干扰, 忽略了这部分干扰带来的能耗, 缩短了网络生存时间. 针对无线传感器网络拓扑能量不均的特点和EG模型的缺陷, 首先引入节点度调节因子, 建立通信度量模型和节点实际生存时间模型; 其次量化网络节点度, 从而获取满足能量均衡和网络生命期最大化需求的节点度的取值规律; 然后利用该取值规律和函数极值充分条件解析推导出网络最大能量消耗值和最长生存时间, 并获得最优节点度; 最后基于以上模型提出一种健壮性可调的能量均衡拓扑控制算法. 理论证明该拓扑连通且为双向连通. 仿真结果说明网络能利用最优节点度达到较高的健壮性, 保证信息可靠传输, 且算法能有效平衡节点能耗, 提高网络健壮性, 延长网络生命周期.     

Proactive channel access in dynamic spectrum networks

Open Spectrum systems allow fast deployment of wireless technologies by reusing under-utilized pre-allocated spectrum channels, all with minimal impact on existing primary users. However, existing proposals take a reactive sense-and-avoid approach to impulsively reconfigure spectrum usage based solely on the latest observations. This can result in frequent disruptions to operations of both primary and secondary users. In this paper, we propose a proactive spectrum access approach where secondary users utilize past channel histories to make predictions on future spectrum availability, and intelligently schedule channel usage in advance. We propose two channel selection and switching techniques to minimize disruptions to primary users and maintain reliable communication at secondary users. Both simulation and testbed results show that the proactive approach effectively reduces the interferences to primary users by up to 30%, and significantly decreases throughput jitters at secondary users.     

The complexity and robustness of metro networks

Transportation systems, being real-life examples of networks, are particularly interesting to analyze from the viewpoint of the new and rapidly emerging field of network science. Two particular concepts seem to be particularly relevant: scale-free patterns and small-worlds. By looking at 33 metro systems in the world, this paper adapts network science methodologies to the transportation literature, and offers one application to the robustness of metros; here, metro refers to urban rail transit with exclusive right-of-way, whether it is underground, at grade or elevated. We find that most metros are indeed scale-free (with scaling factors ranging from 2.10 to 5.52) and small-worlds; they show atypical behaviors, however, with increasing size. In particular, the presence of transfer-hubs (stations hosting more than three lines) results in relatively large scaling factors. The analysis provides insights/recommendations for increasing the robustness of metro networks. Smaller networks should focus on creating transfer stations, thus generating cycles to offer alternative routes. For larger networks, few stations seem to detain a certain monopole on transferring, it is therefore important to create additional transfers, possibly at the periphery of city centers; the Tokyo system seems to remarkably incorporate these properties.     

On the robustness of Spanish telecommunication networks

Complex networks are considered robust against random failures but sensitive to failures in target. Under this perspective we analyze the robustness of the Spanish telecommunication networks for digital transmission, the Synchronous Digital Hierarchy (SDH), considering failures in the equipments that control the flow of information. Our results suggest that the robustness depends not only on the connectivity of equipments removed but also on the capacity and types of links associated with that removal. We have also observed that the robustness of SDH networks depends on the particular initial design and evolution of each Spanish province.     

Dynamical robustness analysis of weighted complex networks

Robustness of weighted complex networks is analyzed from nonlinear dynamical point of view and with focus on different roles of high-degree and low-degree nodes. We find that the phenomenon for the low-degree nodes being the key nodes in the heterogeneous networks only appears in weakly weighted networks and for weak coupling. For all other parameters, the heterogeneous networks are always highly vulnerable to the failure of high-degree nodes; this point is the same as in the structural robustness analysis. We also find that with random inactivation, heterogeneous networks are always more robust than the corresponding homogeneous networks with the same average degree except for one special parameter. Thus our findings give an integrated picture for the dynamical robustness analysis on complex networks.     

Bicomponents and the robustness of networks to failure

We study bicomponents in networks, sets of nodes such that each pair in the set is connected by at least two independent paths, so that the failure of no single node in the network can cause them to become disconnected. We show that standard network models predict there to be essentially no small bicomponents in most networks, but there may be a giant bicomponent, whose presence coincides with the presence of the ordinary giant component, and we find that real networks seem by and large to follow this pattern, although there are some interesting exceptions. We also study the size of the giant bicomponent as nodes in the network fail and find in some cases that our networks are quite robust to failure, with large bicomponents persisting until almost all vertices have been removed.     

Efficiency and robustness in ant networks of galleries

Recent theoretical and empirical studies have focused on the topology of large networks of communication/interactions in biological, social and technological systems. Most of them have been studied in the scope of the small-world and scale-free networks theory. Here we analyze the characteristics of ant networks of galleries produced in a 2-D experimental setup. These networks are neither small-worlds nor scale-free networks and belong to a particular class of network, i.e. embedded planar graphs emerging from a distributed growth mechanism. We compare the networks of galleries with both minimal spanning trees and greedy triangulations. We show that the networks of galleries have a path system efficiency and robustness to disconnections closer to the one observed in triangulated networks though their cost is closer to the one of a tree. These networks may have been prevented to evolve toward the classes of small-world and scale-free networks because of the strong spatial constraints under which they grow, but they may share with many real networks a similar trend to result from a balance of constraints leading them to achieve both path system efficiency and robustness at low cost.Received: 16 July 2004, Published online: 26 November 2004PACS: 89.75.Fb Structures and organization in complex systems - 89.75.Hc Networks and genealogical trees - 87.23.Ge Dynamics of social systems     

Optimization of robustness and connectivity in complex networks

Scale-free networks rely on a relatively small number of highly connected nodes to achieve a high degree of interconnectivity and robustness to random failure, but suffer from a high sensitivity to directed attack. In this paper we describe a parameterized family of networks and analyze their connectivity and sensitivity, identifying a network that has an interconnectedness closer to that of a scale-free network, a robustness to attack closer to that of an exponential network, and a resistance to failure better than that of either of those networks.     

Structural and robustness properties of smart-city transportation networks

The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation system becomes an important topic in the process of city construction. In this work, we study the structural and robustness properties of transportation networks and their sub-networks. We introduce a complementary network model to study the relevance and complementarity between bus network and subway network. Our numerical results show that the mutual supplement of networks can improve the network robustness. This conclusion provides a theoretical basis for the construction of public traffic networks, and it also supports reasonable operation of managing smart cities.     

Dynamical robustness of networks based on betweenness against multi-node attack

We explore the robustness of a network against failures of vertices or edges where a fraction f of vertices is removed and an overload model based on betweenness is constructed. It is assumed that the load and capacity of vertex i are correlated with its betweenness centrality B_i as B_i~θ and(1 + α)Bθi(θ is the strength parameter, α is the tolerance parameter).We model the cascading failures following a local load preferential sharing rule. It is found that there exists a minimal αc when θ is between 0 and 1, and its theoretical analysis is given. The minimal αc characterizes the strongest robustness of a network against cascading failures triggered by removing a random fraction f of vertices. It is realized that the minimalαc increases with the increase of the removal fraction f or the decrease of average degree. In addition, we compare the robustness of networks whose overload models are characterized by degree and betweenness, and find that the networks based on betweenness have stronger robustness against the random removal of a fraction f of vertices.     

Enhancing robustness and synchronizability of networks homogenizing their degree distribution

A new family of networks, called entangled, has recently been proposed in the literature. These networks have optimal properties in terms of synchronization, robustness against errors and attacks, and efficient communication. They are built with an algorithm which uses modified simulated annealing to enhance a well-known measure of networks’ ability to reach synchronization among nodes. In this work, we suggest that a class of networks similar to entangled networks can be produced by changing some of the connections in a given network, or by just adding a few connections. We call this class of networks weak-entangled. Although entangled networks can be considered as a subset of weak-entangled networks, we show that both classes share similar properties, especially with respect to synchronization and robustness, and that they have similar structural properties.     

Improving robustness of complex networks via the effective graph resistance

Improving robustness of complex networks is a challenge in several application domains, such as power grids and water management networks. In such networks, high robustness can be achieved by optimizing graph metrics such as the effective graph resistance, which is the focus of this paper. An important challenge lies in improving the robustness of complex networks under dynamic topological network changes, such as link addition and removal. This paper contributes theoretical and experimental findings about the robustness of complex networks under two scenarios: (i) selecting a link whose addition maximally decreases the effective graph resistance; (ii) protecting a link whose removal maximally increases the effective graph resistance. Upper and lower bounds of the effective graph resistance under these topological changes are derived. Four strategies that select single links for addition or removal, based on topological and spectral metrics, are evaluated on various synthetic and real-world networks. Furthermore, this paper illustrates a novel comparison method by considering the distance between the added or removed links, optimized according to the effective graph resistance and the algebraic connectivity. The optimal links are different in most cases but in close proximity.     

Improving robustness of active noise control in ducts

A robust active noise controller (ANC) is proposed here for finite ducts. While the H(infinity) control theory provides theoretical ground and numerical algorithms to design robust controllers, it is important for an engineer to design and formulate a robust controller so that the objective is more achievable and the H(infinity) constraints less restrictive without sacrificing robustness. A new robust ANC is designed this way with an extra actuator to improve achievable performance and introduce more degrees of freedom to controller parameters. The new strategy relaxes H(infinity) constraints without sacrificing robustness and enables the ANC to tolerate a wide variety of errors and uncertainties including truncation errors between a finite model and an infinite field. Theoretical analysis, numerical examples, and experimental results are presented to demonstrate the improved performance of the proposed ANC when subject to a certain level of uncertainties in a duct.     

Optimization of controllability and robustness of complex networks by edge directionality

Recently, controllability of complex networks has attracted enormous attention invarious fields of science and engineering. How to optimize structural controllability hasalso become a significant issue. Previous studies have shown that an appropriatedirectional assignment can improve structural controllability; however, the evolution ofthe structural controllability of complex networks under attacks and cascading has alwaysbeen ignored. To address this problem, this study proposes a new edge orientation method(NEOM) based on residual degree that changes the link direction while conserving topologyand directionality. By comparing the results with those of previous methods in two randomgraph models and several realistic networks, our proposed approach is demonstrated to bean effective and competitive method for improving the structural controllability ofcomplex networks. Moreover, numerical simulations show that our method is near-optimal inoptimizing structural controllability. Strikingly, compared to the original network, ourmethod maintains the structural controllability of the network under attacks andcascading, indicating that the NEOM can also enhance the robustness of controllability ofnetworks. These results alter the view of the nature of controllability in complexnetworks, change the understanding of structural controllability and affect the design ofnetwork models to control such networks.     

Improving robustness of complex networks by a new capacity allocation strategy

The robustness of infrastructure networks has attracted great attention in recent years. Scholars have studied the robustness of complex networks against cascading failures from different aspects. In this paper, a new capacity allocation strategy is proposed to reduce cascading failures and improve network robustness without changing the network structure.Compared with the typical strategy proposed in Motter–Lai(ML) model, the new strategy can reduce the scale of cascading failure. The new strategy applied in scale-free network is more efficient. In addition, to reasonably evaluate the two strategies, we introduce contribution rate of unit capacity to network robustness as evaluation index. Results show that our new strategy works well, and it is more advantageous in the rational utilization of capacity in scale-free networks.Furthermore, we were surprised to find that the efficient utilization of capacity costs declined as costs rose above a certain threshold, which indicates that it is not wise to restrain cascading failures by increasing capacity costs indefinitely.     

Optimal intentional islanding to enhance the robustness of power grid networks

Intentional islanding of a power system can be an emergency response for isolating failures that might propagate and lead to major disturbances. Some of the islanding techniques suggested previously do not consider the power flow model; others are designed to minimize load shedding only within the islands. Often these techniques are computationally expensive. We aim to find approaches to partition power grids into islands to minimize the load shedding not only in the region where the failures start, but also in the topological complement of the region. We propose a new constraint programming formulation for optimal islanding in power grid networks. This technique works efficiently for small networks but becomes expensive as size increases. To address the scalability problem, we propose two grid partitioning methods based on modularity, properly modified to take into account the power flow model. They are modifications of the Fast Greedy algorithm and the Bloom algorithm, and are polynomial in running time. We tested these methods on the available IEEE test systems. The Bloom type method is faster than the Fast Greedy type, and can potentially provide results in networks with thousands of nodes. Our methods provide solutions which retain at least 40–50% of the system load. Overall, our methods efficiently balance load shedding and scalability.     

Effect of edge removal on topological and functional robustness of complex networks

We study the robustness of several network models subject to edge removal. The robustness is measured by the statistics of network breakdowns, where a breakdown is defined as the destroying of the total connectedness of a network, rather than the disappearance of the giant component. We introduce a simple traffic dynamics as the function of a network topology, and the total connectedness can be destroyed in the sense of either the topology or the function. The overall effect of the topological breakdown and the functional breakdown, as well as the relative importance of the topological robustness and the functional robustness, are studied under two edge removal strategies.     

An effective method to improve the robustness of small-world networks under attack

In this study, the robustness of small-world networks to three types of attack is investigated. Global efficiency is introduced as the network coefficient to measure the robustness of a small-world network. The simulation results prove that an increase in rewiring probability or average degree can enhance the robustness of the small-world network under all three types of attack. The effectiveness of simultaneously increasing both rewiring probability and average degree is also studied, and the combined increase is found to significantly improve the robustness of the small-world network.Furthermore, the combined effect of rewiring probability and average degree on network robustness is shown to be several times greater than that of rewiring probability or average degree individually. This means that small-world networks with a relatively high rewiring probability and average degree have advantages both in network communications and in good robustness to attacks. Therefore, simultaneously increasing rewiring probability and average degree is an effective method of constructing realistic networks. Consequently, the proposed method is useful to construct efficient and robust networks in a realistic scenario.