Robustness Of Network Topology Research Articles

TEAM: A Layered-Cooperation Topology Evolution Algorithm for Multi-Sink Internet of Things

Numerous sensor nodes deployed in the Internet of Things (IoT) can form a large heterogeneous network. The increased energy consumption of sensor nodes and the unbalanced communication load on multiple sink nodes reduce the energy efficiency of the network. Moreover, frequent network attacks also pose severe challenges to topology robustness. Optimizing the network topology to achieve the balance between energy efficiency and robustness is a complex problem. Multi-objective heuristic algorithms based on genetic evolution are commonly used to solve joint optimization problems. However, due to the lack of global search ability caused by the loss of genetic diversity, genetic operations are prone to premature convergence during multi-objective evolution. Therefore, this paper introduces multi-population cooperation into the multi-objective evolution process and proposes a novel layered-cooperation Topology Evolution Algorithm for Multi-sink IoT (TEAM). In TEAM, information entropy is used to measure the effectiveness of load balancing on multiple sink nodes. The crossover and mutation probabilities of different populations are dynamically adjusted to ensure genetic diversity. A layered-cooperation mechanism is designed to avoid premature convergence. Extensive experiments confirm that TEAM can effectively improve the energy efficiency and robustness of network topology while balancing the communication load on multi-sink nodes.

SmartTRO: Optimizing topology robustness for Internet of Things via deep reinforcement learning with graph convolutional networks

The reliability problems caused by random failure or malicious attacks in the Internet of Things (IoT) are becoming increasingly severe, while a highly robust network topology is the basis for highly reliable Quality of Service (QoS). Therefore, improving the robustness of the IoT against cyber-attacks by optimizing the network topology becomes a vital issue. Heuristic algorithms as the mainstream idea to solve the network robustness optimization, but their high computational cost cannot meet the timeliness requirements of real IoT scenarios. This paper proposes a Smart Topology Robustness Optimization (SmartTRO) algorithm based on Deep Reinforcement Learning (DRL). First, we design a rewiring operation as an evolutionary behavior in IoT network topology robustness optimization, which achieves topology optimization at a low cost without changing the degree of all nodes. Then, SmartTRO learns the evolutionary behavior characteristics of IoT network topology by combining Graph Convolutional Network (GCN) and policy network, where the training of neural network parameters is completed by DRL. Experimental results demonstrate that SmartTRO improves the ability of IoT topology to resist cyber-attacks effectively and outperforms the state-of-the-art heuristic algorithm in terms of both topology robustness optimization performance and computational cost.

Isolated Rupture in Composite Networks

Computer networks are prone to targeted attacks and random failures. Robustness is a measure of an ability of a network to continue functioning when part of the network is either naturally damaged or targeted for attack. The study of network robustness is a critical tool in the characterization and understanding of complex interconnected systems. There are several proposed graph metrics that predicates network resilience against such attacks. Isolated rupture degree is a novel graph-theoretic concept defined as a measure of network vulnerability. Isolated rupture degree is argued as an appropriate measure for modelling the robustness of network topologies in the face of possible node destruction. In this paper, the relationships between isolated rupture degree and some other graph parameters such as connectivity, covering number, minimum vertex degree are established. The isolated rupture degrees of [Formula: see text]-free graphs, middle graphs, corona graphs of a middle graph and a complete graph [Formula: see text] on two vertices are evaluated, then compared and the more stable graph types are reported. A sharp upper bound for the isolated rupture degree of middle graphs is established.

Towards Minimizing the $R$ Metric for Measuring Network Robustness

With the prevalence of cyber-attacks on infrastructure networks such as the Internet backbone, measuring the robustness of network topologies has become a critical issue. To this end, a metric called ‘R’ has been proposed and widely used. `R assumes that an attack on a network consists of n rounds of node removals with one node removed each time, where n is the number of nodes in the network. Since the value of R depends on the sequence of node removals, finding the sequence minimizing R becomes an interesting problem, denoted as Min-R hereafter. Although many heuristic approaches have been proposed for Min-R, there has been no optimal polynomial-time algorithm given so far, nor is Min-R proved NP-hard. To fill this gap, this paper presents the following fundamental results. First, to minimize R, a node always needs to be removed from the largest connected component of the current network in each round. Second, the Min-R problem has an Optimal Substructure, which allows it to be solved by Dynamic Programming in exponential time instead of the naive factorial time on general graphs. Finally, an efficient and optimal logarithmic-time algorithm is given for Min-R on path graphs, with its complexity and optimality proved.

Robust Networking: Dynamic Topology Evolution Learning for Internet of Things

The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.

REFIT: Robustness Enhancement Against Cascading Failure in IoT Networks

There has been tremendous growth in the Internet of Things (IoT) technologies, and many new applications have emerged. However, cascading failure as one of the major issues in such constrained networks have been neglected. In this paper, we apply an effective clustering approach dubbed as REFIT to enhance network topology robustness via nodes’ residual energy. The REFIT protocol divides the network processes into two stages, (i) set-up state and (ii) steady state. The Cluster Head (CH) selection method determines the supreme set of CHs that balances load distribution. The routing method is developed with the modified Particle Swarm Optimization (PSO) algorithm and the objective function to find the supreme set of Relay Nodes (RNs). These complete methods are combined into a set-up state to construct an optimal routing tree that links these CHs to the sink via RNs. In steady state, we model the routing tree to Conditional Directed Acyclic Graph (C-DAG) infrastructure that leads to shortcut routes. Simulation results on MATLAB Simulink have demonstrated that compared with the state-of-the-art works, REFIT can significantly promote network robustness against cascading failure.

A Data-Driven Robustness Algorithm for the Internet of Things in Smart Cities

The Internet of Things has been applied in many fields, especially in smart cities. The failure of nodes brings a significant challenge to the robustness of topologies. The IoT of smart cities is increasingly producing a vast amount different types of data, which includes the node's geographic information, neighbor list, sensing data, and so on. Thus, how to improve the robustness of topology against malicious attacks based on big data of smart cities becomes a critical issue. To tackle this problem, this article proposes an approach to improve the robustness of network topology based on a multi-population genetic algorithm (MPGA). First, the geographic information and neighbor list of nodes are extracted from a big data server. Then a novel MPGA with a crossover operator and a mutation operator is proposed to optimize the robustness of topology. Our algorithm keeps the initial degree of each node unchanged such that the optimized topology will not increase the energy cost of adding edges. The extensive experiment results show that our algorithm can significantly improve the robustness of topologies against malicious attacks.

On the Interplay of Network Structure and Routing Strategies on Network Design Methods for Mitigation of Intentional Attacks in Scale-Free Networks

Network robustness and network reliability are important issues in the design of Internet Service Provider's topologies. In this paper, we examine the structural characteristics of network topologies that affect robustness and reliability according to various routing strategies. We examine the interplay between the structural characteristics of network topologies and routing strategies on network robustness and reliability according to resource capacity over-provisioning strategies when the network breakdowns subject to practical constraints (router technology) and economic considerations (link costs). We study the robustness of the Internet connectivity under node intentional harmful attack using two attack strategies: static degree-based and static load-based. We find that the robustness of network topologies is affected by the variation of the interplay between their structural characteristics and the routing strategies. In our proposed approach, we show that highly-heterogeneous topologies have less robustness compared with lightly-heterogeneous topologies. The observations from the robustness study provide us useful insights for proposing multiple efficient preventive resource capacity over-provisioning strategies for mitigation of intentional attacks. The proposed strategies utilize the structural properties by calculating the excess traffic in case of single global cascading failure for each node and measure its influence on the other nodes as well as locally. The results show that our proposed strategies can significantly enhance the robustness and increase the resilience of network topology according to the specific used routing method. Due to our proposed approach results, we also show that highly-heterogeneous topologies have high resilience compared with lightly-heterogeneous topologies.

Quantifying all-to-one network topology robustness under budget constraints

To design robust network topologies that resist strategic attacks, one must first be able to quantify robustness. In a recent line of research, the theory of network blocking games has been used to derive robustness metrics for topologies. However, these previous works did not consider the budget constraints of the network operator. In this paper, we introduce a budget limit on the operator and study two budget constraint formulations: the maximum and the expected cost constraints. For practical applications, the greatest challenge posed by blocking games is their computational complexity. Therefore, we show that the expected cost constraint formulation leads to games that can be solved efficiently, while the maximum cost constraint leads to NP-hard problems. As an illustrative example, this paper discusses the particular case of All-to-One (e.g., sensor or access) networks.

A scoring mechanism for the rank aggregation of network robustness

To date, a number of metrics have been proposed to quantify inherent robustness of network topology against failures. However, each single metric usually only offers a limited view of network vulnerability to different types of random failures and targeted attacks. When applied to certain network configurations, different metrics rank network topology robustness in different orders which is rather inconsistent, and no single metric fully characterizes network robustness against different modes of failure. To overcome such inconsistency, this work proposes a multi-metric approach as the basis of evaluating aggregate ranking of network topology robustness. This is based on simultaneous utilization of a minimal set of distinct robustness metrics that are standardized so to give way to a direct comparison of vulnerability across networks with different sizes and configurations, hence leading to an initial scoring of inherent topology robustness. Subsequently, based on the inputs of initial scoring a rank aggregation method is employed to allocate an overall ranking of robustness to each network topology. A discussion is presented in support of the presented multi-metric approach and its applications to more realistically assess and rank network topology robustness.

Network design methods for mitigation of intentional attacks in scale-free networks

Network robustness and network reliability are important issues in the design of Internet Service Providers' topologies. In this paper, we examine the structural characteristics of network topologies that affect robustness and reliability. We examine the interplay between the structural characteristics of network topologies and the resource capacity over-provisioning strategies when the network breakdowns subject to practical constraints (router technology) and economic considerations (link costs). We study the robustness of the Internet connectivity under node intentional harmful attack using two attacks strategies: static degree-based and static load-based. We find that the robustness of network topologies is affected by the variation of their structural characteristics. In our proposed approach, we show that highly-heterogeneous topologies have less robustness compared with lightly-heterogeneous topologies. The observations from the robustness study provide us useful insights for proposing multiple efficient preventive resource capacity over-provisioning strategies for mitigation of intentional attacks. The proposed strategies utilize the structural properties by calculating the excess traffic in case of single global cascading failure for each node and measure its influence on the other nodes as well as locally. The results show that our proposed strategies can significantly enhance the robustness and increase the resilience of network topology. We also show that highly-heterogeneous topologies have high resilience compared with lightly-heterogeneous topologies. By using real data from the Sprint network at the router level, we provide further empirical evidence in support of the proposed approach.