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This paper benchmarks the key performance measures of the hub airports in Southeast Asia. Our investigation suggests that (1) Hong Kong has been a leader in terms of network connectivity, traffic growth, hub airline developments, and cargo logistics. (2) Bangkok airport's growth has been constrained by political instability and weak hub airline development; in the long-run, the airport, however, has great potential. (3) The development of low-cost carriers have become a major driver for traffic growth, but the implications to airport connectivity have been unclear. (4) Despite limited progress on regional liberalization, intra-Asian routes have clearly contributed to traffic growth and hub airport connectivity. (5) Governments should safeguard airline competition by promoting market liberalization and airport capacity investments.

Identifying critical nodes in complex networks has gained increasing attention in recent years. However, how to design an algorithm that has low computational complexity but can accurately identify important network nodes is still a challenge. Considering the role of structural holes in shaping communication channels, this paper presents an effective method based on local characteristics to identify critical nodes that play important roles in maintaining network connectivity. Our method considers the connections of a node as well as the connectivity of the neighborhood of the node. Through numerical simulations on various real-world networks, we have demonstrated that the proposed approach outperforms some other well-known heuristic algorithms in identifying vital nodes and leads to faster network collapse in target destruction.

Wireless sensor networks (WSNs) provide acceptable low cost and efficient deployable solutions to execute the target tracking, checking and identification of substantial measures. The primary step necessary for WSN is to organize all the sensor nodes in their positions to build up an effective network. In the sensor deployment model, Target COVerage (TCOV) and Network CONnectivity (NCON) are the basic issues in WSNs that have obtained significant consideration in sensor deployment. This paper intends to develop an intelligent context awareness algorithm for sensor deployment process in WSN. Accordingly, the process is divided into two phases. In the first phase, the TCOV process is performed, whereas the second phase of the algorithm establishes NCON among the sensors. An objective model to meet both TCOV and NCON is formulated as a minimization problem. The problem is solved using FireFly (FF) optimization to determine the optimal locations for sensors. It leads to an intelligent sensor deployment model that can determine the optimal locations for the sensors in the WSN. Further, the proposed FF-TCOV and FF-NCON models are compared against the conventional algorithms, namely, genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution and evolutionary algorithm-based TCOV and NCON models. The results achieved from the simulation show the improved performance of the proposed technique.

Studies have revealed that some topological properties of complex networks are robust to random node failures, but highly sensitive to failures in nodes of high centrality (i.e., attacks). This work proposes mechanisms based on local information for detecting vulnerable network configurations and for promoting changes in the topology to mitigate the impact on network connectivity of successive node losses due to attacks and failures. Two scenarios were evaluated: with maintenance of the number of links in the network, and with the creation of new links. For the first case, we show that the removal of the most central nodes affects networks in such a way that is also difficult to preserve their main topological properties, although improvement in the values of global efficiency at the expense of a reduction of the local efficiency was observed. For the second scenario, there was a significant decrease in the impact from attacks and failures. Notably, for failures the connectivity properties not only remained almost unchanged, but in some cases considerably increased, thus improving the overall network performance. The results were also verified in some benchmark real-network topologies, and a comparative performance evaluation with a random self-regenerating process was also analyzed. The study demonstrates the importance and feasibility of local adaptation mechanisms for link rewiring based on the concept of vulnerability.