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SUMMARY Coalition is an essential mechanism in the multi-agent systems in the research of task-oriented area. Self-interested agents coordinate their behaviors in a coalition to pursue a common goal and obtain payoffs. We propose the clustering-based coalition formation and self-adjustment mechanisms for tasks in the wireless sensor network. Before coalition formation, the management center clusters attributes of sensors to reduce the scale of searching space during coalition formation. And then an improved MAX-MIN ant colony optimization algorithm is adopted to resolve the problem of coalition formation. If a coalition member fails to fulfill a task, it can sponsor a negotiation with some noncoalition nodes to execute coalition self-repairing autonomously. The stimulus-response mechanism of wasp colony is introduced to determine the probability of response to the task invitation to avoid consuming extra energy. Simulation results show that our model efficiently reduces energy consumption and network traffic, decreases the number of dead nodes, and prolongs the lifetime of the networks. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY As the computer network increasingly grows larger and more complex, fault diagnosis has become a challenging task. Active probing is an efficient tool for fault localization. By implementing some test programs and analyzing the results, active-probing-based techniques can perform diagnosis efficiently and adaptively. Because probes may generate additional traffic overhead, it is important to appropriately select small number of probes to reach the desired diagnostic capability. However, the computation of probe selection problem in such environment is extremely expensive. Most of the past works purchase the speed at the cost of diagnostic accuracy. In this paper, we first verify that probe selection problem satisfies the property of submodularity. Then we take the use of the property and develop a submodularity-based selection algorithm with following novel features: (i) it is cost effective, failure resistant and more accurate; (ii) it could deal with the uncertainties about the network structures and the observations; and (iii) it can select the required probes in near-linear time. Finally, we implement submodularity-based selection algorithm and other two representative probe selection algorithms (bounded path enumeration approximation algorithm and greedy search algorithm) on different settings of networks. The results have shown how the new algorithm outperforms the former two algorithms. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]