Lan-Lan Rui

Publications (2)0 Total impact

• Conference Proceeding: A self-adaptive method of task allocation in clustering-based MANETs

  Yang Yang, Xue-song Qiu, Luo-ming Meng, Lan-lan Rui
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  ABSTRACT: In a clustering-based MANETs, task allocation has posed increasing research challenges because the needs of management and coordination are accentuated by complicated demands of cluster members. A self-adaptive method of task allocation is designed to facilitate self-planning and self-negotiation for nodes during tasks being distributed and executed. The method is composed of two parts: for one part, the cluster head works out an integrated schedule for tasks, including selecting different sets of execution nodes and defining their functions according to task types. Cooperative group towards synergetic task is formed by policies of filtering and voting. Assignment modes based on either polling or mobile agents are also involved, the latter adopts an improved Ant Colony Optimization (ACO) algorithm to plan a migration path. For another, if a cluster member fails to accomplish a task, it could negotiate as a tenderee with other nodes using a revised contract net protocol. In addition, we employ a stimulation mechanism of distributing virtual task experience in connection with QoS guarantees to offer compensation for nodes' energy consumption and extra load. Simulation results demonstrate performance benefits of our self-adaptive method can efficaciously alleviate load of the cluster head, balance loads of nodes in consideration of energy restriction, and prolong the lifecycle of the cluster.
  Network Operations and Management Symposium (NOMS), 2010 IEEE; 05/2010
• Conference Proceeding: A self-optimization of the fault management strategy for device software

  Shao-Yong Guo, Xing Gao, Lan-Lan Rui, Luo-Ming Meng
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  ABSTRACT: With the growth of network technologies, abundance of network resources, and increase of various services, mobile devices have gained much functionality and intelligence. At the same time, mobile devices are becoming complicated and many software related problems appear. The traditional remote repair method needs the software providers to supply fault information with corresponding repair strategy. It is inconvenient for users when the sold mobile devices have software faults. However, it is impossible for the manufacturers to supply all the fault information and repair-strategy before selling them. So far, no method has been given to collect repair-strategy from the sold mobile device and optimize the self-repair strategy. In this paper, we propose a self-optimization method to learn the software repair strategy from the sold mobile devices and to optimize self-repair strategy based on the Open Mobile Alliance (OMA) Device Management (DM) standard. The managed objects (MOs) are defined for collecting the strategy data and the self-optimization algorithm is proposed and implemented at the central server.
  Machine Learning and Cybernetics, 2009 International Conference on; 08/2009