A self-adaptive method of task allocation in clustering-based MANETs

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.