基于学习自动机的异构有向传感器节点调度算法

Scheduling Algorithm for Heterogeneous Directed Sensor Nodes Based on Learning Automata

现有有向传感器网络调度算法多数只面向同构传感器节点,而未考虑节点异构性对算法性能的影响。为此,提出一种基于学习自动机的异构有向节点调度算法。将节点调度问题转化为集合覆盖问题后,利用学习自动机的特性自适应地更新所选取感知方向的概率,从而构建多个满足条件的节点覆盖集合。仿真结果表明,与贪婪算法相比,该算法能有效减少能量消耗并延长网络寿命。

Most existing directed sensor network scheduling algorithms only focus on isomorphic sensor nodes,without considering the influence of node heterogeneity on the performance of the algorithm. To solve this problem,a heterogeneous nodes scheduling algorithm based on learning automata is proposed. The node scheduling problem is transformed into a set coverage problem,and the probability of the selected perception direction is adaptively updated by using the characteristics of learning automata to construct a number of node coverage sets with satisfying conditions.Simulation results show that compared with greedy algorithm,the proposed algorithm can effectively reduce energy consumption and prolong network lifetime.