异构有向传感器网络连通覆盖调度算法

Connected Coverage Scheduling Algorithm for Heterogeneous Directional Sensor Networks

在面向目标监测的有向传感器网络中,为满足监测目标的不同监测要求,并保持网络连通前提下网络寿命最大化,提出了一种基于增强珊瑚礁算法的节点调度算法。受集合覆盖的启发,以增强珊瑚礁算法为工具求解满足连通覆盖要求的集合。增强珊瑚礁算法采用SOBOL序列和反向学习策略对种群进行初始化,同时在非性繁殖过程中,借鉴和声搜索、生物地理学算法和自适应变异策略的差分进化算法达到继承种群的优秀解和增强子代的优化能力的目的。再者,对种群的最差个体执行随机反向学习和与最优个体差分策略以提升最差个体的优化能力。在数值测试以及在传感器网络节点调度方面的仿真结果表明,改进珊瑚礁算法的性能优于其他算法,证明了改进算法的有效性。

A node scheduling algorithm based on enhanced version of coral reef optimization algorithm(shortly for ECRO) is proposed to solve the life maximization problem of heterogeneous directional sensor networks for connectivity and differentiated target coverage requirements. Based on cover sets theory, ECRO is utilized to get the cover sets, which can cover all the targets and satisfy their connectivity and coverage quality requirements. The improvement of coral reef optimization(shortly for CRO) lies in the three aspects. Firstly, the population is initialized by the SOBOL sequence and an opposition learning strategy. Secondly the operator of harmony search algorithm, immigration in biogeography-based optimization and a self-adaptive mutation strategy in differential evolution algorithm are introduced into the brooding procedure of the coral larvae formation to conserve the excellent solutions of the population and enhance the diversity of the descent and the ability of optimization for coral reef. Moreover, an opposition learning strategy and differential strategy with the optimal individual are utilized to improve the performance of the worst individual of the population. Extensive simulation experiments both in numerical benchmark functions and node scheduling are conducted to validate the proposed ECRO. The results show that the proposed ECRO outperforms the compared algorithms, which demonstrate the superiority of the proposed algorithm ECRO.