面向多目标探测的无线传感器网络资源调度方法

Resource Scheduling Method for Multi-Target Detection-Oriented Wireless Sensor Networks

针对传统无线传感器网络在协同探测跟踪目标过程中,资源调度模型要素考虑不周全、求解算法寻优能力弱、收敛速度慢等问题,首先,综合考虑了影响无线传感器探测网络效能的约束条件,设计了优化调度指标评价体系,并提出了指标描述的量化方法;接着,在此基础上以传感器探测网络效能收益最大为原则,建立了完善的资源调度模型;最后,通过引入混沌映射思想、正余弦算法、自适应权重因子,对象群优化算法进行了改进,并应用到模型的求解当中。仿真结果表明:所提出的资源优化调度方案能够有效提升无线传感器资源的利用率,并能选取合适的传感器资源来完成探测任务;改进后的象群优化算法与基本象群优化算法相比,迭代次数减少了41次,求解精度提高了124%,运行时间缩短了57%。

For the traditional wireless sensor networks,it is found that,during the collaborative detection and tracking of targets,the elements in the resource scheduling model are not well-considered,the solution algorithm is weak in finding the optimal,and the convergence speed is slow.For these problems,and considering the constraints affecting the effectiveness of the wireless sensor detection network,the optimization scheduling index evaluation system was designed and the method to quantify the index description was proposed.Then,an improved resource scheduling model was built based on the principle of maximizing the effectiveness of the sensor detection network.At last,the elephant herding optimization algorithm was improved by introducing the chaotic mapping idea,positive cosine algorithm,and adaptive weighting factor and applied to solve the model.The simulation results show that the proposed resource scheduling model has been improved and applied to the model solution.The simulation results show that the proposed resource optimization scheduling scheme can increase the utilization rate of wireless sensor resources effectively and select the suitable sensor resources to complete the detection tasks,and that the improved elephant herding optimization algorithm reduces the number of iterations by 41 times,improves the solution accuracy by 124%,and shortens the running time by 57%,compared with the basic elephant herding optimization algorithm,effectively improving the solution accuracy and speed of the algorithm.