This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly,...This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly, the analysis of existing energy consuming in the sensing layer and its calculation method were provided to build the energy conserving objective function;What’s more, the other two indicators in target tracking, including target detection probability and tracking accuracy, were combined to be regarded as the constraints of the energy conserving objective function. Fourthly, the three energy conserving approaches, containing optimizing the management scheme, prolonging the time interval between two adjacent observations, and transmitting the observations selectively, were introduced;In addition, the improved lion algorithm combined with the Logistic chaos sequence was proposed to obtain sensor management schemes. Finally, simulations had been made to prove the effectiveness of the proposed methods and algorithm.展开更多
最优线程数设置是影响多线程程序性能和功耗的关键之一。然而,目前寻找最优线程数的算法通常是从单一固定起点开始搜索,往往会造成搜索精度低、搜索开销大的问题。最优线程数的分布和位置与多种因素有关,包括程序所属类型、优化目标(性...最优线程数设置是影响多线程程序性能和功耗的关键之一。然而,目前寻找最优线程数的算法通常是从单一固定起点开始搜索,往往会造成搜索精度低、搜索开销大的问题。最优线程数的分布和位置与多种因素有关,包括程序所属类型、优化目标(性能、功耗和EDP(Energy-delay Product))、并行的多线程区域、软硬件配置参数等。围绕能效优先的最优线程数搜索问题,提出了能效优先的特定起点分类最优线程数搜索算法(Energy-Efficiency-First Optimal Thread Number Search Algorithm based on Specific Starting Point Classification,简称TS^(3)方法)”,通过设计基于程序分类的特殊起点设定方法来确定搜索起点,并采用启发式算法和二分查找方法搜索最优线程数,提升搜索效率,有效提升了能效优先目标(性能最优、功耗最优、能效EDP最优)下的最优线程数搜索精度并降低了搜索开销。在两个x86和一个ARM平台上用8个benchmark对算法有效性进行了详细实验验证,结果表明,与Baseline相比,TS^(3)方法的性能平均提升0.29%(平台A)、0.17%(平台B)、10.77%(平台C);功耗平均降低2.35%(平台A)、1.87%(平台B)、15.97%(平台C);EDP平均降低6.36%(平台A)、5.07%(平台B)、46.94%(平台C)。在3个平台上,与目前经典搜索方法相比,TS^(3)方法的性能平均提升10.16%,功耗平均降低13.45%,EDP平均降低23.77%;搜索开销平均降低86.8%。展开更多
基金funded by (Defense Pre-Research Fund Project of China), grant number 012015012600A2203NSFC (Natural Science Foundation of China), grant number 61573374。
文摘This paper mainly studied the problem of energy conserving in wireless sensor networks for target tracking in defensing combats. Firstly, the structures of wireless sensor nodes and networks were illustrated;Secondly, the analysis of existing energy consuming in the sensing layer and its calculation method were provided to build the energy conserving objective function;What’s more, the other two indicators in target tracking, including target detection probability and tracking accuracy, were combined to be regarded as the constraints of the energy conserving objective function. Fourthly, the three energy conserving approaches, containing optimizing the management scheme, prolonging the time interval between two adjacent observations, and transmitting the observations selectively, were introduced;In addition, the improved lion algorithm combined with the Logistic chaos sequence was proposed to obtain sensor management schemes. Finally, simulations had been made to prove the effectiveness of the proposed methods and algorithm.
文摘最优线程数设置是影响多线程程序性能和功耗的关键之一。然而,目前寻找最优线程数的算法通常是从单一固定起点开始搜索,往往会造成搜索精度低、搜索开销大的问题。最优线程数的分布和位置与多种因素有关,包括程序所属类型、优化目标(性能、功耗和EDP(Energy-delay Product))、并行的多线程区域、软硬件配置参数等。围绕能效优先的最优线程数搜索问题,提出了能效优先的特定起点分类最优线程数搜索算法(Energy-Efficiency-First Optimal Thread Number Search Algorithm based on Specific Starting Point Classification,简称TS^(3)方法)”,通过设计基于程序分类的特殊起点设定方法来确定搜索起点,并采用启发式算法和二分查找方法搜索最优线程数,提升搜索效率,有效提升了能效优先目标(性能最优、功耗最优、能效EDP最优)下的最优线程数搜索精度并降低了搜索开销。在两个x86和一个ARM平台上用8个benchmark对算法有效性进行了详细实验验证,结果表明,与Baseline相比,TS^(3)方法的性能平均提升0.29%(平台A)、0.17%(平台B)、10.77%(平台C);功耗平均降低2.35%(平台A)、1.87%(平台B)、15.97%(平台C);EDP平均降低6.36%(平台A)、5.07%(平台B)、46.94%(平台C)。在3个平台上,与目前经典搜索方法相比,TS^(3)方法的性能平均提升10.16%,功耗平均降低13.45%,EDP平均降低23.77%;搜索开销平均降低86.8%。
