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中文摘要：
任务分配是高性能计算领域中的一个广泛研究的经典问题，然而，传感器网络资源严重受限，现有的算法不能直接应用．提出一种基于遗传算法的嵌套优化技术，在多跳聚簇网络中进行能源高效的任务分配．一般化的优化目标既可以满足应用的实时性要求，也可以实现能源的高效性．优化解通过结合基于遗传算法的任务映射、路由路径分配、任务调度以及动态电压调制（dynamic voltage scaling，简称DVS）这几个过程而获得．随机产生任务图模拟实验，结果表明，嵌套优化技术与随机优化技术相比，具有较好的实时性和能源高效性．
英文摘要：
Task allocation is a typical problem in the area of high performance computing and has been extensively studied in the past. However, existing algorithms cannot be directly used in WSN （wireless sensor network） due to severe energy constraint. A nested optimization technique based on genetic algorithm is proposed for energy-efficient task allocation in multi-hop clusters. The general optimization object can meet application＇s real-time requirement while realizing energy efficiency. Optimal solution can be achieved by incorporating GA-based task mapping, GA-based routing, communication scheduling and dynamic voltage scaling （DVS）. Performance is evaluated through simulations with randomly generated task graphs and simulation results show better solution in terms of real-time and energy-efficiency compared with random optimization techniques.
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