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Modeling Mobile-Agent-based Collaborative Processing in Sensor Networks Using Generalized Stochastic Petri Nets

机译:使用广义随机培养网在传感器网络中建模基于移动代理的协作处理

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In mobile-agent-based distributed sensor networks (MADSNs), instead of moving data from an individual sensor node to a processing center as a typical scenario in the client/server-based computing, mobile agents on that carry the executable code are dispatched from the processing center to the sensor nodes and process data locally. Because of the complicated behavior of the mobile agents, there has not been much work done in modeling and simulation. In this paper, a Generalized Stochastic Petri Net (GSPN) is used to model the mobile agents in DSNs. GSPN is a very popular modeling tool for systems that feature concurrency, synchronization and randomness. One of the most challenging problems in GSPN modeling is to design a mechanism for breaking the transition conflicts. This paper presents a random transition selector based on joint entropy and a so-called "rolling rocks random (R3) selector" to break conflicts among immediate transitions. The GSPN model based on this transition selector is synthesized on a Xilinx Virtex 1000E Field Programmable Gate Array (FPGA) using reconfigurable components. Simulation results show that the proposed transition selector performs better than the commonly used random selector.
机译:在基于移动代理的分布式传感器网络(MADSNS)中,而不是将数据从单独的传感器节点移动到处理中心,作为基于客户端/服务器的计算中的典型方案,携带可执行代码的移动代理被调度处理中心到传感器节点和本地处理数据。由于移动代理商的复杂行为,在建模和模拟中没有做出太多的工作。在本文中,广泛的随机培养型(GSPN)用于将移动代理模拟DSN中。 GSPN是一个非常受欢迎的系统,用于构成并发,同步和随机性的系统。 GSPN建模中最具挑战性问题之一是设计一种破坏转换冲突的机制。本文提出了一种基于关节熵的随机转换选择器和所谓的“滚动岩石随机(R3)选择器”,以在立即转换之间破坏冲突。使用可重新配置组件,基于该转换选择器的GSPN模型在Xilinx Virtex 1000E现场可编程门阵列(FPGA)上合成。仿真结果表明,所提出的过渡选择器比常用的随机选择器更好地执行。

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