Modeling on low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of things based on π-net

Advantages and disadvantages of low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of Things（IoT） directly affect the endurance and network life of nodes. In view of problem of high energy consumption in hardware and software partitioning of intelligent sensor nodes of IoT, a low power consumption hardware and software partitioning model based on π-net was proposed. Firstly, the intelligent sensor nodes of IoT was defined with constraints, and the constrained model of the intelligent sensor nodes was obtained. Then, the hardware and software partitioning model of intelligent sensing nodes based on π-net was established by using the π-net theory, and the low power consumption hardware and software partitioning based on IP core power consumption of hardware and software and the overall power consumption constraints of the system were realized, and the model was analyzed for evolution. The analysis and simulation results show that the model has certain advantages and practicability in terms of fitness, execution time division and minimum system partition energy consumption compared with models based on tabu search algorithm and genetic algorithm, which can reduce the energy consumption of intelligent sensing nodes of IoT and improve their endurance.