Research On Optimization Algorithm Of Node Task Scheduling In Wireless Sensor Networks

In wireless sensor networks,the execution of tasks must be accompanied by the use of resources.The execution of a task must consume certain resources such as communication resources,computing resources,and energy,but the resources in wireless sensor networks are limited.Therefore,when scheduling tasks,it is necessary to make efficient use of limited resources,that is,in a constantly changing external environment and resource-constrained internal environment,perform the most appropriate task at a specific moment to ensure that the resources in the wireless sensor network are effectively used.The research work of this article mainly includes the following two aspects:(1)Aiming at the problem that traditional distributed independent reinforcement learning task scheduling algorithm fails to make full use of environmental information and has poor application performance,this thesis proposes a wireless sensor network node task scheduling algorithm based on improved Q learning.The algorithm combines the actual application scenarios and introduces the data collected by nodes to improve the calculation method of the reward value of the collection task.At the same time,it uses the scheduling times of the task and its Q value to balance the exploration and exploitation,and uses the time step as a clue to control the decline of the exploration rate.The simulation results show that the algorithm has achieved a good balance between exploration and exploitation,and can effectively reduce the energy consumption of nodes.(2)Aiming at the problem that the exploration and exploitation strategy ε-greedy method of traditional multi-step Q learning uses a constant value ε to explore,which is too dependent on the initialization setting of the ε value,this thesis improves its action selection strategy ε-greedy and optimizes the calculation method of the reward value,and thereby proposing a task scheduling algorithm for wireless sensor network nodes based on adaptive multi-step Q learning.The algorithm uses the number of task scheduling as a clue to improve the calculation of the exploration probabilityε so that the value of ε can be changed adaptively.The simulation results show that the algorithm can adjust the task scheduling strategy according to environmental changes,which can save more energy than other traditional scheduling algorithms.The innovations proposed from these two aspects have effectively reduced the energy consumption of wireless sensor network nodes,prolonged the survival time of nodes,and thus increased the life of the entire network.