Adaptive Active And Sleep Control Solution Based On The Variation Of Energy Supply For Sensor Network

In the era of the Internet of things, wireless sensor network will play a more and more important role. However wireless sensor network always use battery power as supply in many applications, so how to improve energy efficiency and solve energy supplement have become the key problem in its development. In recent years, researchers have proposed many solutions for wireless sensor network to obtain renewable energy as supply from environment, such as solar energy, wind energy, geothermal energy, tidal energy and so on. Solar energy is the most common energy resource. Solar panels can collect energy and convert it into electrical energy for wireless sensor nodes to work.Renewable energy resource has the following characteristics: on the one hand, renewable energy is inexhaustible, if appropriately used, it can ensure the system work forever; on the other hand, there is more uncertainty in renewable energy supply because the variation of natural environment has a great impact on the available energy. Therefore it needs to figure out the corresponding solutions for the system which use renewable energy. In order to keep it work continually and effectively, the system should adjust according to the circumstances.This paper presents an adaptive active and sleep control solution based on the variation of energy supply for sensor network in MAC layer. The solution is that the duty cycle can be changed according to the variation of the external energy supply in order to ensure the system keep stable and high energy utilization efficiency in variable environments. Because there are so many kinds of renewable energy resource, we focus on solar energy. The basic idea and solution can also be applied to systems which use other renewable energy resource after modifications. This paper uses OPNET to simulate, results show that this algorithm can improve the energy utilization efficiency and is better than the existing algorithms.